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All Journal Jurnal Varian
Aswi Aswi
Universitas Negeri Makassar, Makassar, Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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Comparison of COM-Poisson and Poisson–Tweedie Regression in Handling Overdispersion Khaeriah Wahyu; Aswi Aswi; Sitti Masyitah Meliyana R
Jurnal Varian Vol. 9 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v9i2.6383

Abstract

Count data are commonly modeled using Poisson regression. However, this model has limitations when the equidispersion assumption is violated, particularly under conditions of overdispersion. This study aims to compare the performance of the COM-Poisson and Poisson–Tweedie regression models for handling overdispersion and to identify factors influencing the number of measles cases among children under five in Indonesia in 2024. The method used in this research is a quantitative approach using secondary data on laboratory-confirmed measles cases among children under five across 38 provinces in Indonesia. The data were analyzed using COM-Poisson and Poisson–Tweedie regression models. The explanatory variables include measles immunization coverage, the number of people living in poverty, the percentage of vitamin A supplementation, the percentage of exclusive breastfeeding, and the percentage of undernourished children under five. The results of this research indicate that the Poisson regression model exhibits overdispersion and is therefore not suitable for the data. Both the COM-Poisson and Poisson–Tweedie regression models can accommodate overdispersion. Based on model selection criteria, namely the Akaike Information Criterion (AIC), the Poisson–Tweedie regression model demonstrates the best performance for analyzing the number of measles cases among children under five in Indonesia in 2024. Based on the selected model, measles immunization coverage, the number of people living in poverty, and the percentage of vitamin A supplementation have a statistically significant effect on the number of measles cases among children under five. In contrast, the percentage of exclusive breastfeeding and the percentage of undernourished children under five do not show a statistically significant effect.
Application of the Stevenson-Porter Fuzzy Time Series Method in Forecasting Oil and Gas Import Values Hairatun Hisanun; Aswi Aswi; Muhammad Fahmuddin S
Jurnal Varian Vol. 9 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v9i2.6407

Abstract

The value of Indonesia’s oil and gas imports is one of the important indicators of Indonesia’s economic growth. The high value of imports over time shows that Indonesia has not been able to manage domestic energy supply properly, which can threaten national energy security. Therefore, an accurate forecasting method is needed to forecast the value of Indonesia’s oil and gas imports. The objective of this research is to forecast the value of Indonesia’s oil and gas imports for January 2025. The method used in this study is the Stevenson–Porter fuzzy time series method, using monthly data on Indonesia’s oil and gas import values from January 2010 to December 2024. The results indicate that the forecasted value of Indonesia’s oil and gas imports for January 2025 is US$ 2,553.5 million, with a Mean Absolute Percentage Error (MAPE) of 6.21%, indicating very good forecasting accuracy. The forecast result can support policymakers in improving energy import planning and strengthening Indonesia’s national energy security.
A Negative Binomial Regression Analysis of Factors Affecting the Labor Force Participation Nur Aisyah; Aswi Aswi; Wahidah Sanusi; Indy Pratiwi HR
Jurnal Varian Vol. 9 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v9i2.6587

Abstract

Labor force participation is a crucial indicator of regional economic development, providing essential evidence for effective employment policies. However, empirical studies analyzing labor force determinants frequently rely on Poisson regression, which assumes equidispersion and produces inefficient estimates when socioeconomic count data exhibit overdispersion. Despite this widespread issue, the application of Negative Binomial Regression (NBR) to model labor force participation remains limited, particularly in South Sulawesi, Indonesia. Therefore, this study aims to examine the effects of Gross Regional Domestic Product (GRDP), average years of schooling, poverty rate, and the population aged 15 and older on the labor force in South Sulawesi, while identifying the most appropriate regression model for overdispersed count data. Using a Generalized Linear Model (GLM) approach, the study compares Poisson regression and NBR using 2023 secondary data from Statistics Indonesia (BPS). Model performance was evaluated through overdispersion testing, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Results demonstrate significant overdispersion in the data, rendering the Poisson model inadequate. Conversely, NBR demonstrates superior performance with lower AIC and BIC values. Furthermore, the findings reveal that average years of schooling and the population aged 15 and above have statistically significant positive effects on the labor force, whereas GRDP and the poverty rate do not. These findings imply that educational attainment and demographic structures are stronger determinants of labor force participation than macroeconomic conditions in South Sulawesi. Ultimately, NBR provides a more robust framework for modeling such data, offering reliable empirical evidence for regional workforce planning.
Co-Authors Hairatun Hisanun Indy Pratiwi HR Khaeriah Wahyu Muhammad Fahmuddin S Nur Aisyah Sitti Masyitah Meliyana R Wahidah Sanusi