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All Journal JITRO (Jurnal Ilmiah dan Teknologi Peternakan Tropis) BAREKENG: Jurnal Ilmu Matematika dan Terapan Inferensi Jurnal Abdi Masyarakat Saburai (JAMS) JURNAL ILMU MANAJEMEN SABURAI (JIMS) Ekombis Sains: Jurnal Ekonomi, Keuangan dan Bisnis SENTRI: Jurnal Riset Ilmiah Science and Education Journal Mathematical Journal of Modelling and Forecasting journal of social and economic research Diophantine Journal of Mathematics and Its Applications
Reza Pahlepi, Reza
Halu Oleo University
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Veterinary

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Journal : Mathematical Journal of Modelling and Forecasting

 1 
Modelling the Number of Stunting Cases in Indonesia in 2022 Using Negative Binomial Regression to Address Overdispersion Oktarina, Cinta Rizki; Pahlepi, Reza
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 2 (2024): December 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i2.27

Abstract

This study models the incidence of stunting in toddlers in Indonesia in 2022 using negative binomial regression to address the overdispersion issue often present in count data. The Poisson regression model, typically used for count data, showed less accurate results due to the variance exceeding the mean, indicating overdispersion. By adopting a negative binomial regression approach, this study accommodates higher variability in the data, leading to more accurate estimates. The results reveal that the percentage of pneumonia cases and low birth weight are significant factors in stunting incidence. In contrast, other variables, such as complete basic immunization and poverty levels, are insignificant in the final model. The final negative binomial model yielded a lower AIC value than the initial model, indicating an improved model fit, with an R-squared (Nagelkerke's R²) of 50.50%. This study offers enhanced insights into the factors influencing stunting, supporting more targeted health policy decisions to reduce stunting rates in Indonesia.
Statistical Modelling of Rainfall Data Using Robust Kriging with Gaussian Semivariogram in Bengkulu Province Rizki Oktarina, Cinta; Pahlepi, Reza
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 2 (2025): December 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i2.46

Abstract

This study aims to predict rainfall in Bengkulu Province for January 2024 using the Robust Kriging method, an advanced geostatistical approach designed to handle outliers and non-ideal spatial characteristics. The novelty of this study lies in integrating Robust Kriging with a Gaussian semivariogram for short-term rainfall prediction in Bengkulu Province. This combination has not been explored in previous hydrometeorological studies. Rainfall data were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) and analysed to identify spatial dependency and variation. The analysis began with descriptive statistics, assumption testing, and outlier detection, followed by the construction of robust empirical and theoretical semivariogram models. Three semivariogram models, Spherical, Exponential, and Gaussian, were compared to determine the most suitable model based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. The results indicate that the Gaussian model produced the smallest MSE and MAPE values, showing the best fit to the empirical semivariogram. The Robust Kriging interpolation generated spatial predictions of rainfall intensity across Bengkulu, showing higher rainfall in the north and lower rainfall in the south. The findings demonstrate that Robust Kriging effectively improves prediction accuracy by minimizing the influence of outliers and optimizing spatial weighting. These results provide valuable insights for water resource management, agricultural planning, and hydrometeorological disaster mitigation in Bengkulu Province.
Co-Authors Aghnia, Haliza Agwil, Winalia Ahiruddin Ahiruddin Amalia, Helvi Cinta Rizki Oktarina Dewi, Ade Sandra Dian Agustina Enjelina, Tiara Fajar Sasora Gaol, Rotua Astrina Lumban Husna Purnama Idhia Sriliana Josiah, Trinosnowati Khairul Saleh Kinanti Nurul Faithya Kuswarak, Kuswarak Lies Kumara Dewi Mashuri, Faa’iz Nurul Hidayati Pepi Novianti Pujiati, Ani Rauf, Eka Ubaya Taruna Setiawan, Ade Agung Sigit Nugroho Sunandi, Etis Syuhada Putri, Sri Yanti, Rizki Dwi Yuliana Yamin