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All Journal Media Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Mahasiswa Statistik Journal of Innovation and Applied Technology 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 Pakem : Jurnal Pengabdian Kepada Masyarakat
Henny Pramoedyo
Prodi Statistika Jurusan Matematika FMIPA Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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IMPLEMENTATION OF LOCALLY COMPENSATED RIDGE-GEOGRAPHICALLY WEIGHTED REGRESSION MODEL IN SPATIAL DATA WITH MULTICOLLINEARITY PROBLEMS (Case Study: Stunting among Children Aged under Five Years in East Nusa Tenggara Province) Fadliana, Alfi; Pramoedyo, Henny; Fitriani, Rahma
MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.2.125-135

Abstract

East Nusa Tenggara Province, according to the findings of 2013 Baseline Health Research and 2016 and 2017 Nutritional Status Surveys, was recorded as the province with the highest prevalence of stunting in Indonesia. Efforts should be made to formulate policies that are integrated with spatial aspects in order to reduce the prevalence of stunting. The LCR-GWR model approach is used by using locally compensated ridge, which were meant to adjusts to the effect of collinearity between predictor variables (i.e., the factors affecting the prevalence of stunting) in each area. Results of the analysis showed that factors affecting the prevalence of stunting in all districts/cities in East Nusa Tenggara Province are the percentage of children aged under five who were weighed ≥ 4 times, the percentage of children aged under five who receive complete basic immunization, the percentage of households consuming iodized salt, the percentage of households with decent source of drinking water and the real per capita expenditure. The analysis showed that LCR-GWR is able to produce a better model than the GWR model in overcoming local multicollinearity problems in stunting in East Nusa Tenggara Province, with lower RMSE value (0.0344) than the GWR RMSE model (3.8899).
Determination of Stunting Risk Factors Using Spatial Interpolation Geographically Weighted Regression Kriging in Malang Pramoedyo, Henny; Mudjiono, Mudjiono; Fernandes, Adji Achmad; Ardianti, Deby; Septiani, Kurniawati
Mutiara Medika: Jurnal Kedokteran dan Kesehatan Vol 20, No 2: July 2020
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.
Distance and Areas Weighting of GWR Kriging for Stunting Cases In East Java Ardianti, Deby; Pramoedyo, Henny; Nurjannah, Nurjannah
CAUCHY Vol 6, No 4 (2021): 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/ca.v6i4.10455

Abstract

Spatial heterogeneity shows the characteristic location from one location to others location and it is the main assumption in Geographically Weighted Regression.  The location becomes a weight on GWR model, There are two groups of location weight namely based on distance and area. The weight considers the closeness between the location. The accuracy weighted is needed because the weighting represents the data location. The aim of this research was to get a suitable weighting method for stunting data. This research used secondary data about stunting and the influence factors of stunting such as coverage visiting of pregnant women (K1), consumption of FE tablet, exclusive of breastfeeding, immunization coverage, and clean health behaviour. Those data obtained from the Healthy Ministry of East Jawa.Based on the results of this research show that the goodness weighting for GWR modell is Adaptive Bisquare Kernel (distance weighting). The predicted mapping stunting is showed by interpolation Kriging with a range of 27%  to 49,5%.
Efektifitas Model Regresi OLS (Ordinary Least Square) dan Geographically Weighted Regression (GWR) pada Indeks Pembangunan Manusia (IPM) di Provinsi Jawa Timur Nur Azizah; Henny Pramoedyo
Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Vol 3 No 1 (2019): Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai Islami)
Publisher : Mathematics Department

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (417.163 KB)

