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All Journal International Journal of Public Health Science (IJPHS) CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Innovation and Applied Technology Mutiara Medika: Jurnal Kedokteran dan Kesehatan ComTech: Computer, Mathematics and Engineering Applications Jambura Journal of Biomathematics (JJBM) Advance Sustainable Science, Engineering and Technology (ASSET)
Henny Pramoedyo
Brawijaya University
Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology

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Determination of Stunting Risk Factors Using Spatial Interpolation Geographically Weighted Regression Kriging in Malang Henny Pramoedyo; Mudjiono Mudjiono; Adji Achmad Fernandes; Deby Ardianti; Kurniawati Septiani
Mutiara Medika: Jurnal Kedokteran dan Kesehatan Vol 20, No 2 (2020): July
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/mm.200250

Abstract

Stunting is the condition toddlers have Stunting is the condition toddlers have less length or height if compared to age. The high percentage of stunting is influenced by several factors, namely access to healthy latrines, quality drinking water, hand washing behavior with soap, coverage of posyandu access and coverage of breast milk 1-6 months, and there are indications that if an area has a high stunting percentage, then there is a possibility that the nearest area has the same condition. So, the statistic method for this research use the spatial interpolation Geographically Weighted Regression Kriging. Geographically Weighted Regression (GWR) is a weighted regression in which the weighting function is used to describe the closeness of relations between regions. The weight used is distance based weight dan weighting by area (contiguity). Ordinary kriging method calculated with semivariogram which is one function to describe and model the spatial autocorrelation between data of a variable and function as a measure of variance. The results showed that based on value GWR model with weight Fixed Gaussian Kernel better to use then the weighted GWR model Rook Contiguity. The Predicted of prevelensi stunting in the form of map based on interpolation GWR Kriging. Keywords: Stunting, GWR, and Kriging.
Community Assistance For Quality Improvement And Testing Of Dairy Products As A Superior Product In Krisik Village, Gandusari District, Blitar Regency Henny Pramoedyo; Novi Nur Aini; Bestari Archita Safitri; Suci Astutik; Achmad Efendi; Loekito Adi Soehono
Journal of Innovation and Applied Technology Vol 8, No 1 (2022)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2022.008.01.5

Abstract

Krisik Village is one of the villages located in Gandusari District, Blitar Regency, East Java Province. Krisik Village has abundant natural resources. Krisik Village has livestock products in the form of milk and its processed products which are managed independently by the Bumdes Krisik. Krisik Village already has several types of dairy products, namely fermented milk, milk sticks, milk candy and milk ice cream. As a step to improve the typical product of Krisik village, it is necessary to have an activity that is able to increase public understanding in product processing and marketing. This service activity aims to improve the quality and marketing of dairy products in Krisik village. Activities that have been carried out are in the form of coordination with the village, making ice cream packaging designs and product marketing training by utilizing social media. This activity is expected to increase the independence of the crisis village community in marketing their products.
Spatial Modeling of Fixed Effect and Random Effect with Fast Double Bootstrap Approach Wigbertus Ngabu; Henny Pramoedyo; Rahma Fitriani; Ani Budi Astuti
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 1 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i1.8033

Abstract

The use of panel data on spatial regression has many advantages. However, testing the spatial dependency and parameter presumption generated in spatial regression of panel data becomes inaccurate when applied to regions with large numbers of small spatial units. One method of overcoming problems of small spatial unit sizes is the bootstrap method. The research aimed to combine cross-section and time-series panel data. The analysis was performed to extract information based on observations modified by the influences of space or location, known as spatial analysis of panels. The influence of location effects on spatial analysis was presented in the form of weighting. The research applied the Fast Double Bootstrap (FDB) method by modeling poverty rates on Flores Island. The results of the Hausman test show the right model, which is a random effect. Meanwhile, spatial dependency testing concludes spatial dependence and poverty modeling in Flores Island, which is more likely to be the Spatial Autoregressive Random (SAR) model. SAR random effect in modeling value has R2 of 77,38% and does not meet the normality assumption. SAR effect in modeling the FDB approach can explain the diversity of poverty rate in the Flores Island with 88,64% and meets residual normality assumptions. The analysis with the FDB approach on spatial panels shows better results than the common spatial panels.
Haversine-Based Geographically Weighted Panel Regression of Human Development in Gorontalo (2016–2025) Debora Dwi Kurniawati; Henny Pramoedyo; Suci Astutik; Friansyah Gani
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): 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.v11i1.40886

Abstract

Spatial disparities in human development indicate that socioeconomic factors may influence development outcomes differently across locations. This study aims to analyze spatially varying relationships between the Human Development Index and its key determinants in districts and cities in Gorontalo Province, Indonesia, during the period 2016--2025. The analysis uses balanced panel data and models human development as a function of mean years of schooling, life expectancy at birth, and real per capita expenditure. A geographically weighted panel regression approach is applied, with spatial relationships modeled using great-circle distances and an adaptive kernel weighting scheme, while a fixed-effects panel model serves as the global reference. The results reveal a clear spatial heterogeneity in the effects of the explanatory variables, where education consistently shows the strongest positive influence on human development in all regions, followed by health conditions. Economic expenditure exhibits a weaker and spatially varying effect and is not influential in the provincial capital. These findings underscore the importance of accounting for spatial heterogeneity in regional development analyses and support the formulation of place-based human development policies tailored to local conditions.
A Multiscale Extension of Geographically Weighted Spline Nonparametric Regression: Model Formulation and Theoretical Properties Indi Rizqy Fahrani; Henny Pramoedyo; Atiek Iriany
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): 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.v11i2.42954

