Analisis dan Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Penyakit Diabetes Melitus

Abstract

Diabetes Mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action or both. Hyperglycemia is a medical condition in the form of an increase in glucose levels beyond normal limits which is a characteristic of several diseases, especially Diabetes Mellitus, in addition to various other conditions. Diabetes Mellitus is currently a global health threat. Classification is one of the techniques of data mining that can be used to help predict the results of the classification of types of diabetes using the naïve Bayes algorithm. Testing was carried out using 5 evaluation models including rapid miner with 3 options, namely use training set, 5 Fold Cross-Validation, 10 Fold Cross-Validation, and 2 other evaluation models, namely Microsoft Excel and Python. Testing data regarding Diabetes Mellitus has high accuracy in the excel evaluation model, which is 89.00% compared to other evaluation models. Meanwhile, the lowest accuracy is the Python evaluation model which obtains an accuracy of 86.36%. The Naïve Bayes algorithm can be said to be one of the most effective algorithms, both in terms of calculations and the final results, where the test can be used as a basis for diabetes mellitus considering the accuracy results are above 85%.