PENINGKATAN ALGORITMA C4.5 MENGGUNAKAN ENSEMBLE LEARNING UNTUK MENDETEKSI PENYAKIT GINJAL

Agusviyanda Agusviyanda, Rita Novita, Alfa saleh, Muhammad Jamaris

Abstract


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.


Keywords


Penyakit Ginjal, Machine Learning, C4.5, Adaboost, SMOTE-ENN, LASSO

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References


Arif, M. S., Mukheimer, A. & Asif, D. (2023). Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model. Big Data and Cognitive Computing, 7(3). https://doi.org/10.3390/bdcc7030144

Cahya, S. D., Sartono, B., Indahwati, I. & Purnaningrum, E. (2022). Performance of LAD-LASSO and WLAD-LASSO on High Dimensional Regression in Handling Data Containing Outliers. JTAM (Jurnal Teori Dan Aplikasi Matematika), 6(4), 844. https://doi.org/10.31764/jtam.v6i4.8968

Cleto-Yamane, T. L., Gomes, C. L. R., Koch-Nogueira, P. C. & Suassuna, J. H. R. (2024). Acute kidney injury requiring dialysis in children: a multicentric, emerging country perspective. Pediatric Nephrology, 39(7), 2253–2262. https://doi.org/10.1007/s00467-024-06305-9

Damayanti, H. L. & Herawati, R. (2024). Enhancing Stroke Disease Prediction Performance Through a Fusion of Adaboost With C4.5 and K-Nearest Neighbor Algorithms. Proxies, 7(2), 102–114. https://doi.org/10.24167/proxies.v7i2.12470

Ghazal, T. M., Ibrahim, A., Akram, A. S., Qaisar, Z. H., Munir, S. & Islam, S. (2023). Heart Disease Prediction Using Machine Learning. 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023. https://doi.org/10.1109/ICBATS57792.2023.10111368

Herianto, Kurniawan, B., Hartomi, Z. H., Irawan, Y. & Anam, M. K. (2024). Machine Learning Algorithm Optimization using Stacking Technique for Graduation Prediction. Journal of Applied Data Sciences, 5(3), 1272–1285. https://doi.org/10.47738/jads.v5i3.316

Husain, G., Nasef, D., Jose, R., Mayer, J., Bekbolatova, M., Devine, T. & Toma, M. (2025). SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models. Algorithms, 18(1), 1–16. https://doi.org/10.3390/a18010037

Ibarra, R., León, J., Ávila, I. & Ponce, H. (2022). Cardiovascular Disease Detection Using Machine Learning. Computacion y Sistemas, 26(4), 1661–1668. https://doi.org/10.13053/CyS-26-4-4422

Ingkafi, D. A., Aryana, G. A., Putra, A. K. & Kusumaningrum, R. (2023). Sentiment Analysis of The National Covid-19 Vaccination Program on Twitter Using The Bidirectional Encoder Representation From Transformer. ICIC Express Letters, 17(2), 201–208. https://doi.org/10.24507/icicel.17.02.201

Islam, M. A., Majumder, M. Z. H. & Hussein, M. A. (2023). Chronic kidney disease prediction based on machine learning algorithms. Journal of Pathology Informatics, 14(1), 1–12. https://doi.org/10.1016/j.jpi.2023.100189

Kumar, K., Pradeepa, M., Mahdal, M., Verma, S., RajaRao, M. V. L. N. & Ramesh, J. V. N. (2023). A Deep Learning Approach for Kidney Disease Recognition and Prediction through Image Processing. Applied Sciences (Switzerland), 13(6). https://doi.org/10.3390/app13063621

Li, X., Chen, X. & Yuan, Z. (2021). Applicable model of liver disease detection based on the improved CART-AdaBoost algorithm. 2021 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2021, 1177–1181. https://doi.org/10.1109/ICAICA52286.2021.9498046

Liu, J., Ma, Y., Xie, W., Li, X., Wang, Y., Xu, Z., Bai, Y., Yin, P. & Wu, Q. (2023). Lasso-Based Machine Learning Algorithm for Predicting Postoperative Lung Complications in Elderly: A Single-Center Retrospective Study from China. Clinical Interventions in Aging, 18, 597–606. https://doi.org/10.2147/CIA.S406735

