JURNAL ILMIAH INFORMATIKA
Vol 10 No 01 (2022): Jurnal Ilmiah Informatika (JIF)

KLASIFIKASI DIABETES PADA WANITA MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER

Mohammad Faisal Fahrul (Universitas Stikubank Semarang)
Wiwien Hadikurniawati (Universitas Stikubank Semarang)



Article Info

Publish Date
01 Mar 2022

Abstract

The report from Riskesdas shows that there is a 2x increase in diabetes every year in Indonesia. This is due to an increase in factors such as human population, age, obesity, irregular eating patterns and lack of physical activity. The increase in a factor that causes diabetes in Indonesia must be prevented. The first step in preventing diabetes is to detect the risk factors for diabetes that may occur. Influencing factors include behavioral factors and sociodemographic factors The increase in diabetes in a country is due to late identified factors. The number of factors that are collected in order to detect whether a person has diabetes or not requires a fairly large data processing system. The data used in this study are diabetes data obtained from the Pima Indian Diabetes Database with attributes of pregnant, glucose, diastolic, triceps, insulin, BMI, history of diabetes, age and 300 data output. The Naive Bayes Classifier method can be used to classify diabetes in women based on pregnant, glucose, diastolic, triceps, insulin, BMI, history of diabetes, age and output. The accuracy result of the Naive Bayes Classifier method in classifying diabetes in women is 84% of 300 data which is divided into 2, namely 275 data as training data and 25 data as test data.

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Journal Info

Abbrev

jif

Publisher

Subject

Computer Science & IT

Description

Jurnal Teknologi Informatika dan Sistem Informasi Fakultas Teknik dan Komputer UPB, telah menerbitkan publikasi ilmiah dengan topik yang mencakup tentang Information System, Geographical Information System, Remote Sensing, Cryptography,artificial intelligence, Computer Network, Security dan ...