INFORMATIKA : Jurnal Ilmiah Fakultas Sains & Teknologi Universitas Labuhanbatu diterbitkan oleh Universitas Labuhanbatu melalui Lembaga Penelitian dan Pengabdian Masyarakat, dimaksudkan sebagai media pertukaran informasi dan karya ilmiah antara staf pengajar, alumni, mahasiswa dan masyarakat pada umumnya yang terbit tiga kali dalam setahun Januari-Mei-September), yang mulai awal terbit sejak tahun 2013. Redaksi menerima naskah yang belum pernah diterbitkan oleh media lain dan berhak menyingkat dan mengubah tanpa mengurangi maksud dan isi. Pendapat yang dinyatakan dalam jurnal ini merupakan pendapat pribadi tidak mencerminkan pendapat penerbit dan dewan redaksi.Jurnal ini telah diakreditasi oleh SINTA 5 Mulai Volume 8, Nomor 2, Juli 2020. No SK :Surat Keputusan Direktur Jenderal Pendidikan Tinggi,Riset, dan Teknologi Nomor 79/E/KPT/2023, tanggal 11 Mei 2023. E-ISSN:2615-1855 (Online), P-ISSN:2303-2863(Print)
Fakultas Sains & Teknologi, Universitas Labuhanbatu
Analisis Perbandingan Algoritma C4.5 Dan Naive Bayes Dalam Menilai Kelayakan Bantuan Program Keluarga Harapan
Social assistance is a form of government intervention that aims to help people who are in less fortunate economic conditions. This form of assistance can be in the form of cash assistance, food assistance, or health service assistance. Social assistance programs are often aimed at reducing poverty, addressing hunger, and improving the overall well-being of society. Program Keluarga Harapan (PKH) is a form of conditional social assistance launched by the government of Indonesia to help poor and vulnerable families. The Program aims to improve the quality of life of poor families through the provision of cash assistance accompanied by obligations for recipients to meet certain requirements, such as ensuring their children attend school and regular health checks at health facilities. With the PKH, it is expected to improve the access of poor families to education and health services, which in turn will improve the quality of Indonesian human resources. Thus, the author can evaluate the advantages and disadvantages of each method in the context of the data used. In addition, this comparative analysis also aims to provide more informative recommendations for policy makers. If one of the methods proves to be superior, then it can be adopted to improve the selection process for CCT recipients in the future. However, if both methods have balanced performance, a combination or integration of the two can be the optimal solution. By comparing the performance of Naive Bayes and the C4.5 algorithm, the study not only focused on identifying the right recipients, but also provided valuable insights in choosing the most effective analytical tool for the purpose.
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Application of Apriori and Fp-Growth Methods in Analyzing Book Lending Patterns Penerapan Metode Apriori dan Fp-Growth dalam Analisis Pola Peminjaman Buku
Clustering of book borrowing patterns in the University of Labuhanbatu library aims to identify and understand student preferences and habits in borrowing books. With this analysis, the library can be more effective in managing book collections, ensuring the availability of frequently borrowed books, and improving the quality of service according to student needs. Using clustering techniques also helps in designing a more targeted book procurement strategy, so that existing resources can be optimally utilized to support the teaching and learning process. In this study, the methods used are Kf-Growth and Apriori to identify book borrowing patterns. Kf-Growth is used to find frequent itemsets or collections of books that are often borrowed together, while Apriori is used to generate association rules that reveal the relationships between borrowed books. Both of these methods allow for a more in-depth and comprehensive analysis of book borrowing patterns in the library, with the ability to handle large amounts of data and identify significant relationships between items. This process involves several stages, including data preprocessing, algorithm application, and evaluation of the results to ensure the validity and accuracy of the resulting clustering. The results of the clustering analysis show a very good confidence value, with many male and female students borrowing the book "Pengantar Akuntansi" consistently. This borrowing pattern shows that books related to economics and accounting have a high level of demand. The Kf-Growth and Apriori methods have proven to be very effective in clustering, providing accurate and reliable results. With these results, the Labuhanbatu University library can take more informative and strategic steps in managing book collections, ensuring that frequently borrowed books are always available, and improving the borrowing experience for students.
Analisis Minat Masyarakat Menggunakan Media Sosial Menggunakan Algoritma C4.5 dan Metode Naïve Bayes
The analysis of public interest using social media in data mining aims to understand user preferences and interests in various topics or products. By analyzing data from social media platforms, such as posts, comments, and interactions, researchers can identify significant interest patterns and trends, which can be used for more effective marketing strategies or product development that suits the public's desires. Common methods used in this analysis are the C4.5 and Naive Bayes algorithms. The C4.5 algorithm builds a decision tree that makes it easy to visualize and interpret the main factors that influence public interest. Meanwhile, Naive Bayes, with its probabilistic approach, classifies data based on existing features, providing fast and accurate predictions. Both methods are applied to process data from social media and produce in-depth insights into user preferences. The results of the analysis show that the prediction and classification of public interest have good accuracy, with the comparison result values showing very satisfactory performance. Both are able to identify and classify interests accurately, utilizing the advantages of each method to provide a better understanding of what is interesting to the public on social media.
Analisis Sistem Informasi Pengelolaan Data Alumni MAN Labuhanbatu Berbasis Codeigniter PHP Framework
Alumni data management information system is a platform designed to manage and maintain alumni data effectively and efficiently. This system makes it easy for educational institutions to collect, store, and manage information about their graduates, such as personal data, education history, career, and their contribution to the alumni community. Using database technology and specialized software, the system enables fast data retrieval, organized data storage, and real-time monitoring of alumni activities. The existence of this information system also helps in maintaining good relations between institutions and alumni, as well as supporting alumni programs such as reunion events, networking, and career development. In addition, the alumni data Management Information System serves as a strategic tool in improving the quality and reputation of educational institutions. Research on alumni data management information system analysis using the CodeIgniter PHP framework as a programming language is interesting to do. CodeIgniter PHP Framework is known as one of the lightweight and efficient frameworks in web application development, so it can provide advantages in managing complex alumni data.
Analisis Pola Pembelian Melalui Ponsel Menggunakan Algoritma Apriori dan Fp–Growth Pada Millenium Ponsel
The purpose of this research is to understand the main factors that influence consumer decisions in purchasing the device. By exploring information about consumer preferences, needs, and behavior, this study seeks to identify purchasing trends and understand how aspects such as mobile phone features, price, and brand influence consumer choices. The main objective of this study is to provide in- depth insights to technology industry players so that they can develop more effective and relevant marketing and product strategies to meet dynamic market needs. To achieve this goal, this study uses the Apriori and FP-Growth methods, which are data mining algorithms that are effective in finding associations and patterns in transaction data. The Apriori method focuses on identifying the frequency of occurrence of itemsets and forming association rules based on support and confidence values, while FP- Growth uses a tree approach to store and extract frequently occurring patterns more efficiently. Both methods allow for in-depth analysis of mobile phone purchase data, so that complex patterns can be revealed more accurately and quickly. The results of this study indicate that there is a very clear mobile phone purchasing pattern among consumers, with confidence values reaching 90% for some association rules. For example, consumers who purchase phones with AMOLED displays tend to also choose large battery capacities from certain brands. These patterns indicate strong and consistent preferences across consumer groups, providing manufacturers with opportunities to target specific market segments with tailored product offerings. These findings not only provide valuable insights into consumer behavior but also help companies optimize their marketing strategies and increase their competitiveness in the technology industry.
