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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Pattimura International Journal of Mathematics (PIJMath)
Preatin, Preatin
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Factor Analysis on Poverty in Kalimantan Island with Geographically Weighted Negative Binomial Regression Halim, Alvin Octavianus; Satyahadewi, Neva; Preatin, Preatin
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 1 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss1pp41-52

Abstract

Poverty is one of the problems still faced by Indonesia. The problem of poverty is a development priority because poverty is a complex and multidimensional problem. Therefore, to reduce poverty, it is necessary to know the factors that influence the number of people living in poverty. The influencing factors in each region are different due to the effects of spatial heterogeneity between regions such as geographical, economic, and socio-cultural conditions. This research considers spatial factors by using the Geographically Weighted Negative Binomial Regression (GWNBR) method on poverty-based regions in Kalimantan Island. This research uses eleven independent variables. The weighting function used is the Adaptive gaussian kernel because the adaptive kernel can produce the number of weights that adjust to the distribution of observations. The stage starts with descriptive statistics and checking multicollinearity. Then proceed with the formation of Poisson Regression, because the data used is enumerated data. Then check for overdispersion. If overdispersion is detected where the variance is bigger than the mean, then Negative Binomial Regression is continued. After that, it is tested for the presence or absence of spatial heterogeneity. If there is, proceed to find the bandwidth and Euclidean distance. After that, the graphical weighting matrix is searched. Then proceed with GWNBR modeling. The results of the analysis show that there are seven significant variables, including the percentage of households with the main source of lighting is non-state electricity company (PLN), average monthly net income of informal workers, population density for every square kilometer, monthly per capita expense on food and non-food essentials, percentage of people who have a health complaint and do not treat it because there is no money and percentage of population 15 years and above who do not have a diploma. Based on the categories of significant variables, six groups were formed in 56 districts/cities in Kalimantan Island.
IMPLEMENTATION OF RESPONSE-BASED UNIT SEGMENTATION IN PARTIAL LEAST SQUARE (REBUS-PLS) FOR ANALYSIS AND REGIONAL GROUPING Al-Ham, Hairil; Satyahadewi, Neva; Preatin, Preatin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0197-0210

Abstract

Housing environmental health is a key indicator of community quality of life. In West Kalimantan Province, variations in geographical and socioeconomic conditions contribute to disparities in housing conditions. This study analyzes and classifies regions based on factors influencing housing environmental health using the Response-Based Unit Segmentation in Partial Least Squares (REBUS-PLS) method. REBUS-PLS helps detect unobserved heterogeneity by identifying subgroups with different structural relationships. The exogenous latent variables include household economics, education, and housing facilities, while the endogenous variable is housing environmental health, measured through 15 indicators. The results of the SEM-PLS analysis obtained 3 paths that had a significant effect: household economics on housing facilities, household economics on education, and housing facilities on the health of the Housing environment. SEM-PLS assumes homogeneity across data, meaning all observations follow the same structural pattern. However, this assumption may not hold, especially with data representing diverse regions. To address potential heterogeneity, REBUS-PLS was applied. The analysis revealed two distinct segments, each with stronger explanatory power than the global model, as indicated by higher R² values (Segment 1 = 95.6%, Segment 2 = 91.4%, compared to 87.7% in the global model). Segment 1 consists of Landak, Sanggau, Sekadau, Kayong Utara, and Singkawang City. Segment 2 includes Bengkayang, Melawi, Ketapang, Kapuas Hulu, Sanggau, Sekadau, Sintang, and Pontianak City. These findings confirm the presence of structural heterogeneity and demonstrate that REBUS-PLS provides a more accurate understanding of the factors affecting housing environmental health across regions.
Co-Authors Al-Ham, Hairil Halim, Alvin Octavianus Neva Satyahadewi