Mubarak, M. M. R., Chrisnanto, Y. H. & Sabrina, P. N. (2023). Implementation of Random Forest Using Smote and Smoteenn in Customer Churn Classification in E-Commerce. Enrichment: Journal of Multidisciplinary Research and Development, 1(8), 463–477. https://doi.org/10.55324/enrichment.v1i8.69

Mustafizur Rahman, M., Al-Amin, M. & Hossain, J. (2024). Machine learning models for chronic kidney disease diagnosis and prediction. Biomedical Signal Processing and Control, 87, 1–17. https://doi.org/10.1016/j.bspc.2023.105368

Piao, C., Wang, N. & Yuan, C. (2023). Rebalance Weights AdaBoost‐SVM Model for Imbalanced Data. Computational Intelligence and Neuroscience, 2023(1), 1–26. https://doi.org/10.1155/2023/4860536

Pradyto, K. D. A. & Raharja, M. A. (2023). Implementasi Random Forest dengan LASSO Dalam Klasifikasi Penyakit yang Ditularkan Melalui Nyamuk. JNATIA (Jurnal Nasional Teknologi Informasi Dan Aplikasinya), 1(4), 1197–1202. https://www.kaggle.com/datasets/richardbernat/vector-borne-disease-

Pratama, I. G. A. M., Astuti, L. G., Widiartha, I. M., Putra, I. G. N. A. C., Pramartha, C. R. A. & Darmawan, I. D. M. B. A. (2022). Diagnosis Penyakit Ginjal Kronis dengan Algoritma C4.5, K-Means dan BPSO. Jurnal-Elektronik-Ilmu-Komputer-Udayana, 10(4), 371–381. https://doi.org/10.24843/JLK.2022.v10.i04.p07

Singh, B., Arora, K. & Iyer, S. S. (2022). Chronic Kidney Disease Detection Using Machine Learning Regression Models. ECS Transactions, 107(1), 2191–2207. https://doi.org/10.1149/10701.2191ecst

Sivaraman, K. & Khanna, V. (2021). Machine learning models for prediction of cardiovascular diseases. Journal of Physics: Conference Series, 2040(1). https://doi.org/10.1088/1742-6596/2040/1/012051

Sui, Q. & Ghosh, S. K. (2024). Active Learning for Stacking and AdaBoost-Related Models. Stats, 7(1), 110–137. https://doi.org/10.3390/stats7010008

Van FC, L. L., Anam, M. K., Bukhori, S., Mahamad, A. K., Saon, S. & Nyoto, R. L. V. (2025). The Development of Stacking Techniques in Machine Learning for Breast Cancer Detection. Journal of Applied Data Sciences, 6(1), 71–85. https://doi.org/10.47738/jads.v6i1.416

Venkatesan, V. K., Ramakrishna, M. T., Izonin, I., Tkachenko, R. & Havryliuk, M. (2023). Efficient Data Preprocessing with Ensemble Machine Learning Technique for the Early Detection of Chronic Kidney Disease. Applied Sciences (Switzerland), 13(5), 1–18. https://doi.org/10.3390/app13052885

Wang, A. X., Chukova, S. S. & Nguyen, B. P. (2023). Synthetic minority oversampling using edited displacement-based k-nearest neighbors. Applied Soft Computing, 148, 1–12. https://doi.org/10.1016/j.asoc.2023.110895

Yustanti, W., Iriawan, N. & Irhamah. (2023). Categorical encoder based performance comparison in preprocessing imbalanced multiclass classification. Indonesian Journal of Electrical Engineering and Computer Science, 31(3), 1705–1715. https://doi.org/10.11591/ijeecs.v31.i3.pp1705-1715




DOI: https://doi.org/10.36987/informatika.v12i3.7542

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INFORMATIKA
Journal URL: https://jurnal.ulb.ac.id/index.php/informatika
Journal DOI: 10.36987/informatika
P-ISSN: 2303-2863
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Fakultas Sains dan Teknologi, Universitas Labuhanbatu
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