Implementasi Data Mining Untuk Klustering Stunting Gizi Pada Balita Dipuskesmas Sigambal Meggunakan Metode K-Medoids Dan K-Means
The aim of this study was to identify and understand the different characteristics of toddlers in the context of factors that contribute to nutritional stunting. By using the clustering method, this study aims to group toddlers into several groups based on the similarity of their characteristics, so that more targeted interventions can be designed in dealing with stunting problems. Through this approach, it is hoped that significant patterns and risk factors can be found that distinguish stunted toddlers from toddlers who grow normally, and provide insights that can be used by policy makers and health practitioners to improve the quality of life of children. The method used in this study involves the application of two clustering techniques, namely K-Means and K-Medoids to Group sample data of 116 toddlers. The clustering process is carried out by measuring the distance between the toddler data and the centroid or medoid to determine which group is most suitable. The Data were analyzed to find patterns identifying unique characteristics of each cluster, reflecting differences in nutritional stunting-related risk factors.This process helps in differentiating groups of toddlers who are prone to stunting from those who are not, so that the analysis can be focused on the groups most in need of intervention. The results of clustering analysis showed that as many as 48 toddlers entered the C1 cluster, while the other 68 toddlers entered the C2 cluster. Each cluster describes two groups of toddlers with different characteristics in the context of nutritional stunting risk factors. The findings provide deep insight into the significant differences between the two groups, allowing researchers to identify specific patterns and risk factors. This information is then used to design more specific and effective interventions in addressing nutritional stunting in toddlers, taking into account the unique characteristics of each cluster that has been identified.
Simulasi Kinerja Karyawan di Kantor Pertanahan Labuhanbatu Menggunakan Algoritma C4.5
Employee performance analysis using the C4.5 algorithm in data mining aims to identify and classify employees based on their performance. The analysis process includes several stages, namely data analysis, preprocessing, model design in data mining, and method evaluation. From 47 sample data analyzed, the results show that 40 employees have good characters, while 7 employees have bad characters. Good employee characters are characterized by punctuality and high discipline in carrying out their duties. Conversely, bad employee characters are characterized by unpunctuality and low discipline, which have a negative impact on productivity and efficiency in the workplace. The results of this classification help identify areas that require more attention and intervention to improve overall employee performance. Model evaluation is carried out using two widgets, namely Test and Score and Confusion Matrix. The evaluation results of these two widgets show perfect accuracy of 100%. Meanwhile, the Confusion Matrix widget shows that all predictions are in accordance with the actual data without any errors in classification. These results confirm that the C4.5 algorithm is very effective and accurate in classifying employee performance. The perfection of the evaluation results shows that the C4.5 algorithm is very suitable for use as a classification model in employee performance analysis. The 100% accuracy of both widgets indicates that this algorithm is not only able to predict correctly but also consistently in various evaluation tools.
Penerapan Data Mining Untuk Evaluasi Data Penjualan Menggunakan Metode Clustering Dan Agoritma Hirarki Divisive Studi Kasus Toko Sembako Pujo
The larger a company, the longer the company stands, the more companies have branches, of course, the greater the data owned. These data can be consumer data, purchase data, sales data, payroll data, and many other data. All data will usually be stored in a database. But many companies, even the Information Technology (IT) division, do not realize how valuable the pile of old data generated by the company in transactions and activities. Data mining is the study of methods for generating knowledge or finding patterns for processing data. So it's not just information, it's knowledge. Data Mining has several methods including clustering. Clustering is a well-known and widely used method in data mining. The main purpose of this clustering method is to Group a number of data/objects into clusters (groups) so that the cluster will contain the same data as each group. In this study, Divisive hierarchy algorithm is used to form clusters. From the pattern obtained is expected to provide knowledge for the company Media World Pekanbaru as a supporting tool to take policy.
Implementasi Metode Naive Bayes dan Neural Network Untuk Menentukan Minat Masyarakat Pada Handphone Samsung
Naive Bayes and Neural Network methods are used in analyzing people's interest in Samsung mobile phones to gain a better understanding of consumer preferences. Naive Bayes is a simple but very effective probability-based classification method. This method generates possible consumer interests by analyzing features such as price, specifications, and brands, and calculating the probability of different categories. Naive Bayes is very useful in situations where data has independent features, and can provide accurate results at high speed. By identifying patterns of people's preferences, this method can help Samsung adjust marketing and product strategies that are more in line with consumer needs. On the other hand, Neural Network offers more complex analytical capabilities by imitating the way the human brain works through a network of neurons. This method is used to process larger and more complex data in understanding consumer interest patterns in Samsung mobile phones. Neural Network can identify deeper relationships between various factors, such as the interaction between camera features and user needs, using deep learning processes. The purpose of using Neural Network is to capture nuances and trends that cannot be identified with simple methods, thereby providing a more comprehensive view of what drives consumer interest. The use of these two methods of analysis in public interest in Samsung mobile phones has provided very satisfactory results. The calculation values obtained from both methods show a high level of accuracy in the classification of consumer interest. The results of this analysis provide valuable insights for Samsung in understanding consumer preferences and needs, as well as helping the company in designing more effective products and marketing strategies. Thus, the combination of the use of Naive Bayes and Neural Networks not only provides stron g results, but also provides a more holistic approach to consumer data analysis.
Prediksi Biaya Pemakaian Listrik Dengan Metode Logika Fuzzy Mamdani
Understanding the electricity tariff class is needed to predict how much usage costs or electricity bills that must be incurred by customers. Each power capacity has a different basic electricity tariff based on the class, household, business and industry. Fuzzy logic can solve problems with imprecise and heuristic data by producing accurate conclusions. The conclusion obtained from the Fuzzy Logic results is expected to be a consideration for customers in predicting electricity usage costs based on electricity tariff groups and customer electrical power capacity. The proposed research uses the Mamdani Fuzzy Logic Method, the use of this method aims to facilitate customers in predicting the cost of electricity usage based on the electricity tariff class and the customer's electric power capacity. The output of this research is the result of predicting the cost of electricity usage based on the power group in residential homes. The higher the tariff group, the number of kWh, electronic goods, and usage time, the higher the predicted energy consumption used. The results obtained from adjusting the fuzzy input antecedents and fuzzy values, namely [900;850;30;20], then for Type_Tariff Power 900, Total_KWh 850, Electronics 30, and Time_Use 20 is 986. Then the cost that must be incurred in the example case above is 1,333,072 rupiah.
