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All Journal International Journal of Electrical and Computer Engineering Journal of Nonformal Education Telematika Jurnal Transformatika JUITA : Jurnal Informatika Jurnal The Messenger Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pengembangan Rekayasa dan Teknologi Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknologi Informasi dan Komunikasi Information Science and Library SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Tematik
April Firman Daru
Universitas Semarang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Social Sciences Transportation

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Journal : JUITA%20:%20Jurnal%20Informatika

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Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection April Firman Daru; Mohammad Burhan Hanif; Edi Widodo
JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (759.462 KB) | DOI: 10.30595/juita.v9i1.9941

Abstract

Diabetic or silent killer diseases are an alarming scourge for the world and are classed as serious diseases. In Indonesia, the increase in diabetics occurred by 2% in vulnerable times between 2013 to 2018. This affects all sectors, both medical services and the financial sector. The Neural Network method as a data mining algorithm is present to overcome the burden that arises as an early detection analysis of the onset of disease. However, Neural Network has slow training capabilities and can identify important attributes in the data resulting in a decrease in performance. Pearson correlation is good at handling data with mixed-type attributes and is good at measuring information between attributes and attributes with labels. With this, the purpose of this study will be to use the Pearson correlation method as a selection of features to improve neural network performance in diabetes detection and measure the extent of accuracy obtained from the method. The dataset used is diabetes data 130-US hospital UCI with a record number of 101767 and the number of attributes as many as 50 attributes. The results of this study found that Pearson correlation can improve neural network accuracy performance from 94.93% to 96.00%. As for the evaluation results on the AUC value increased from 0.8077 to 0.8246. Thus Pearson's Correlation algorithm can work well for feature selection on neural network methods and can provide solutions to improved diabetes detection accuracy.
Co-Authors Alauddin Maulana Hirzan Aria Hendrawan, Aria Arif Kurniawan Atmoko Nugroho Atmoko Nugroho B. Very Christioko Basworo Ardi Pramono Didin Herlinudinkhaji, Didin Dominicus Ferdian Wendy Pratama Edi Widodo Edi Widodo Erlangga, Erwin Febrian Wahyu Christanto Galet Guntoro Setiaji Handayani, Sri Heddy Tyas Darmawan Hindriyanto Dwi Purnomo Irwan Desyantoro Khoirudin Khoirudin Kristoko Dwi Hartomo Mohammad Burhan Hanif Muhammad Burhan Hanif nofiati nofiati Rastri Prathivi Rastri Prathivi Saifur Rohman Cholil Sara Sharifzadeh Shona Chayy Bilqisth Slamet Ashadi Sugiarti, Rini Veronika, Nofita Whisnumurti Adhiwibowo