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All Journal International Journal of Public Health Science (IJPHS) Media Statistika The Journal of Experimental Life Sciences (JELS) CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Mahasiswa Statistik Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Mutiara Medika: Jurnal Kedokteran dan Kesehatan Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) ComTech: Computer, Mathematics and Engineering Applications Industri Inovatif : Jurnal Teknik Industri Jambura Journal of Biomathematics (JJBM) Pakem : Jurnal Pengabdian Kepada Masyarakat
Henny Pramoedyo
Prodi Statistika Jurusan Matematika FMIPA Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Other

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Geospatial patterns and determinants of toddler stunting: evidence from geographically weighted regression Anismuslim, Muhammad; Pramoedyo, Henny; Andarini, Sri; 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.
Geographically and Temporally Weighted Regression Modeling in Analyzing Factors Affecting the Spread of Dengue Fever in Malang Indrayani, Fahmi; Pramoedyo, Henny; Iriany, Atiek
The Journal of Experimental Life Science Vol. 8 No. 2 (2018)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1175.267 KB) | DOI: 10.21776/ub.jels.2018.008.02.01

Abstract

Geographically and Temporally weighted regression (GTWR) modeling has been developed to evaluate spatial heterogeneity and temporal heterogeneity in factors influencing the spread of dengue fever in Malang city. By using the monthly data in 2012-2015 as the temporal unit of each urban village in Malang and village is considered as a spatial unit. GTWR model is compared with the GWR model using several statistical criteria. GTWR model shows that the relationship between dengue incidence with population density and monthly average temperature significantly affects each Village in Malang.Keywords : DHF, GTWR, Spatiotemporal Pattern
Enhancing Spatio-Temporal PCA with FASTMCD for Climate Comfort Assessment Yarcana, Agus; Pramoedyo, Henny; Astutik, Suci
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.37866

Abstract

This study presents a robust formulation of the Spatio-Temporal Principal Component Analysis (STPCA) by integrating the Fast Minimum Covariance Determinant (FASTMCD) estimator into the spatio-temporal decomposition framework. Unlike classical STPCA—which constructs the spatio-temporal matrix from sample-based means and is therefore highly sensitive to extreme observations—the proposed STPCA–FASTMCD replaces the classical mean and scatter structure with robust estimates derived from FASTMCD. The method incorporates functional Fourier-based temporal smoothing and an inverse power–distance spatial weight matrix to better capture the underlying spatio-temporal patterns. Monthly climate data (thermal comfort, cloud cover, rainfall, and wind speed) from 24 monitoring locations in Bali during 2010–2019 are analyzed. Performance is evaluated using mean-shift analysis, eigenvalue-stability assessment, and eigenvector perturbation diagnostics. The classical STPCA produces inflated and unstable leading components, with the first eigenvalue reaching 63.36, whereas STPCA–FASTMCD reduces this value to 37.79 and yields smoother, more coherent spatial loading patterns. The robust STPC1 reveals a clear thermal–wind variability mode, enhancing the interpretability of spatial gradients relevant to climate comfort. Overall, the proposed formulation substantially improves the stability and climatic relevance of dominant spatio-temporal modes, providing a more reliable foundation for climate comfort assessment in Bali.
Haversine-Based Geographically Weighted Panel Regression of Human Development in Gorontalo (2016–2025) Kurniawati, Debora Dwi; Pramoedyo, Henny; Astutik, Suci; Gani, Friansyah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim 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.
Spatial Variation of HDI in East Java: A Tricube-Based Geographically Weighted Regression–Flower Pollination Algorithm Modeling Approach Gani, Friansyah; Pramoedyo, Henny; Efendi, Achmad
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.38007

Abstract

Understanding spatial disparities in human development is essential for designing equitable development policies. This study examines the spatial variation of the Human Development Index (HDI) in East Java Province using an integrated Geographically Weighted Regression–Flower Pollination Algorithm (GWR–FPA) optimized with a Tricube kernel. The integration of GWR and FPA enables simultaneous spatial weighting and bandwidth optimization using the corrected Akaike Information Criterion (AICc) as the objective function. For standard GWR, the bandwidth was selected using Cross-Validation (CV) to minimize prediction error, while for the GWR–FPA model, bandwidth optimization was performed using the Flower Pollination Algorithm (FPA) with the corrected Akaike Information Criterion (AICc) as the objective function. Three predictors were analyzed: population size (X1), literacy rate (X2), and mean years of schooling (X3). Statistical diagnostics indicated significant spatial autocorrelation and heteroskedasticity in the OLS residuals, justifying the use of a spatial modeling framework. The GWR estimates revealed strong spatial non-stationarity: X1 showed no significant local effect, whereas educational factors (X2 and X3) were significant in all 38 districts and cities. The FPA optimization enhanced bandwidth selection, resulting in improved model fit. Model comparison based on AIC and AICc showed that the GWR–FPA–Tricube model achieved the lowest values (AIC = 135.8821; AICc = 137.0045), outperforming both global OLS and standard GWR. The results highlight the dominant contribution of education-related components to the spatial decomposition of HDI variation across East Java. The optimized model provides a more accurate spatial representation of local development disparities, supporting targeted policy interventions and illustrating the effectiveness of integrating metaheuristic optimization within spatial regression.
Co-Authors Achmad Efendi Alfi Fadliana Amelia, Okky Ariska Ani Budi Astuti Ani Budi Astuti Anismuslim, Muhammad Ardianti, Deby Arif, Nur Fitriyah Atiek Iriany Chairunissa, Abela Danang Ariyanto Eni Sumarminingsih Fachri Faisal Fernandes, Adji Achmad Fernandes, Adji Achmad Rinaldo Gani, Friansyah Handoyo, Samingun Heni Kusdarwati Indrayani, Fahmi Jaka Pratama Musashi Jannah, Friendtika Miftaqul Khoirunnisa, Elsa Azizah Korniasari, Leli Dwi Kurniawan, Galih Widhi Kurniawati, Debora Dwi Kusdarwati, Heni Liduina Asih Primandari, Liduina Asih Loekito Adi Soehono Luthfatul Amaliana, Luthfatul Maghfiroh, Aulia Maharani, Kasih Megasari, Dannys Melanwati, Rinda Lolita Mitakda, Maria Bernadetha Mudjiono Mudjiono, Mudjiono Ngoro, Inayati Ni Wayan Surya Wardhani Ni Wayan Surya Wardhani Novi Nur Aini, Novi Nur Nur Azizah Rahma Fitriani Ramadhan, Apry Zakaria Ramadhan, Rajuliaddin Ridwan, Yusrina Layli Riza, Sativandi Rizkiyani, Cirairina Sa’adah, Zumrotus Saniyawati, Fang You Dwi Ayu Shalu Saris, Laode Muhammad Sariyya, Hanah Rista Septiani, Kurniawati Setiarini, An Nisa Dwi Shara, Yoeniarti Siswahyudianto Sri Andarini Suci Astutik Sudarto Sudarto Sumarminingsih, Eni Suryawardhani, Ni Wayan Widya Reza Wigbertus Ngabu Wijaya, Dewi Ratnasari Yanuari, Anindita Yarcana, Agus Yuli Triana, Eka Putri