Rancang Bangun Purwarupa Monitoring Arus Bocor Pada Kabel Grounding Trafo Incoming 20 KV di Gardu Induk Nusa Dua Berbasis Internet Of Things
Failure of the grounding isolation on the 20 kV transformer incoming cable, resulting in leakage current. The leakage current on the grounding cable can change periodically, so an accurate and real-time monitoring system is required to protect the power transformer equipment and facilitate responsive handling. Therefore, an Internet of Things-based monitoring device is needed that can detect the magnitude of the leakage current present on the 20 kV secondary side of the transformer using an ESP8266 microcontroller and Arduino UNO R3 as the brain of the monitoring system, which controls and processes data from the input to output components. The SCT-013 current sensor is used to measure the AC current on the transformer incoming 20 kV grounding cable without requiring cable cutting, and the Arduino IDE is used to configure the program on the ESP8266 microcontroller to work according to the desired configuration. The results of the prototype testing using the ESP826 and Arduino UNO R3 microcontrollers and the SCT-013 current sensor have shown that the system can work well and the monitoring has been successfully implemented with real-time current monitoring using the Thinkspeak and Blynk platforms. The testing also proved that the SCT-013 current monitoring device can provide a comparison of the test results and measurements with a Tang Ampere, and the data obtained shows that the real-time SCT-013 current monitoring device is accurate, with an average reading error of less than 3% from the SCT-013 non-linearity specification, with a total reading error percentage of 2.0%. Additionally, the current monitoring device is precise, with the lowest standard deviation value of 0.046.
Utilizing FP-Tree and FP-Growth Algorithms for Data Mining on Medicine Sales Transactions at Khanina’s
Although Khanina Pharmacy is a growing pharmacy with a lot of processes, the data processing is still done by hand. This study examines the use of the FP-Tree and FP-Growth algorithms to the medication sales transaction system. The FP-Tree and FP-Growth algorithm methods use methods or strategies to choose data in order to identify trends or intriguing details. The FP-Tree and FP-Growth algorithm approaches are two frequently used techniques in data mining. The purpose of this medicine sales transaction data is to identify concurrently purchased products. The FP-Growth Algorithm is used to find item pattern combinations. Use of FP-Tree to identify frequently occurring itemsets from a database in combination with the FP-Growth algorithm. When searching for product attachment patterns for sales tactics in decision-making rules, the Association Rule method is employed. In order to determine which medications are frequently bought by customers, we can create rules using the data in the database. The Rapidminer 5 program was used to conduct the test. This test yielded the following results: the number of itemsets created and rules constructed increased with decreasing support values.
Sistem Pengambilan Keputusan Untuk Pengolahan Data Siswa Penerima Bantuan Belajar Komputer Gratis
Students play an important role in learning computers, especially in the process of education and learning. According to Law No. 14 of 2005 article 51 paragraph 1 item b states that students are entitled to receive awards for their performance during computer Learning . On the other hand, information technology continues to develop rapidly, one of the developments in information technology is the emergence of Decision Support Systems. Decision support system is a system that helps decision makers in making a decision.There are many methods or algorithms that can be used in decision support systems, including Weighted Product (WP), TOPSIS, Simple Addictive Weighting (SAW), Analytical Hierarchy Process (AHP) methods and others. Analytical Hierarchy Process (AHP) method is one method that has been widely used in Decision Support Systems. This method has the ability to measure the degree of consistency of the decisions to be made. One of the consistency calculations performed in the AHP method is the calculation of the consistency ratio, which in this calculation is done using the value of the random index (IR). Over time, many researchers have conducted research on the value of the Analytical Hierarchy Process random index, so there are many new random index values in addition to the AHP random index found by students.
Implementasi Deep Learning Untuk Menentukan Harga Buah Sawit
This study aims to analyze the price of palm oil using Convolutional Neural Network (CNN) method in deep learning. CNN was chosen for its ability to process complex data and recognize patterns from diverse data. The stages of research include data analysis, data pre-processing, predictive model design for CNN method, CNN classification model prediction results, CNN method evaluation, and CNN method evaluation results. This study aims to produce a model that can predict the price of oil palm with high accuracy, based on data covering a variety of characteristics of farmers and the quality of oil palm crops. Prediction results were conducted using data from 50 oil palm farmers. From the prediction, as many as 23 data farmers get a price of IDR 2,300, 13 other farmers get a price of IDR 2,000, and the remaining 14 data farmers get a price of IDR 1,800. The results of this prediction are based on data from farmers and the quality of oil palm crops they grow and produce. By utilizing the CNN method, the model can capture various factors that affect the price of palm oil, including the quality of palm fruit and agricultural conditions. Evaluation of the CNN method showed very good results, with almost perfect accuracy. This method managed to predict palm oil prices very precisely, showing that CNN can be an effective tool in the analysis of palm oil prices. The results of this evaluation confirmed that the CNN method can be relied upon to provide accurate predictions, helping farmers and palm oil industry players in determining prices that are in accordance with the quality and condition of the crop.
Perancangan Pembelajaran Media Animasi (Studi Kasus SD Negeri 22 Rantau Utara) Dengan Menggunakan Adobe Flash
Dalam era digital seperti sekarang adalah zaman yang maju. Dalam kemajuan tersebut didukung oleh teknologi yang berkembang pesat. Komputer dapat digunakan sebagai sarana pembelajaran yang menarik dan interaktif, sehingga dapat meningkatkan minat dan antusias peserta didik. Media pembelajaran berbasis komputer dapat menyajikan materi pembelajaran secara visual dan audio yang lebih menarik dan mudah dipahami. Selain itu, media pembelajaran berbasis komputer juga dapat memberikan umpan balik secara langsung kepada peserta didik, sehingga dapat membantu siswa untuk memahami materi pembelajaran dengan lebih baik , tempat penelitian SDN 22 Rantau Utara merupakan salah satu sekolah Dasar Negeri di daerah Pulo Padang yang beralamat di Pasir Tinggi, kecamatan rantau utara kabupaten labuhanbatu sumatra utara. SDN 22 Rantau Utara. Penelitian ini menggunakan Adobe Flash sebagai aplikasi perancangan animasi pengenalan huruf dan angka.
