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All Journal Desimal: Jurnal Matematika STATISTIKA
Safitri Pratiwi, Luh Putu
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Decision Sciences, Operations Research & Management Education Mathematics Social Sciences

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Geographically Weighted Logistic Regression Modeling on the Spread of Dengue Fever in Bali Province Safitri Pratiwi, Luh Putu; I Made Pasek Pradnyana Wijaya
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.5852

Abstract

Abstract. One of the statistical methods that can be used for data analysis by taking spatial factors into account is Geographically Weighted Logistic Regression (GWLR). GWLR is a model where there are parameters that are influenced by location (Geographically varying coefficient) and parameters that are not influenced by location. Research continues to be carried out to understand the factors that influence the spread of dengue fever and to develop more effective strategies for controlling this disease as well as the best model for data on the spread of dengue fever in Bali Province based on AIC values. The variables used are the response variable (Y) which is the case of dengue fever. The variables studied are the number of dengue fever sufferers in 2022 as the response variable and the predictor variables are number of drinking water facilities (X1), population density (X2), number of doctors (X3), number of health workers, namely nurses (X4), and number of adequate sanitation facilities (X5). The GWLR model is better used to analyze dengue fever data in Bali compared to the Logistic Regression model seen from the low AIC value of 29.4481. The variable number of doctors (X3) is the only variable that significantly affects the probability of DHF occurrence in Bali Province at α = 10%. The positive coefficient for β3 indicates that an increase in the number of doctors increases the probability of DHF occurrence in the region.
A comparative study of poisson and negative binomial regression models on economic growth in Bali Province Safitri Pratiwi, Luh Putu; I Made Pasek Pradnyana Wijaya
Desimal: Jurnal Matematika Vol. 8 No. 3 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i3.202529212

Abstract

Economic growth is the main indicator of regional development success and is measured through Gross Regional Domestic Product (GDP). This study aims to determine the best model in explaining the factors that affect economic growth in Bali Province in 2023 using Poisson regression and Negative Binomial regression. Secondary data were obtained from the Central Statistics Agency (BPS) and the Investment Coordinating Board (BKPM), with GDP response variables and predictor variables including labor force participation rate, open unemployment rate, foreign investment, population density, and literacy rate. The results of the analysis showed that the Poisson model was over dispersed, so the Negative Binomial model gave results that were more in line with the Akaike Information Criterion (AIC) value of 174.572. Economically, increasing labor force participation and foreign investment have a positive effect on GDP, while increasing unemployment and low literacy rates reduce economic growth. Thus, the Negative Binomial regression model is considered more appropriate to explain the variation in economic growth in Bali Province because it is able to handle overdispersion and provide more stable estimation results.
Co-Authors I Made Pasek Pradnyana Wijaya