Kohesi: Jurnal Sains dan Teknologi

Articles

Vol. 10 No. 4 (2025): Kohesi: Jurnal Multidisiplin Saintek

PERBANDINGAN AKURASI NAÏVE BAYES DECISION TREE UNTUK KLASIFIKASI RISIKO DIABETES

DOI:
https://doi.org/10.2238/g2afsv55
Submitted
August 28, 2025
Published
2025-08-28

Abstract

Diabetes is one of the chronic diseases with a prevalence that continues to increase globally. Early detection of diabetes risk through a data mining approach can assist in the prevention and management of this disease. This study aims to compare the performance of the Naïve Bayes and Decision Tree algorithms in classifying diabetes risk based on a patient dataset containing several clinical variables. The results show differences in accuracy between the two algorithms, which can serve as a consideration in selecting classification methods for medical applications.

Diabetes merupakan salah satu penyakit kronis yang prevalensinya terus meningkat secara global. Deteksi dini risiko diabetes melalui pendekatan data mining dapat membantu dalam pencegahan dan pengelolaan penyakit ini. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Naïve Bayes dan Decision Tree dalam mengklasifikasikan risiko diabetes berdasarkan dataset pasien yang berisi sejumlah variabel klinis. Hasil penelitian menunjukkan perbedaan akurasi antara kedua algoritma, yang dapat menjadi pertimbangan dalam pemilihan metode klasifikasi untuk aplikasi medis.

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