Implementation of Soft Voting Ensemble Learning for Early Detection of Type 2 Diabetes Mellitus

Authors

  • Carles Sekolah Tinggi Ilmu Kesehatan Tengku Maharatu
  • Osi Draini STIKES Tengku Maharatu
  • Sabtria Winda Sari
  • Nu'man STIKES Tengku Maharatu
  • M. Khairul Anam Universitas Samudra

DOI:

10.33395/sinkron.v10i4.16740

Keywords:

Diabetes, Machine Learning, GUI, Ensemble Learning, Soft Voting

Abstract

Type 2 Diabetes Mellitus is a chronic disease that requires early detection to minimize the risk of complications. This study aims to develop a machine learning model for the early detection of Type 2 Diabetes Mellitus using an ensemble Soft Voting approach. The proposed model combines three algorithms, namely LightGBM, Random Forest, and Support Vector Machine. The dataset used consists of patient health attributes, including pregnancies, glucose level, blood pressure, skin thickness, insulin level, body mass index, diabetes pedigree function, and age. The preprocessing stage was conducted to prepare the data before the training process, including data balancing so that each class contained the same number of records, namely 500 diabetes data and 500 non-diabetes data. The evaluation results show that the model achieved an accuracy of 80%, with precision, recall, and f1-score of 0.80 for both classes. In addition, the Area Under the Curve value of 0.89 indicates that the model has a very good capability in distinguishing between diabetes and non-diabetes classes. The system was also implemented in the form of a Graphical User Interface to facilitate patient clinical data input and display risk detection results. Based on these results, the developed Soft Voting model can be used as a practical and sufficiently accurate tool for the early detection of Type 2 Diabetes Mellitus.

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References

Anam, M. K., Firdaus, M. B., Suandi, F., Lathifah, Nasution, T. & Fadly, S. (2024). Performance Improvement of Machine Learning Algorithm Using Ensemble Method on Text Mining. ICFTSS 2024 - International Conference on Future Technologies for Smart Society, 90–95. https://doi.org/10.1109/ICFTSS61109.2024.10691363

Anam, M. K., Lestari, T. P., Efrizoni, L., Handayani, N. S. & Andhika, I. (2025). Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 9(4), 905–914. https://doi.org/10.29207/resti.v9i4.6708

Atiyah, O. S. (2024). Using Soft Voting Classifier of Machine Learning Algorithms for Earthquake Prediction. Al-Kitab Journal for Pure Sciences, 9(01), 1–13. https://doi.org/10.32441/kjps.09.01.p1

Enríquez-Ortega, D., Chulde-Fernández, B., Pozo-Coral, P., Vaca, A., Zhinin-Vera, L., Almeida-Galárraga, D., Ramírez-Cando, L., Tirado-Espín, A., Cadena-Morejón, C., Villalba-Meneses, F., Guevara, C. & Acosta-Vargas, P. (2025). Enhancing Diabetes Diagnosis Through Machine Learning: A Comparative Study. Applied Sciences (Switzerland), 15(1), 1–18. https://doi.org/10.3390/app151810087

Erlin, Marlim, Y. N., Junadhi, Suryati, L. & Agustina, N. (2022). Deteksi Dini Penyakit Diabetes Menggunakan Machine Learning dengan Algoritma Logistic Regression. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi , 11(2), 88–96. https://doi.org/10.22146/jnteti.v11i2.3586

Ginting, J. B., Ginting, R. & Hartono. (2022). Deteksi Dan Prediksi Penyakit Diabetes Melitus Tipe 2 Menggunakan Machine Learning (Scooping Review). Jurnal Keperawatan Priority, 5(2), 93–105. https://doi.org/10.34012/jukep.v5i2.2671

Hamdani, Randi N.A & Anam, M. K. (2024). Comparison of Support Vector Machine and Random Forest Algorithms for Analyzing Online Loans on Twitter social media. JAIA-Journal Of Artificial Intelligence And Applications, 4(1), 8–16. https://doi.org/10.33372/jaia.v4i1.1087

Ikhwan Saputra, E., Khairul Anam, M., Yenni, H., Hamdani & Zamsuri, A. (2025). Optimalisasi Algoritma Support Vector Machine pada Aspect-Based Sentiment Analysis Menggunakan GridSearchCV. Zonasi (Jurnal Sistem Informasi), 7(1), 271–279. https://doi.org/10.31849/zn.v7i1.17800

