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Vinnia Kemala Putri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Penggunaan Heaviside Activation Function pada Regresi Linear untuk Klasifikasi Diabetes Felix Indra Kurniadi; Vinnia Kemala Putri
Ultimatics : Jurnal Teknik Informatika Vol 10 No 1 (2018): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1355.624 KB) | DOI: 10.31937/ti.v10i1.708

Abstract

Diabetes is one of the diseases that rapidly increase in the world. One of the most used dataset for diabetes is Pima indian dataset. Pima indian have 8 features such as pregnancies, glucose, blood pressure, insulin, BMI, diabetes pedigree function and age. In this research we are comparing between Linear Regression using Heaviside Activation Function and Logistic Regression. Logistic regression gives better result compare linear regression using Heaviside Activation Function. Index Terms—Diabetes, Regresi, Heaviside Activation Function, Logistic Regression
Co-Authors Felix Indra Kurniadi