Perancangan UI/UX Aplikasi Booking Online Pada Elaine Studio Dengan Metode Design Thinking
Nail art is currently experiencing a significant increase in popularity, driven by beauty trends and growing consumer preferences. The nail art salon industry is undergoing growth and transformation driven by new trends and technological advancements. This makes it an exciting time for salon owners looking for solutions by adopting technology to help businesses attract more customers and also increase customer satisfaction. The process of booking appointments and providing information on Elaine Studio is still done manually, namely booking through social media such as Instagram, Whatsapp or directly face to face to come to the studio and is also still recorded manually to book an appointment. This study aims to design and develop an effective user interface (UI) and User Experience (UX) for the nail art online booking mobile application using the Design Thinking method. The main focus of the study is to create an intuitive and user-friendly design, which is able to increase ease of Use and user satisfaction. After going through a series of design and testing iterations, the application is tested using the system Usability Scale (SUS) method to evaluate its usability level.
Application Of Data Mining In Selecting Superior Products Using The K-Means And K-Medoids Algorithm Methods
As a supermarket, we are committed to always improving everything, including selecting the greatest goods. To evaluate which items are more superior or popular and which are less popular, you will want a sizable amount of information sources. To select products and identify those that belong in the superior product cluster, researchers employed the clustering method. The clustering strategy uses two forms of cluster analysis, k-means and k-medoids, which have related techniques. The research results show that the k-means algorithm's Davies Bouldin value is -0.430, whereas the k-medoids algorithm's Davies Bouldin value is -1.392. This suggests that the Davies Bouldin value of the k-medoids approach is the lowest, showing that the grouping findings of the k-means method are a better method to apply to the issue of choosing better products.
Implementasi Metode Backpropagation Neural Network Untuk Memprediksi Saham Bank Terbesar di Indonesia
Prediksi harga saham yang akurat sangat penting bagi investor untuk membuat keputusan investasi yang bijak. Penelitian ini bertujuan untuk memprediksi harga saham harga close tiga bank besar di Indonesia menggunakan algoritma backpropagation neural network. Metode penelitian meliputi pengumpulan data historis harian dari Yahoo Finance, preprocessing data, dan pembuatan model neural network dengan satu hidden layer. Evaluasi model menggunakan metrik RMSE, MAE, dan MAPE. Hasil penelitian menunjukkan bahwa model prediksi memiliki tingkat akurasi yang tinggi untuk ketiga bank. Bank Mandiri mencapai performa terbaik dengan 3 neuron hidden layer, learning rate 0.01, dan toleransi error 0.000001, menghasilkan RMSE 49.565, MAE 38.3087, dan MAPE 0.63%. Bank BRI optimal dengan 12 neuron hidden layer (RMSE 50.003, MAE 29.5462, MAPE 0.74%), sementara Bank BCA dengan 6 neuron hidden layer (RMSE 62.434, MAE 47.7587, MAPE 0.51%). Kesimpulannya, algoritma backpropagation neural network terbukti efektif dalam memprediksi harga saham penutup dengan tingkat akurasi tinggi, ditunjukkan oleh nilai MAPE di bawah 1% untuk ketiga bank.
Perancangan Sistem Informasi Perpustakaan Berbasis Website di SMP Negeri 1 Anjatan
SMP Negeri 1 Anjatan is a junior high school located in Anjatan Indramayu Regency. To make it easier for Library Officers to manage libraries, a library information system is needed. the Information System created includes: attendance visits, management of borrowing and returning books, and the process of managing book collections.An information system is a collection of elements interconnected with each other that form a single whole for integrating data, processing and storing and distributing information. Library Information System is a system that exists in an organization that meets the needs of daily data processing, supporting activities in data storage, and providing certain outside parties with the necessary reports. The method used in the development of this library information system using SDLC with RAD model. Stages in the RAD method are requirement planning, user design, constructions, and cutover. Making this information system using PHP Programming Language, HTML, CSS. Using Visual Studio Code as note editor, in the implementation of data base using local server from MySQL.Questionnaires that will be used for prototype testers at the user design stage and the results of contructions are SUS (System Usability Scale) and blackbox testing. Where SUS to measure the Usability of Information Systems and use Black box testing to measure functional suitability.The results of SUS prototype testing included in the good category and acceptability ranges included in the acceptable category. The results of black box testing, all questions on the questionnaire were successful, which means the score of the black box testing is 100%. Based on testing, the Library Information System can be said to be feasible to use.
Determinasi Pertanian, Industri dan Perdagangan Terhadap Pengurangan Tingkat Kemiskinan di Nusa Tengara Barat Tahun 2018-2022
The most serious problem faced by a country in realizing economic development is poverty. This study was conducted to see the effect of agriculture, industry, and trade on poverty reduction. The method used is the static panel regression method to analyze the relationship and influence of the independent variable on the dependent variable. The results showed that there was a negative effect of the agricultural sector on the poverty rate and there was a positive effect of the industrial sector on poverty, but the trade sector had no effect on poverty. Then simultaneously the agricultural, industrial, and trade sectors affect the poverty rate.
Analisis Pengaruh Ekspor dan Impor Terhadap Pertumbuhan Ekonomi di Indonesia Periode 2019-2023
In a country, economic sector growth is an increase in per capita output. When a country's economic growth increases, its capacity to meet the needs of its citizens also increases, thereby improving their overall welfare. Economic growth in Indonesia occurs from many factors, one of which is exports and imports. This study aims to analyze how the value of Indonesia's exports and imports in the 2019-2023 period affects its economic growth. This research applies secondary data in the form of time series covering the time span. The data is obtained through data statistics and previous journals. The application of the method utilized by researchers in this study is the OLS (Ordinary Least Square) method. The tests applied are partial data testing (T-test) and simultaneous data testing (F-test). The regression equation in this study is Y = 4.264 - 5.579EX1 + 9.323EX2. Exports have such an impact on Indonesia's economic growth that is fully supported by the results of the t-test. Similarly, imports also show a significant influence on economic growth at the country level. The reason why economic growth is not affected is because of the influence of other variables.
Implementasi Data Mining Menggunakan Metode Algoritma FP-Growth Dan Algoritma Apriori Pada Toko IBR Jaya Untuk Meningkatkan Penjualan
Dian trading business is one of the grocery stores engaged in buying and selling the main household needs of nine basic ingredients which have been doing a lot of grocery sales transactions. This transaction Data continues to grow every day and in the IBR Jaya store sales transaction data is only presented as an archive or report and it is not mentioned what the benefits of these data are. Nah, the problem at the IBR Jaya store is the improvement of improvements due to the shortage of basic food stocks that are often purchased by consumers are not available which results in improvements and usability improvements then the FP-Growth algorithm is used to analyze patterns of improvement and a priori algorithms for comparison through archived transaction data goods that will be purchased later as a reference to increase food stocks so as to increase sales at the IBR Jaya Food Store in the hope that this increase can help this is one of many ways to make money online. Association rules are a process in Data Mining to establish all associative policies that meet the minimum requirements for support (minsup) and trust (minconf) in a database . In association rules, there are 2 methods that can be used, namely a priori method and FP-Growth method. In this study the method used is FP-Growth algorithm and a priori algorithm, FP-Growth algorithm and a priori method is a method to find the most frequently appearing data set (frequent itemset) without using candidate generation that is suitable to analyze a data transaction.
