Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Kesehatan dan Gizi J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Reza Pahlepi
Unknown Affiliation
Computer Science & IT Health Professions

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pemetaan Spasial Keterkaitan Faktor Risiko Kematian Neonatal dengan Mixed Geographically Weighted Regression Cinta Rizki Oktarina; Sri Syuhada Putri; Reza Pahlepi; Avrillia Permata Hati4; Dyah Setyo Rini
Jurnal Ilmu Kesehatan dan Gizi Vol. 2 No. 2 (2024): April : Jurnal Imu Kesehatan dan Gizi
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jikg.v2i2.2818

Abstract

Neonatal mortality is a major issue in developing countries, particularly in Indonesia. Data reveals that Neonatal Mortality Rate (NMR) contributes to 59% of infant deaths in Indonesia. Infant mortality rates remain high in Indonesia, at 20 per 1,000 live births. West Java has recorded a significant decline in neonatal mortality rates, dropping from 9.9 per 1,000 live births in 2019 to 9 per 1,000 in 2021. Factors influencing neonatal mortality have been extensively studied, including through the Mixed Geographically Weighted Regression (MGWR) method. The MGWR model combines local and global models, generating parameter estimators that are both local and global according to the observation locations. This research uses secondary data from the health profile of West Java, with the dependent variable being the number of neonatal deaths in 27 districts/cities in the year 2020. MGWR analysis results indicate that congenital anomalies have a local impact, while low birth weight and complete neonatal visits affect the entire West Java region globally. This study offers vital insights into the factors contributing to neonatal mortality in West Java and can serve as a foundation for targeted policy improvements and healthcare interventions
Outlier Handling in Applied Regression: Performance Comparison Between Least Trimmed Squares and Maximum Likelihood-Type Estimators Oktarina, Cinta Rizki; Andini Setyo Anggraeni; Muhammad Arib Alwansyah; Reza Pahlepi
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.082.01

Abstract

Poverty analysis often relies on regression models whose performance can deteriorate in the presence of outliers, leading to biased estimates and unreliable conclusions. This study aims to evaluate the effectiveness of robust regression methods compared with Ordinary Least Squares (OLS) when modeling poverty levels across 154 regions in Sumatra. Four socioeconomic indicators were used as predictors, and outlier detection was conducted using the DFFITS approach. After identifying deviations from normality and the presence of influential observations, two robust estimation techniques M-estimation and Least Trimmed Squares (LTS) were applied to improve model stability. The results show that while all predictors significantly influence poverty, the LTS estimator provides the most accurate and robust performance, yielding the smallest Mean Squared Error (MSE) and an R-squared value of 53.37%. These findings demonstrate that LTS is better suited than OLS and M-estimation for handling data contamination and offers a more reliable approach for modeling poverty determinants
Co-Authors Anggraeni, Andini Setyo Avrillia Permata Hati4 Cinta Rizki Oktarina Dyah Setyo Rini Muhammad Arib Alwansyah Sri Syuhada Putri