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All Journal Jurnal Aplikasi Statistika & Komputasi Statistik PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Ristiyanti Tarida, Arna
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Pemodelan Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) pada Kasus HIV di Indonesia Djuraidah, Anik; Anisa, Rahma; Ristiyanti Tarida, Arna; Alwi Aliu, Muftih; Septemberini, Cintia; Putri Astrini, Yufan Putri Astrini; Tasya Meilania, Gusti
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i2.608

Abstract

In general, spatial regression is used to model one of the spatial effects, namely spatial dependency or heterogeneity. For the effects of spatial dependencies, the models that have been used frequently follow Elhost's taxonomy, with the spatial dependencies being on the response, predictor, or error. Whereas for the effect of spatial heterogeneity generally use geographically weighted regression models (GWR) or if there are global predictors use mixed geographically weighted regression (MGWR). The data used in this study are cases of Human Immunodeficiency Virus (HIV) per 100,000 population as a response variable, and key populations, positive cases in pregnant women, tuberculosis patients, poverty rate, and unemployment rate as predictors. In the data used, there are spatial dependencies and heterogeneity. The MGWR-SAR is a model that can be used if the data has both spatial effects. This study aims to determine the factors influencing HIV cases in districts/cities in Indonesia using a spatial model. The results showed that the combined model of GWR and spatial autoregressive regression (SAR) was the best model. Key population explanatory variables have a global and significant influence. Other explanatory variables that have local influence are positive cases in pregnant women, tuberculosis patients, poverty rates, and unemployment rates.
Clustering of Junior High School Education in West Java Based on Density and Dropout Ratios Using Quartile and KMeans Methods Nurkhofifah, Eva; Athina, Dwilaras; Ristiyanti Tarida, Arna; Amelia Pratiwi, Friska
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.662

Abstract

Education disparities across regions often reflect differences in school density, teacher availability, and student dropout rates. This study aims to classifies junior high school education in West Java into more homogeneous groups to better understand these disparities. Two clustering approaches were applied: quartile grouping and the K-Means algorithm. Quartile grouping provided a simple categorization of each indicator into four levels (very high, high, low, very low), while K-Means offers a more flexible and data-driven segmentation. K-Means algorithm produced three distinct clusters: (1) Balanced and Stable regions with proportional ratios and low dropout rates, (2) High-Density but Stable regions concentrated in urban and periurban areas with high student-teacher and student-school ratios but controlled dropout levels, and (3) Elevated Dropout Risk regions, mostly in rural and southern areas, with lower density but higher dropout rates. The comparison shows that quartile grouping is easy to interpret for individual indicators, while K-Means provides more comprehensive insights into multidimensional patterns. This research highlights the potential of clustering methods to guide policymakers in designing differentiated strategies, from infrastructure expansion in dense regions to social support programs in dropout-prone areas. 
Co-Authors Alwi Aliu, Muftih Amelia Pratiwi, Friska Anik Djuraidah Anisa, Rahma Athina, Dwilaras Nurkhofifah, Eva Putri Astrini, Yufan Putri Astrini Septemberini, Cintia Tasya Meilania, Gusti