Analisis Kinerja Sistem Informasi SMK Swasta Pemda
This research aims to analyze the performance of the information system used in local government private vocational schools, which plays an important role in supporting the smooth operation of schools, including the teaching and learning process, administration and financial management. The research method used is benchmarking, which involves collecting data through interviews, observations and questionnaires. The research results show that the existing information system still has several weaknesses, such as lack of integration between systems, inaccurate data reporting, and low efficiency in data processing. Based on these findings, this research provides recommendations for improving the information system in local government private vocational schools to make it more effective and efficient in supporting school activities.
Analisis Faktor Yang Mempengaruhi Kepuasan Pegawai Dinas Pangan: Pendekatan Menggunakan Algoritma C4.5
The level of satisfaction is an important measure in evaluating the extent to which the needs and expectations of a person or group are met by a product, service, or experience. The concept is often used in a business context to measure how well a product or service meets customer expectations. The level of satisfaction can be measured through various methods such as surveys, interviews or analysis of consumer behavior data. The results of this satisfaction level evaluation provide valuable insights for companies in improving the quality of their products or services, as well as maintaining customer loyalty. Therefore, the author will conduct a study on the level of employee satisfaction Department of food using machine learning approach with C4.5 method. This study aims to explore the patterns and factors that significantly affect the level of employee satisfaction in the context of the Department of food. The C4.5 method was chosen because of its ability to handle complex and diverse data, as well as being able to provide insight into the relationship of complex and non-linear variables.
Sistem Informasi Jual Beli Kelapa Sawit Berbasis Web pada Peron Reskianto
In Surau Gading village, the majority of the population is employed as oil palm farmers. Every two weeks, farmers harvest oil palm fruit. After harvesting the oil palm fruit, the farmers sell it to Peron Resdianto, who is a young entrepreneur in the village of Surau Gading. Almost every day, the platform owner purchases agricultural products made from palm oil. Because a large number of customers sell their agricultural products, palm oil platform owners have to make sales to the factory once every two days. Once the palm oil mill receives the sales proceeds, the platform owner disburses payments to the palm oil farmers who sell their agricultural products. As information technology advances, it has now spread to almost all fields and is developing at a rapid pace. Information technology's application is evolving, as evidenced by the constant advancements in the field. The author wants to design a web-based information system for selling and buying palm oil on the Resdianto platform using the PHP and My SQL programming languages. We designed this system using system modeling tools, personal home page (PHP), MYSQL, HTML, data flow diagrams (DFD), Xampp, flowcharts, and entity relationship diagrams (ERD). Peron Reskianto can improve performance by creating a web-based information system for buying and selling palm oil, which will assist platform owners in collecting data on palm oil purchases and sales. This information system guarantees security and reduces errors.
Analisis Prediksi Prestasi Siswa UPTD SD Negeri 30 Aek Batu Dalam Machine Learning Dengan Metode Naive Bayes
Education is one of the efforts made to determine the success of a nation, successful education will continue to produce a good generation as well. Along with the rapid global challenges, the challenges of the world of education are becoming greater, this aspect that encourages learners to achieve the best achievements. Given the presence of teachers in the process of teaching and learning activities is very influential, it should be the quality of teachers must be considered. The problem that often occurs in every school, especially in UPTD SD Negeri 30 AEK Batu, is that there are many students who are lazy to learn, students who lack fun lessons, do not have attention to what has been learned, school assignments are a burden, learning outcomes are only to go to class or graduate from school and school just to meet friends and get pocket money. Therefore, to predict the achievements of different students, the education of UPTD SD Negeri 30 AEK Batu requires accurate data on student achievement so that it can be a reference for education to better know the achievements of students who excel and underachieve. Application of student achievement prediction UPTD SD Negeri 30 AEK Batu in machine learning with naive bayes method can be solved well or not.
Penerapan Metode KNN untuk Menentukan Minat Calon Mahasiswa
This study focuses on the implementation of data mining to determine the interests of prospective male and female students in the Informatics Management Department using the K-Nearest Neighbors (KNN) method. The analysis process is carried out through the Knowledge Discovery in Databases (KDD) stages, which include data selection, pre-processing, transformation, data mining, and pattern evaluation. The KDD stage ensures that the data used has been prepared and processed properly to produce an accurate and relevant model. The KNN method is used to classify sample data consisting of 82 prospective male and female students. The results of this study indicate that 63 out of 82 prospective students are interested in the Informatics Management Department, while 19 other prospective students are not interested. This classification process shows that the KNN method is able to identify the interests of prospective students with a high level of accuracy, providing useful information for universities in understanding the preferences of their prospective students. Evaluation of the research results using two evaluation tools, namely Test and Score and Confusion Matrix, showed perfect results with an accuracy of 100%. Both of these evaluation tools are consistent in assessing the performance of the KNN model, confirming that this model works very well in classifying prospective student interests. In conclusion, the KNN method is proven to be effective and reliable in determining prospective students' interest in the Informatics Management Department, providing a strong foundation for similar applications in the future.
Grouping Student Achievement Data In A Decision Making System Using The Weight Product Method
Information, modeling, and data manipulation systems are called decision support systems (DSS). When there is uncertainty about the best course of action in semi-structured or unstructured situations, the system is utilized to support decision-making. There are various approaches available for producing decision support systems, one of which is the Weighted Product (WP) Method. With the Weighted Product (WP) approach, attribute ratings are connected by multiplication; however, each attribute's rating must first be increased to the power of the attribute's weight. The normalizing process is same to this one. SPK procedure to choose the winners of the scholarships. Scholarship information from MTS Swasta Alwasliyah Simpang Merbau can be saved in the Decision Support System using this method. This way, in the event that an error arises when entering grades or scholarship information, the wrong information can be fixed without requiring the scholarship information to be re-input. Scholarships are presents to individuals in the form of financial aid intended to be utilized toward their ongoing educational pursuits.
Sistem Informasi Kepegawaian Pada Kantor Kecamatan Rambah Samo Berbasis Framework Laravel Fakultas Ilmu Komputer Universitas Pasir Pengaraian
Promotion is an award given for work performance and dedication. Promotions must be given in a timely manner and to the right person. Promotions are given to civil servants at a higher level if concerned. At least 4 years late in the last level. The existing system at the Rambah Samo sub-district office, even though they are already using a computer, but for the process of completeness of files which often results in problems that arise related to promotion. that rank. problems that occur such as the number of employees who do not understand what files are proposed even though each period of the BKPP Kab. Rokan Hulu always sends letters regarding what files are prepared, many proposals are rejected because the period has not been calculated (not enough months). The programming language used in designing and implementing a personnel information system at the Rambah Samo District Office is PHP and the database used is MySQL. The results of research at the Rambah Samo District office, it can be concluded that this system is expected to assist the Rambah Samo District Office in managing promotion data and personnel data.
