Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Ramya Rachmawati Ramya
Universitas Bengkulu
Computer Science & IT

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

Found 1 Documents
Search

 1 
Handling Missing Data in Bivariate Gamma Generation Data Using the Random Forest Method Muhammad Arib Alwansyah Arib; Ramya Rachmawati Ramya
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKOMA.082.02

Abstract

Missing data is a common problem in data analysis that can reduce the quality and accuracy of study results if not handled properly. This study aims to evaluate the performance of the Random Forest (RF) imputation method at various levels of missing value proportions, namely 5%, 10%, 15%, and 20%. The data used are Bivariate Gamma data of 200 observations with two variables, generated using RStudio software. Evaluation of imputation performance is carried out by considering the correlation value between the imputed data and the original data, the p-value as an indicator of the significance of the difference, and the error measures Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).
Co-Authors Muhammad Arib Alwansyah Arib