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All Journal Mattawang: Jurnal Pengabdian Masyarakat Journal of Economic Education and Entrepreneurship Studies Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Awalia, Andi Dio Nurul
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : Journal of Embedded Systems, Security and Intelligent Systems

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Analysis of Naive Bayes and Support Vector Machine Algorithms in Classification of Diabetes Cases Based on Lifestyle Factors Awalia, Andi Dio Nurul; Muhammad Fadhil Hani; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9783

Abstract

The increase in diabetes mellitus cases globally, including in Indonesia, demands a more adaptive lifestyle-based risk prediction strategy. This study aims to evaluate and compare the efficiency of Support Vector Machine (SVM) and Naive Bayes in the diabetes risk classification process using a Hybrid clustering-classification approach . The data analyzed in this study were obtained from the Kaggle platform , with 8,500 data of diabetes patients analyzed based on the attributes of age, gender, and smoking history. The Clustering process was carried out using K-Means (k=3), resulting in three main groups with different lifestyle characteristics. The classification results showed that Naive Bayes provided stable performance with an F1-score of 0.975. Meanwhile, SVM excelled in terms of F1-score 98.3% and perfect AUC (1,000), and was able to classify all data in cluster C3 without error. However, SVM recorded a higher classification error in the majority cluster . This study concluded that SVM was superior by 0.8% over Naive Bayes . Naive Bayes is more suitable for balanced data, while SVM is effective for detecting patterns in minority groups. These findings support the use of a hybrid approach in lifestyle data-based diabetes early detection systems. Future research directions include integrating additional variables and ensemble techniques to improve model generalization.
Co-Authors Agusyana, Nurrahmah Ahmad Faris Al Faruq Akbar, Muh. Andika Isma Ayu Hasnining Della Fadhilatunisa Dewi Fatmarani Surianto M. Miftach Fakhri Muhammad Fadhil Hani Muhammad Fardan NIRMALA, PUTRI Nirmalasari, Aprilianti Nurhayani nurhayani Nurrahmah Agusnaya Rauf, Annajmi Ryketeng, Masdar Soeharto Soeharto Zulfikar, Muh Ihsan