Analisis Data Penjualan Menggunakan Algoritma Apriori pada Analisis Kopi
Data Mining is a technique for finding, searching, or extracting new information or knowledge from a very large set of data, by integration or merging with other disciplines such as statistics, artificial intelligence, and machine learning, making Data Mining as one of the tools to analyze data and then produce useful information. Association Rule is a process in Data Mining to determine all associative rules that meet the minimum requirements for support (minsup) and confidence (minconf) in a database. In Association Rule, there are 2 methods that can be used, namely a priori method and FP-Growth method, where FP-Growth method is the development of a priori method where a priori method there are still some shortcomings such as there are many patterns of data combinations that often appear (many frequent patterns), many types of items but low minimum support fulfillment, it takes quite a long time because database scanning is done repeatedly to get the ideal frequent pattern. In this study the method used is a priori algorithm method, a priori algorithm method is one of the alternative ways to find the most frequently appearing data sets (frequent itemset) without using candidate generation that is suitable for analyzing a transaction data. Coffee analysis is a Cafe Shop engaged in the sale of food and beverages that many food and beverage sales transactions. Open on November 7, 2021 coffee analysis penetrates 245 sales transactions and this transaction data continues to grow every day.
Sistem Pendukung Keputusan Penghuni Asrama Mahasiswa Kalimantan Tengah di Yogyakarta Menggunakan Metode Simple Additive Weighting (SAW)
The Central Kalimantan Student Dormitory, located at Jalan Pakuningratan No.61 Yogyakarta, is one of the provincial dormitories established and funded by the regional government to serve as housing for students from outside the region who stay for a certain period. It is specifically intended for students currently studying, with a capacity of 24 rooms. Due to the limited number of rooms and the growing number of students from Central Kalimantan, which exceeds 24 individuals, the dormitory faces challenges in managing its residents. To assist dormitory managers in selecting suitable candidates for residency, the author conducted research on a web-based decision support system designed to streamline the selection process for Central Kalimantan students seeking accommodation at the dormitory in Yogyakarta. This system evaluates students based on criteria that reflect their eligibility to occupy a dormitory. By utilizing web-based decision support technology and employing the Simple Additive Weighting (SAW) method, the system processes various criteria, including data on prospective residents, academic readiness, adaptability, need for social support, and commitment to dormitory rules. With an accuracy rate of 93.3%, as determined by comparing original data, this system ensures a faster, more accurate, transparent, and efficient selection process based on objective data.
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Integrasi Model Pembelajaran Mesin dalam Game Menggunakan Gerakan Tangan
This study develops a Tetris game controlled through hand gestures using a machine learning model. The primary objective of this research is to create an interactive and responsive gaming experience by utilizing hand gesture detection as the main control mechanism. A hand gesture dataset was collected from videos segmented into individual frames, which were then analyzed using MediaPipe to detect and label gestures. The machine learning model employs a Convolutional Neural Network (CNN) trained to recognize hand gesture patterns and translate them into commands within the game. After implementation, an evaluation was conducted by distributing questionnaires to 18 Informatics students at Adzkia University to assess the system's comfort and responsiveness. The questionnaire results showed a high satisfaction level, with an average score of 84.56, covering evaluations of control ease, gesture detection accuracy, and system responsiveness. The average score for ease of use reached 85, indicating that the majority of users found the gesture-based controls comfortable. This study demonstrates that applying machine learning models in gesture-based control games can provide a more interactive and responsive experience, with potential applications in other interactive technologies.
Sistem Informasi Manajemen Magang dengan Metode System Integration di Biro Kesejahteraan Rakyat Setdaprovsu
Efficient internship recruitment management is one of the important elements in supporting human resource management, but the implementation of services at the Bureau of People's Welfare of the North Sumatra Provincial Secretariat is still constrained by manual processes, lack of system integration, and low efficiency. This study aims to improve the quality of internship recruitment management through the application of the process optimization method using the Waterfall Model approach. This method involves the stages of needs analysis, system design, implementation, testing with the black-box method, and maintenance of PHP and MySQL-based systems. The results of the study showed that the information system developed succeeded in increasing work efficiency, reducing errors in data recording, accelerating the selection process, and clarifying access to information for users, both administrators and interns. This system also proved to be responsive to user needs with appropriate feature validation. The conclusion of this study is that the process optimization method is able to significantly improve the performance of internship recruitment services. The researcher suggests that employee training be carried out continuously to ensure mastery of the system, as well as improving technology infrastructure so that the system can adapt to future needs and support the sustainability of services.
Rancang Bangun Sistem Informasi Stock Barang Berbasis Website Pada PT. XYZ
The development of information technology has facilitated the creation of applications to enhance productivity across various sectors. However, the utilization of this technology has not been fully optimized, including in the TEKPOL Division of PT. Perkebunan Nusantara III. Data management in the TEKPOL Division is still performed manually using Microsoft Excel, which makes it difficult for administrators to manage data in a structured and efficient manner. This study aims to design and develop an information system for managing goods data to assist administrators in the TEKPOL Division in organizing, storing, and accessing data more easily and systematically. The system was developed using PHP as the programming language, Bootstrap 5 as the framework for the interface, and MySQL as the database for data processing. The result of this development is an information system with a user-friendly interface that enhances efficiency and accuracy in managing goods data in the TEKPOL Division. Therefore, this system is expected to provide an effective solution in supporting administrative activities at PT. Perkebunan Nusantara III.
Analisis Pengembangan Sistem Informasi Manajemen Fiber To The Home Menggunakan Metode Pieces Pada PT. XYZ
PT. XYZ is a company engaged in the field of digital information technology and telecommunications services in Indonesia, utilizing Fiber To The Home (FTTH) technology as a telecommunications solution to connect service providers with residential customers. PT.XYZ faces challenges in managing FTTH data due to the manual recording of information in books, which can lead to inaccuracies, hinder data accessibility, and complicate the understanding of network transmission point distribution. A viable approach to address this issue is the implementation of a management information system capable of managing FTTH data and visualizing the distribution of transmission points based on current data. This project aims to create a web-based FTTH management information system to enhance the efficiency of fiber optic network data management at PT.XYZ. This research employs the PIECES technique (Performance, Information, Economy, Control, Efficiency, Service) for requirements analysis and system assessment. The system is built using the Waterfall methodology, which includes phases of requirements analysis, design, implementation, and testing. The results of black-box testing indicate that all scenarios effectively meet the specified requirements. Usability testing using the PIECES framework shows that the average user satisfaction falls within the "Satisfied" category across all criteria. This system offers a more effective and organized FTTH data management solution, capable of visualizing the distribution of network transmission points, thereby assisting operators in their daily operations.
