Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Data Science Methods and Applications
Khanaya Erviona
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering Library & Information Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analisis Perbandingan Algoritma Klasifikasi Decision Tree, K-Nearest Neighbors, Naive Bayes, dan Random Forest pada Data Pemilihan Legislatif KPU Menggunakan Kurva ROC Naura Fayza I; Nicholas Svensons; Sri Asni Fatmawati; Pricillia Rotua S; Khanaya Erviona
Journal of Data Science Methods and Applications Vol. 1 No. 1 (2025)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the context of the digital information era, analysis of general election data is crucial for understanding political dynamics. Legislative election data from the Indonesian General Election Commission (KPU) provides insight into voter behavior and election results. Selection of an appropriate classification algorithm is the main challenge in producing accurate predictions. This study compares four classification algorithms: Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, and Random Forest, using Receiver Operating Characteristic (ROC) curves as the main evaluation. The results show Random Forest performs best in handling legislative election data, providing important insights for future policy and research.
Co-Authors Naura Fayza I Nicholas Svensons Pricillia Rotua S Sri Asni Fatmawati