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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi CommIT (Communication & Information Technology) Natural B Industri Inovatif : Jurnal Teknik Industri Pattimura Proceeding : Conference of Science and Technology
Henny Pramoedyo
Jurusan Matematika Fakultas MIPA Universitas Brawijaya Jalan Veteran Malang 65145
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics

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Bayesian Geographically Weighted Generalized Poisson Regression Modeling on Maternal Mortality in NTT in 2022 Dewi Ratnasari Wijaya; Henny Pramoedyo; Ni Wayan Suryawardhani
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.31626

Abstract

Maternal mortality is a crucial indicator of healthcare quality, particularly in East Nusa Tenggara (NTT) Province, which still records high mortality rates with significant spatial variation. This study aims to model maternal mortality in NTT in 2022 using the Bayesian Geographically Weighted Generalized Poisson Regression (BGWGPR) approach. This method integrates spatial weighting techniques with Bayesian parameter estimation through Gibbs Sampling to address spatial data characterized by overdispersion. Significant factors, including pregnant women's visits to healthcare facilities (K1), were found to influence the distribution of maternal deaths across districts in NTT. The model identifies that visits to healthcare facilities (K1) (X_1) are significant across all regions, while the variable for pregnant women receiving Tetanus Toxoid (X_3) is only significant in Alor and Timor Tengah Selatan. This model not only provides insights into determining factors but also helps identify priority areas for intervention. Therefore, this study contributes to evidence-based health policy-making aimed at reducing maternal mortality in NTT. The BGWGPR approach proves to be relevant for analyzing complex spatial data and can be applied to other epidemiological cases.
Modelling Geographically Weighted Truncated Spline Regression Using Maximum Likelihood Estimation for Human Development Disparities Laode Muhammad Saris; Henny Pramoedyo; Adji Achmad Rinaldo Fernandes
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.31381

Abstract

A development of nonparametric truncated spline regression, Geographically Weighted Regression Spline Truncated (GWSTR) incorporates spatial effects in the modelling of nonlinear relationships between the response and predictor variables. This research utilizes the Maximum Likelihood Estimation (MLE) technique to estimate the parameters of the model. The first-order truncated spline with a single knot yielded a minimal Generalized cross-validation (GCV) value of 1. 729781, suggesting a high level of accuracy in the model.  Four weighting functions were evaluated: Gaussian Kernel, Exponential Kernel, Bi-Square Kernel, and Tri-Cube Kernel. Among these, the Bi-Square weighting function performed the best, achieving a coefficient of determination of 99.999%, demonstrating the model’s capability to explain nearly all data variability effectively. GWSTR proves to be a robust method for capturing complex nonlinear relationships while accounting for spatial variations, making it a valuable tool for spatial data analysis across various disciplines.
Co-Authors Adji Achmad Rinaldo Fernandes Alfi Fadliana Arif Ashari Danang Ariyanto Debora Dwi Kurniawati Dewi Ratnasari Wijaya Eka Saraswati Elok Pratiwi Fahimah Fauwziyah Laode Muhammad Saris Liduina Asih Primandari Meilinda Trisilia Meilinda Trisilia Ni Wayan Surya Wardhani Ni Wayan Suryawardhani Novi Nur Aini Nur Mahmudah Rahma Fitriani Ria Rosilawati Suci Astutik Vita Dewi Islami