Determinan Green Human Resouce Management terhadap Komitmen Karyawan dengan Motivasi Kerja Sebagai Variabel Moderasi pada Dinas Perhubungan Sumatera Barat
This study aims to analyze the effect of Green Human Resource Management (GHRM) on employee commitment, with work motivation as a moderating variable at the West Sumatra Department of Transportation. This research employs a quantitative method with an explanatory research approach, collecting data through a survey of 95 employees and analyzing it using SEM-PLS. The results indicate that GHRM has a positive and significant effect on employee commitment. Work motivation also significantly influences commitment but fails to moderate the relationship between GHRM and employee commitment. These findings confirm that effective GHRM implementation can enhance employee loyalty and engagement without being influenced by individual motivation levels. This study provides implications for organizations in developing environmentally-oriented human resource policies to improve employee performance and retention.
Rancang Bangun Aplikasi Reminder Service Untuk Kenaikan Pangkat Dan Informasi Pensiun ASN Menggunakan Whatsapp Api Berbasis Website
This research aims to design and develop a web-based reminder service application that informs Civil Servants (ASN) about promotions and retirement information through the WhatsApp API. Currently, notifications regarding promotions and retirement information for ASN are still manually conveyed through WhatsApp messages without standardization. This research aims to provide more structured information about promotions and retirement, from scheduling to standardized messages. The research focuses on the need for an efficient system to manage and deliver important information related to ASN promotions and retirement, given the frequent delays and inaccuracies in information dissemination. The system development method used is the Waterfall model, which includes the stages of requirements analysis, design, implementation, testing, and maintenance. This application is built using the PHP programming language and the Yii Framework, and it integrates the WhatsApp API for personal notifications. Testing results show that this application functions well, providing timely notifications and making it easier for ASN to receive important information related to promotions and retirement through WhatsApp. The web-based reminder service application with WhatsApp API integration is effective in enhancing the efficiency of delivering promotion and retirement information to ASN and is expected to be widely implemented in personnel administration systems.
Efektivitas Aplikasi Ujian Online Untuk Peserta Didik SMK
This study aims to develop an effective online exam application for students of SMK Negeri 2 Padang in the Software Modeling subject of class XI Software Engineering. This study uses the Research and Development (R&D) method, with a four-D development model. The four-D development procedure is Define, Design, Development and Disseminate. The data analysis technique used is to describe the effectiveness of the online exam application for daily tests. Based on the results of this study, it can be concluded that an effective application with the values obtained from teacher responses is very effective. Based on the findings of this study, it is concluded that the Online Exam Application is declared effective for use as an alternative to daily tests in the Software Modeling subject in class XI Software Engineering.
Efektivitas dan Kelemahan Autentikasi Berbasis Web Menggunakan One-Time Password (OTP) dalam Mencegah Akses Tidak Sah
Authentication based on one-Time Password (OTP) is one method that is widely used in securing access to web systems. This study aims to analyze in depth the effectiveness and weaknesses of the OTP authentication system in preventing unauthorized access. Through a qualitative approach based on literature studies, as well as comparisons between other authentication methods, it was found that OTP is able to increase a significant additional layer of security, especially when combined with other authentication methods such as passwords or biometrics. However, this system still has various weaknesses, such as the risk of phishing attacks, man-in-the-middle (MITM) attacks, and vulnerabilities to SIM swapping attacks, especially in the implementation of OTP via SMS. Dependence on user devices and communication networks is also a limiting factor in the effectiveness of OTP. This study provides recommendations for the implementation of strengthening measures such as Multi-Factor Authentication (MFA), the use of authenticator applications, and the implementation of end-to-end encryption to reduce security risks. The results of this study are expected to be a reference for system developers and organizations in choosing and implementing authentication methods that are more secure and in accordance with current cybersecurity needs.
PENINGKATAN ALGORITMA C4.5 MENGGUNAKAN ENSEMBLE LEARNING UNTUK MENDETEKSI PENYAKIT GINJAL
Deteksi dini penyakit ginjal sangat penting untuk menurunkan risiko komplikasi dan meningkatkan prognosis pasien. Permasalahan utama dalam diagnosis penyakit ginjal adalah adanya gejala yang tidak spesifik dan ketidakseimbangan distribusi data pasien. Penelitian ini mengusulkan peningkatan performa algoritma C4.5 untuk deteksi penyakit ginjal dengan mengintegrasikan beberapa tahapan modern, yaitu pra-pemrosesan menggunakan Label Encoder dan Ordinal Encoder untuk mengolah fitur kategorikal, penyeimbangan data menggunakan metode SMOTE-ENN, serta seleksi fitur dengan LASSO. Selanjutnya, model dasar C4.5 ditingkatkan dengan metode ensemble learning menggunakan AdaBoost. Hasil pengujian menunjukkan bahwa integrasi Adaboost pada algoritma C4.5 secara signifikan meningkatkan akurasi deteksi penyakit ginjal dibandingkan model dasar maupun model-model pada penelitian terdahulu. Model terbaik pada penelitian ini mencapai akurasi 99%, melebihi performa XGBoost maupun stacking ensemble pada kasus serupa. Kontribusi penelitian ini menegaskan efektivitas kombinasi boosting, balancing, dan seleksi fitur dalam membangun sistem pendukung keputusan berbasis machine learning untuk diagnosis penyakit ginjal.
Analisis Jadwal Retensi Arsip Inaktif di Kementerian Pertanian Republik Indonesia
This study aims to analyze the process of preparing, implementing, and addressing the obstacles in implementing the Inactive Archives Retention Schedule (JRA) at the Ministry of Agriculture, Republic of Indonesia. Inactive archives are documents that are rarely used, but they must still be managed according to regulations to prevent accumulation and ensure easy retrieval when needed. This research design used a qualitative approach with a case study design, allowing researchers to understand the problem in depth in a real-world context. Data were collected through observation, interviews, documentation, and questionnaires distributed to archives management staff. The results indicate that the JRA preparation has not been optimally implemented across all work units due to low employee understanding, minimal training, and limited resources. Furthermore, the JRA implementation is also less than optimal because many archives have not been adjusted to the applicable retention period. Therefore, strengthening regulations, regular training, and the formation of a dedicated archives team are necessary to ensure more effective and efficient archives management.