Abstract

This study proposes a Multiscale Geographically Weighted Spline Nonparametric Regression (MS-GWSNR) model to simultaneously accommodate spatial heterogeneity, multiscale spatial variation, and nonlinear relationships within a unified regression framework. The proposed model extends Geographically Weighted Spline Nonparametric Regression (GWSNR) by incorporating component-specific bandwidths adapted from the Multiscale Geographically Weighted Regression (MGWR) approach. Parameter estimation is developed using a Weighted Least Squares (WLS) framework combined with a backfitting algorithm to address the absence of a closed-form estimator under multiple spatial weighting matrices. The theoretical properties of the estimator are derived through a smoothing matrix representation, including unbiasedness, variance, and Mean Squared Error (MSE). A numerical illustration using simulated spatial data with controlled multiscale heterogeneity shows that the proposed estimator converges stably within 12 iterations, with a final smoothing operator convergence value of 2.48 × 10−5. The estimation also yields satisfactory reconstruction accuracy with MAE of 0.0680 and RMSE of 0.0880. These results indicate that the proposed MS-GWSNR model can flexibly reconstruct nonlinear spatial relationships across different spatial scales while maintaining stable estimation performance.
Geospatial patterns and determinants of toddler stunting: evidence from geographically weighted regression Muhammad Anismuslim; Henny Pramoedyo; Sri Andarini; Sudarto Sudarto
International Journal of Public Health Science (IJPHS) Vol 15, No 1: March 2026
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v15i1.23216

Abstract

This study aimed to map and analyze the spatial distribution of toddler stunting in Malang and identify key risk factors that are spatially correlated with stunting incidence across sub-districts and villages. A geospatial modeling approach using geographically weighted regression (GWR) was employed to account for local variations in the influence of risk factors, reflecting the heterogeneity of conditions that contribute to stunting in different areas. The analysis revealed significant spatial autocorrelation, with stunting cases clustering in specific locations. Results indicate that sanitation risks and household waste management practices were the most significant determinants of stunting prevalence among toddlers in Malang. Improper waste segregation, which leads to odors and attracts flies, and ineffective disposal methods, such as open burning or dumping, were strongly associated with higher stunting rates. These findings underscore the importance of targeted interventions addressing environmental health and sanitation at the local level. By integrating geospatial analysis with GWR modeling, this study highlights the spatial heterogeneity of stunting determinants, providing evidence to guide community-specific public health strategies. Improved sanitation practices and proper household waste management are critical to reducing toddler stunting in areas with clustered risk.
Bayesian Generalized Poisson Regression Modeling for Overdispersed Maternal Mortality Data Dewi Ratnasari Wijaya; Henny Pramoedyo; Ni Wayan Surya Wardhani
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.1928

Abstract

Maternal mortality is a global health issue that reflects disparities in access to and the quality of healthcare services. This study applies the Bayesian Generalized Poisson Regression (BGPR) approach to address the problem of overdispersion in the data, which renders the standard Poisson regression model less appropriate. The Generalized Poisson model was chosen for its ability to handle overdispersion, while the Bayesian approach provides more stable parameter estimates, particularly when working with small sample sizes. The analysis results show that all independent variables have a statistically significant effect on maternal mortality. In addition, the BGPR model yields a lower Bayesian Information Criterion (BIC) value compared to the standard Poisson model, indicating better model performance. The BGPR model helps identify the key factors that truly contribute to maternal mortality, making the results useful for local governments or health institutions in setting priorities for intervention.
Geographically Weighted Poisson Regression Modeling Using Adaptive Gaussian Kernel Weighting For Mapping Maternal Mortality Rates In East Java Inayati Ngoro; Henny Pramoedyo; Ani Budi Astuti
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 4: December 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i4.30411

Abstract

Maternal Mortality Rate (MMR) is a key public health indicator that reflects spatial variation across districts in East Java.  This study aims to model the spatial distribution of MMR using Geographically Weighted Poisson Regression (GWPR) with an Adaptive Gaussian Kernel weighting function. Secondary data were obtained from the 2022 East Java Provincial Health Profile, covering 38 districts and municipalities. The results indicate that GWPR outperforms the classical Poisson regression. The intercept β=2.889 (exp=17.95) suggests an average of 18 maternal deaths in the absence of predictor effects. The coverage of the fourth antenatal care visit (K4) has a significant negative effect ( β=-0.027; exp = 0.973), indicating that a 1% increase in K4 coverage reduces MMR by approximately 2.7%. Conversely, obstetric complications managed by midwives show a significant positive effect (β= = 0.0173; exp = 1.017), meaning that a 1% increase in complications raises MMR by 1.7%. Other predictorsfirst antenatal care visit (K1), ironfolic acid (IFA) supplementation, and number of health workersare not statistically significant. This study underscores the importance of expanding K4 coverage and strengthening complication management as priority strategies to reduce maternal mortality.  Furthermore, GWPR-based mapping enables more targeted maternal health interventions tailored to local characteristics.
Co-Authors Achmad Efendi Adji Achmad Rinaldo Fernandes Ani Budi Astuti Atiek Iriany Bestari Archita Safitri Debora Dwi Kurniawati Deby Ardianti Dewi Ratnasari Wijaya Friansyah Gani Inayati Ngoro Indi Rizqy Fahrani Kurniawati Septiani Loekito Adi Soehono Mudjiono Mudjiono Muhammad Anismuslim Ni Wayan Surya Wardhani Novi Nur Aini Rahma Fitriani Sri Andarini Suci Astutik Sudarto Sudarto Wigbertus Ngabu