Abstract

Indeks Pembangunan Manusia (IPM) merupakan indikator penting untuk menilai keberhasilan dalam upaya membangun kualitas hidup manusia (masyarakat/penduduk). Pembangunan manusia di Jawa Timur mengalami peningkatan selama periode 2011 hingga 2016. Oleh karena itu, peningkatan IPM tersebut perlu dijaga agar IPM di Provinsi Jawa Timur lebih meningkat. Peningkatan IPM berdampak kepada peningkatan kualitas hidup masyarakat di Provinsi Jawa Timur. Guna meningkatkan IPM, faktor-faktor yang berpengaruh terhadap peningkatan IPM perlu ditingkatkan. Penelitian ini bertujuan untuk memodelkan IPM di Provinsi Jawa Timur tahun 2016 menggunakan model regresi linier OLS (Ordinary Least Square) dan Geographically Weighted Regression (GWR), mengetahui variabel yang mempengaruhi IPM dan membandingkan efektifitas kedua model tersebut. Hasil penelitian disimpulkan bahwa metode regresi OLS lebih efektif dalam memodelkan IPM dibandingkan dengan model GWR berdasarkan nilai Akaike’s Information Criterion (AIC). Nilai AIC pada regresi OLS (77,78) lebih rendah dibandingkan dengan model GWR (190,5837). Variabel independen yang mempengaruhi nilai IPM berdasarkan regresi OLS yaitu angka partisipasi kasar SMU (X1), jumlah rata-rata pengeluaran per kapita sebulan untuk makanan (X2) dan angka kematian bayi (X3) dengan nilai R2 sebesar 78,86%. Hal ini menunjukkan bahwa keragaman yang dapat dijelaskan oleh model regresi OLS pada IPM di Jawa Timur tahun 2016 sebesar 78,86%.
Technical Guidance for Improving Information from Deterministic and Stochastic Modeling for Sectoral Data of Diskominfo Malang Regency Kusdarwati, Heni; Pramoedyo, Henny; Amaliana, Luthfatul
Journal of Innovation and Applied Technology Vol 9, No 1 (2023)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2023.009.01.4

Abstract

Data on the number of people with diabetes mellitus in Kanjuruan Hospital and monthly rainfall in Malang are used as examples for extracting information with inferential statistics. The statistic models used are linear and harmonic regression deterministic models and ARIMA and SARIMA stochastic models. The purpose of community service activities is to provide technical guidance on understanding sectoral time series data analysis for Malang Regency Communication and Information Technology employees. Each participant is given a theoretical module, modeling steps and RStudio script. There is an increase in information from the number of people with diabetes mellitus in Kanjuruan Hospital and the monthly rainfall associated with increasing time becomes related to the value of the data itself at the previous time. Descriptively there is an increase in the understanding value of the ARIMA and SARIMA models between before and after technical guidance.
Location Based Stunting Modeling Using Geographically Weighted Panel Regression in Blitar Regency Pramoedyo, Henny; Ngabu, Wigbertus; Iriany, Atiek
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 21 No. 2 (2024)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2024.v21.i2.17446

Abstract

Stunting remains a significant public health issue in Blitar Regency, Indonesia, particularly in rural areas where chronic malnutrition and inadequate access to healthcare services persist as major challenges. This study aims to explore the spatial and temporal factors influencing stunting using the Geographically Weighted Panel Regression (GWPR) method. By integrating cross-sectional and time-series data from 2021 to 2023, the study evaluates various factors, including the stunting prevalence rate and independent variables such as maternal education level, per capita income, the number of postpartum mothers receiving Vitamin A supplements, immunization coverage, and the availability of healthcare personnel. The findings reveal that stunting prevalence is significantly influenced by location-specific variables, with healthcare access and nutrition being dominant factors in rural areas, while economic conditions exert a greater influence in urban areas. The GWPR model provides deeper insights into spatial heterogeneity and offers valuable guidance for designing targeted and region-specific policies to reduce stunting rates in Blitar Regency. The results indicate that the R-Square value of the GWPR model is 0.9123, meaning that 91.23% of the stunting prevalence in Blitar Regency can be explained by the independent variables in this study
SPATIAL PANEL MODELING OF PROVINCIAL INFLATION IN INDONESIA TO MITIGATE ECONOMIC IMPACTS OF HEALTH CRISES Astuti, Ani Budi; Pramoedyo, Henny; Astutik, Suci; Setiarini, An Nisa Dwi
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.105-116