Iparraguirre-Villanueva, O., Espinola-Linares, K., Castañeda, R. O. F. & Cabanillas-Carbonell, M. (2023). Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes. Diagnostics, 13(14), 1–16. https://doi.org/10.3390/diagnostics13142383

Jabbar, H. G. (2024). Advanced Threat Detection Using Soft and Hard Voting Techniques in Ensemble Learning. Journal of Robotics and Control (JRC), 5(4), 1104–1116. https://doi.org/10.18196/jrc.v5i4.22005

Karlos, S., Kostopoulos, G. & Kotsiantis, S. (2020). A soft-voting ensemble based co-training scheme using static selection for binary classification problems. Algorithms, 13(1), 1–19. https://doi.org/10.3390/a13010026

Kull, K., Khan, M. A., Asad, B., Naseer, M. U., Kallaste, A. & Vaimann, T. (2025). Adaptive Deep Belief Networks and LightGBM-Based Hybrid Fault Diagnostics for SCADA-Managed PV Systems: A Real-World Case Study. Electronics (Switzerland), 14(18), 1–29. https://doi.org/10.3390/electronics14183649

Nugroho H, Y. D., Zakiyabarsi, F. & Paramita, A. J. (2025). Implementasi SMOTE-ENN dan Borderline SMOTE Terhadap Performa LightGBM Pada Imbalanced Class. Rabit : Jurnal Teknologi Dan Sistem Informasi Univrab, 10(1), 51–59. https://doi.org/10.36341/rabit.v10i1.5436

Pratama, N. A. & Utomo, D. W. (2026). Deteksi Diabetes Mellitus dengan Menggunakan Teknik Ensemble XGBoost dan LightGBM. JISKA (Jurnal Informatika Sunan Kalijaga), 11(1), 1–12. https://doi.org/10.14421/jiska.4908

Rahayu, K., Anam, M. K., Efrizoni, L., Nurjayadi & Fitri, T. A. (2024). Optimasi Teknik Voting Pada Sentimen Analisis Pemilihan Presiden 2024 Menggunakan Machine Learning. The Indonesian Journal of Computer Science, 13(4), 6289–6302. https://doi.org/10.33022/ijcs.v13i4.4119

Rai, N., Kaushik, N., Kumar, D., Raj, C. & Ali, A. (2022). Mortality prediction of COVID-19 patients using soft voting classifier. International Journal of Cognitive Computing in Engineering, 3, 172–179. https://doi.org/10.1016/j.ijcce.2022.09.001

Suandi, F., Anam, M. K., Firdaus, M. B., Fadli, S., Lathifah, L., Yumami, E., Saleh, A. & Hasibuan, A. Z. (2024). Enhancing Sentiment Analysis Performance Using SMOTE and Majority Voting in Machine Learning Algorithms. International Conference on Applied Engineering, 126–138. https://doi.org/10.2991/978-94-6463-620-8_10

Susanto, E. R., Inzaghi, M. R., Amarudin, A. & Neneng, N. (2025). Evaluasi Kinerja Model Random Forest Dalam Memprediksi Diabetes Berdasarkan Dataset Kesehatan di Indonesia. Jurnal Pendidikan Dan Teknologi Indonesia, 5(7), 1857–1866. https://doi.org/10.52436/1.jpti.871

Trisna, N. & Ayu Mahessya, R. (2025). Prediksi Risiko Diabetes Tahap Awal Menggunakan Machine Learning dengan Algoritma K-Nearest Neighbor. Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, Dan Arsitektur Komputer), 5(2), 481–487. https://doi.org/10.55382/jurnalpustakadata.v5i2.1550

Van Fc, L. L., Anam, M. K., Firdaus, M. B., Yunefri, Y. & Rahmi, N. A. (2024). Enhancing Machine Learning Model Performance in Addressing Class Imbalance. COGITO Smart Journal, 10(1), 478–490. https://doi.org/10.31154/cogito.v10i1.626.478-490

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How to Cite

Carles, Draini, O., Sari, S. W. ., Nu’man, & Anam, M. K. (2026). Implementation of Soft Voting Ensemble Learning for Early Detection of Type 2 Diabetes Mellitus. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(4), 2459-2473. https://doi.org/10.33395/sinkron.v10i4.16740

Issue

Vol. 10 No. 4 (2026): Research Articles October 2026

Section

Articles

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Copyright (c) 2026 Carles, Osi Draini, Sabtria Winda Sari, Nu'man, M. Khairul Anam

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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