Heaviside Activation Function in Linear Regression for Diabetes Classification

Authors

  • Felix Indra Kurniadi Tanri Abeng University
  • Vinnia Kemala Putri Universitas Indonesia

DOI:

https://doi.org/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

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Published

2018-04-11

How to Cite

Kurniadi, F. I., & Putri, V. K. (2018). Heaviside Activation Function in Linear Regression for Diabetes Classification. Ultimatics : Jurnal Teknik Informatika, 10(1), 7–10. https://doi.org/10.31937/ti.v10i1.708

Issue

Vol. 10 No. 1 (2018): Ultimatics : Jurnal Teknik Informatika

Section

Articles

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