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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Yanathifal Salsabila Anggraeni
Universitas Esa Unggul
Computer Science & IT

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Performansi K-NN, J48, Naive Bayes dan Regresi Logistik sebagai Algoritma Pengklasifikasi Diabetes Agung Mulyo Widodo; Yanathifal Salsabila Anggraeni; Nizirwan Anwar; Arief Ichwani; Binastya Anggara Sekti
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Diabetes is a chronic disease characterized by high blood sugar (glucose) levels. This disease is often found in adults who are elderly, but this disease can also attack people who are still young. Along with advances in machine learning technology to support decision makers, many predictive models are made of whether a person can be classified as diabetic or not by using certain algorithms. In this study, a prediction model was made whether a person is classified as diabetic or not, based on parameters/variables, namely weight, height, cholesterol levels, fasting sugar, non-fasting sugar, uric acid levels and gender. Prediction model is made using K-NN, J48 (based on decision tree), Naive Bayes and logistic regression classification algorithms. Then a performance analysis was carried out on the testing results of each of these algorithms, and it was found that the K-NN algorithm produced a prediction model with the highest accuracy compared to the three algorithms used in this study.
Co-Authors Binastya Anggara Sekti Ichwani, arief Nizirwan Anwar Widodo, Agung Mulyo