Comparison of Naive Bayes Method and Support Vector Machine in Classifying Diabetes Mellitus Disease

Indah Kusuma Sari; Rizky Putra Wijaya

doi:10.69693/jesa.v2i2.33

Authors

Indah Kusuma Sari Sekolah Tinggi Teknologi Jayapura
Rizky Putra Wijaya Sekolah Tinggi Teknologi Jayapura

DOI:

https://doi.org/10.69693/jesa.v2i2.33

Keywords:

Data Mining, Diabetes Mellitus, Naïve Bayes, Support Vector Machine

Abstract

Diabetes mellitus is a chronic disease that occurs due to excessively high blood glucose levels resulting in the absence of insulin. In the period of data at the Siti Khadijah Islamic Hospital in Palembang, which is influenced by the number of patients undergoing health checks such as diabetes mellitus, it affects the classification of data that will complicate the hospital. So by utilizing data mining, classification to determine patients who have undergone examinations including diabetes sufferers or not. With these problems, the author conducted a comparative analysis of two algorithms, namely the naïve Bayes algorithm and the support vector machine algorithm for the classification of diabetes by using the WEKA tool with the Cross Validation and Confusion Matrix options tools with the highest accuracy results, namely the support vector machine algorithm with a polynomial kernel, the results of which are 96.2704% and an error rate of 3.7296%, it can be concluded that the most accurate algorithm in the classification of diabetes is the support vector machine algorithm with a polynomial kernel.

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