Comparison of Decision Tree and Random Forest Algorithms for Diabetes Classification

Betharya Ester Lina Tampubolon; Alde William Limbong; Wulan Liviana Simbolon; Saudurma Sidabutar; Febi Sitohang

doi:10.59934/jaiea.v6i1.2685

Authors

Betharya Ester Lina Tampubolon Department of Computer Science, Faculty of Mathematics and Natural Sciences, HKBP Nommensen University, Pematangsiantar, Indonesia
Alde William Limbong
Wulan Liviana Simbolon
Saudurma Sidabutar
Febi Sitohang

DOI:

https://doi.org/10.59934/jaiea.v6i1.2685

Keywords:

Diabetes, Machine Learning, Decision Tree, Random Forest, Classification

Abstract

Diabetes merupakan salah satu penyakit kronis yang paling umum di seluruh dunia, dengan jumlah penderita yang terus meningkat setiap tahunnya. Perkembangan pesat Machine Learning telah menyediakan pendekatan efektif untuk meningkatkan akurasi klasifikasi diabetes. Studi ini bertujuan untuk membandingkan kinerja algoritma Decision Tree dan Random Forest dalam mengklasifikasikan diabetes menggunakan Dataset Diabetes Suku Indian Pima. Dataset ini terdiri dari 768 catatan pasien dengan delapan atribut prediktor dan satu atribut target. Metodologi penelitian meliputi pra-pemrosesan data, pembagian dataset menjadi set pelatihan dan pengujian, pengembangan model, dan evaluasi kinerja menggunakan matriks kebingungan, akurasi, presisi, recall, dan F1-score. Hasil eksperimen diharapkan dapat mengidentifikasi algoritma dengan kinerja klasifikasi terbaik dan memberikan referensi berharga untuk menerapkan teknik Machine Learning di sektor kesehatan.

Downloads

Download data is not yet available.

References

Alnuaimi, A. F. A. H., & Albaldawi, T. H. K. (2024). An overview of machine learning classification techniques. BIO Web of Conferences, 97. https://doi.org/10.1051/bioconf/20249700133

Chang, V., Bailey, J., Xu, Q. A., & Sun, Z. (2023). Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms. Neural Computing and Applications, 35(22), 16157–16173. https://doi.org/10.1007/s00521-022-07049-z

Helmud, E., Helmud, E., Fitriyani, F., & Romadiana, P. (2024). Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 13(1), 92–97. https://doi.org/10.32736/sisfokom.v13i1.1985

Krisnanda, C. M., Dwi Usmanto, K., & Kristanto, J. J. (2026). EVALUATION OF THE C4.5 DECISION TREE AND RANDOM FOREST CLASSIFICATION ALGORITHMS IN PREDICTING DIABETES. In Jurnal (Vol. 19, Number 1).

Mienye, I. D., & Jere, N. (2024). A Survey of Decision Trees: Concepts, Algorithms, and Applications. IEEE Access, 12, 86716–86727. https://doi.org/10.1109/ACCESS.2024.3416838

Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random Forest Algorithm Overview. In Babylonian Journal of Machine Learning (Vol. 2024, pp. 69–79). Mesopotamian Academic Press. https://doi.org/10.58496/BJML/2024/007

Shamal Salih, M., Khalil Ibrahim, R., M Zeebaree, S. R., Asaad Zebari, D., Abdulrahman, L. M., Mahdi Abdulkareem, N., & Author, C. (2024). Diabetic Prediction based on Machine Learning Using PIMA Indian Dataset. In Communications on Applied Nonlinear Analysis (Vol. 31, Number 5s). https://internationalpubls.com

Sujon, K. M., Hassan, R., Choi, K., & Samad, M. A. (2025). Accuracy, precision, recall, f1-score, or MCC? empirical evidence from advanced statistics, ML, and XAI for evaluating business predictive models. Journal of Big Data, 12(1). https://doi.org/10.1186/s40537-025-01313-4

Wang, S. (2024). Diabetes Prediction Using Random Forest in Healthcare. In Highlights in Science, Engineering and Technology SDPIT (Vol. 2024).

Werner de Vargas, V., Schneider Aranda, J. A., dos Santos Costa, R., da Silva Pereira, P. R., & Victória Barbosa, J. L. (2023). Imbalanced data preprocessing techniques for machine learning: a systematic mapping study. Knowledge and Information Systems, 65(1), 31–57. https://doi.org/10.1007/s10115-022-01772-8