Analisis Kepatuhan Karyawan PT Cipta Megaswara Televisi (Kompas TV) Divisi Traffic
Employee compliance is a form of obedience from a person's attitude in complying with certain regulations or provisions related to work. This study aims to determine and analyze employee compliance at PT Cipta Megaswara Televisi (Kompas TV) Traffic Division. This study uses a qualitative research method, a type of research where researchers rely on the views of participants or informants. Researchers took four samples related to the research topic. These four samples provided researchers with various information or important aspects that improve or enrich understanding of the perspective being studied. Data collection techniques included observation, interviews, and documentation. The results of this study indicate that employee compliance in the traffic division has improved significantly due to the regulations enforced by the company and employees' ability to demonstrate a responsible attitude and professionalism when implementing compliance in the workplace. Factors influencing employee compliance can be improved through motivation, leadership, and effective communication, thus employee compliance can be significantly improved and to encourage greater compliance. Strategies to improve employee compliance require companies to develop more in-depth strategies such as weekly training and more effective supervision. Therefore, researchers hope that the results of this study can provide suggestions and insights regarding employee compliance in any company.
Penerapan Algoritma K-Nearest Neighbor Dalam Menentukan Kualitas Buah Jeruk Bali
Grapefruit, large orange or pomelo (Citrus maxima) could be a native Indonesian fruit plant that has high economic value. Grapefruit is spread across various regions in Indonesia and has various types of fruit such as Bali Merah, Cikoneng, Nambangan, Raja, Ratu, and Pangkep. Grapefruit (Citrus maxima) is one of three types of local citrus besides Citrus medica and Citrus reticulate. Grapefruit grows in tropical areas so that grapefruit has the potential to be cultivated in Indonesia. The quality of grapefruit is usually obtained through selection by distinguishing contrast based on the characteristics of natural materials, calculating the dosage and shape of natural materials, the shape of the tip and base of natural materials, the color and surface of grapefruit fruit. flavedo (epicarp), viscosity and color of albedo (mesocarp), color of endocarp, color and taste of vesicles or natural product tissue, odor of essential oils, number of natural products per tree and number of seeds in each natural product. Test results using the grapefruit application in data mining processing using KNN calculations. In the data testing, the precision value was obtained:0.834, Classification precision:0.860, F1:0.860, accuracy:0.861, review:0.860 and MCC:0.720 on the test data.
Implementasi Data Mining Untuk Menentukan Kelayakan Penerimaan Bedah Rumah Menggunakan Algoritma K-Means Fakultas Ilmu Komputer Universitas Pasir Pengaraian
The house renovation program is a government program, namely the Public Housing and Settlement Area Office, which is shown to rehabilitate or repair community houses that are not habitabel. This program is often a problem in Bangun Purba Village because when determining house renovation assistance, it is still by looking or relying on thoughts and predictions for people who will get assistance, so that the assistance that will be distributed is not on target. This house renovation program aims to provide welfare for the poor to build or renovate houses that are not habitabel. Therefore, the author created a Data Mining system to determine the feasibility of receiving home renovation using the K-Means algorithm using the PHP programming language and MySQL database. The K-Means method utilizes training data to generate probabilities such as each grouping for different classes, so that the probability values of the grouping can be optimized to determine the eligibility for receiving home surgery assistance based on the grouping process carried out by the K-Means method.
Pengembangan Evidence Monitoring Tingkat PH dan Kekeruhan Air Untuk Menentukan Kelayakan Konsumsi Air
Development of Evidence Monitoring of PH Levels And Water Turbidity to Determine the Feasibility of Water Consumption. (Supervised by Muhammad Basri and Untung Suwardoyo). The most common method for detecting water quality parameters is by manually collecting samples and sending them to a laboratory for detection and analysis. However, with the advancement of time and technology, this method has become somewhat less relevant due to its manual nature. For that reason, a study was conducted titled "Development of Evidence-Based Monitoring for pH and Turbidity Levels to Determine Drinking Water Feasibility." This study aims to develop a monitoring process that also simplifies the assessment of water feasibility through an automated system, making the process more efficient and relevant. This monitoring system uses two indicators: turbidity and pH. The system is developed using a pH sensor SEN0161-V2 and a turbidity sensor DF Robot-SEN0189, with an ESP32 microcontroller.(Permenkes RI, 2010) It is designed to monitor changes in values in real time, which are then displayed through an Android application. The device is also equipped with a filtration system. Test results showed a change in turbidity levels from an initial value of 29.8 NTU (not suitable for consumption) to 4.8 NTU (suitable for consumption), within a water filtration cycle duration of 24 minutes and 56 seconds.
Aplikasi Penilaian Kinerja Dosen Teknik Informatika
This research highlights the strategic role of lecturers in higher education, particularly in the teaching and assessment process. However, lecturer performance appraisals often contain elements of subjectivity that cause data inaccuracies. To overcome this, a technology-based system is needed that is able to support the decision-making process more objectively. The Simple Additive Weighting (SAW) method is used in this research because it is able to provide an assessment based on predetermined weights for each criterion. With this approach, the research aims to design a lecturer performance appraisal system that can improve the quality of education through more accurate and structured measurements.
Pengembangan Sistem Absensi Berbasis Real-Time Face Recognation Di Lingkungan Sekolah Menggunakan You Only Look Once (Yolo)
The increasing demand for efficient and reliable student attendance systems highlights the limitations of conventional manual methods, which are often time-consuming, prone to fraud, and lack accuracy. This study develops a real-time attendance system based on facial recognition using the YOLOv8 deep learning model integrated with TensorFlow. The system is designed to automatically detect and recognize students’ faces through a webcam and record attendance data in real time. The research method involved three main stages: face registration, model training, and attendance recording. A dataset of 4,200 face images from 14 students was collected to train the model. The recognition process used cosine similarity with a threshold of 0.7 to balance accuracy and avoid false recognition. The results showed that the system could effectively recognize student faces under varying lighting and expression conditions with high accuracy, while also recording attendance automatically and reliably. The system proved to be efficient in reducing time consumption, minimizing fraud, and ensuring more accurate attendance records. This study demonstrates that YOLOv8-based face recognition can provide an effective solution for modernizing school attendance management.
Perancangan Sistem Informasi Promosi dan Penjualan Berbasis Web Pada Unit Produksi SMKN 1 Ampek Angkek Menggunakan Framework CodeIgniter
The Production Unit (PU) plays an important role in fostering students’ entrepreneurial skills through the creation of economically valuable products and services. However, product promotion at the Production Unit of SMK N 1 Ampek Angkek has so far only been carried out through exhibitions held once every semester. The school’s less strategic location results in many products remaining unsold, and those stored for too long often become damaged. To address this issue, a web-based promotional and sales information system was designed using the CodeIgniter framework. The system was developed using UML (Unified Modeling Language) modeling with an object-oriented approach. The programming languages used were PHP with the CodeIgniter framework and JavaScript, while MySQL served as the database and Sublime Text 3 as the code editor. The result of this design is a web-based information system that can present craft product information more widely and efficiently. This system helps the Production Unit promote its products without time and location limitations and provides convenience for consumers to view and purchase products online.