Sukmana, Difa Ageng (2023) Penerapan Algoritma K-Nearest Neighbor (KNN) dan Naive Bayes untuk Klasifikasi Diabetes Mellitus. Undergraduate thesis, Fakultas Teknologi Informasi Universitas Merdeka Malang.

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Abstract

This research focuses on the application of K-Nearest Neighbor (KNN) and Naive Bayes algorithms for diabetes mellitus classification. Diabetes mellitus is a serious global health problem, requiring accurate early diagnosis to prevent further complications. KNN and Naive Bayes are classification methods that have good performance in various cases. The purpose of this research is to compare the performance of KNN and Naive Bayes in diabetes classification and implement a model comparison of these two algorithms. This research uses data analysis on a dataset from the Malang District Health Office with a total of 3811 samples. The results showed that the KNN model performed slightly better than Naive Bayes in classifying diabetes data. KNN has an accuracy of 91.57% while Naive Bayes reaches 90.78%. Although this difference is not significant, KNN has better precision and F1-Score values. On the other hand, Naive Bayes has a slightly higher recall than KNN.

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