Abstract

Probabilistic statistical modeling simplifies complex issues, including economic and health challenges, by applying inductive statistics. Spatial panel modeling, using Queen Contiguity weighting, has proven to be essential for analyzing inflation expenditure patterns during health crises, such as COVID-19 in Indonesia. This study highlights the impact of inflation on national economic stability and explores the inter-provincial relationships that influence inflation dynamics across expenditure groups. The purpose of this study is to develop a spatial panel model to address this gap, offering insights for policy and recovery strategies. The results reveal significant spatial interdependence in provincial inflation data, underscoring the role of spatial factors in economic analysis. Two models are identified: Spatial Autoregressive Model with Random Effects (SAR-RE) before the crisis and Spatial Error Model with Random Effects (SEM-RE) during the crisis. Transportation facilities consistently affect inflation, demonstrating the effectiveness of spatial panel modeling in guiding policies for economic stability and recovery.
Spatio-Temporal Median Polish Kriging with ARIMA Integration for Monthly Precipitation Interpolation in East Kalimantan Jannah, Friendtika Miftaqul; Fitriani, Rahma; Pramoedyo, Henny
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.29570

Abstract

Precipitation can lead to disasters like droughts and floods, necessitating accurate interpolation methods. Traditional spatio-temporal kriging often struggles with outliers, which can reduce estimation reliability. This study develops spatio-temporal median polish kriging, which separates spatial and temporal components to improve interpolation accuracy, particularly in handling outliers. Unlike conventional kriging, this method integrates median polish kriging for robust spatial interpolation and ARIMA for capturing temporal trends, making it more effective in dynamic precipitation pattern estimation. The study utilizes precipitation data from seven observation posts in East Kalimantan (2021–2023). The data is processed using a combination of spatial, temporal, and spatial-temporal modeling approaches to capture precipitation variations accurately. For spatial interpolation, the study applies kriging in median polish spatial effects. The best semivariogram model for spatial effects is exponential, which is used to characterize spatial dependencies. To capture temporal effects of median polish, the study employs ARIMA(1,2,0), which models precipitation trends over time and helps manage temporal fluctuations. For residuals of median polish interpolation, the study applies spatio-temporal kriging, using a simple sum-metric model as the best approach to integrate both spatial and temporal dependencies. The semivariograms selected for spatial, temporal, and joint dependencies follow a gaussian structure. The interpolation results reveal that precipitation increases toward the west, with precipitation patterns also showing an increasing trend over time. These findings demonstrate the model’s capability in capturing spatial and temporal precipitation variations while addressing potential outliers through the median polish approach. By utilizing a robust statistical framework, the model reduces the influence of extreme values, leading to more reliable precipitation estimates. However, this study utilizes only seven observation posts. The limited number of observation posts may introduce uncertainty in regions distant from measurement stations and affect the model's accuracy. Therefore, further research should test this model by applying it to different geographical regions with a more extensive dataset.
Modeling Spatio-Temporal Precipitation Patterns in East Kalimantan using Space-Time Kriging and Median Polish-Based Spatio-Temporal Kriging Jannah, Friendtika Miftaqul; Fitriani, Rahma; Pramoedyo, Henny
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30642

Abstract

Precipitation variability presents significant challenges for disaster risk reduction and water resource management, particularly in flood and drought-prone regions such as East Kalimantan. This study aims to develop and evaluate two statistical approaches for spatio-temporal precipitation modeling: spatio-temporal kriging (ST-Kriging) and spatio-temporal median polish kriging (ST-MPK). Using monthly precipitation data obtained from seven observation stations provided by BMKG and BPS for the period 2021 to 2023, both models were assessed using performance metrics. ST-Kriging employed a simple sum-metric semivariogram model that combines exponential spatial and Gaussian temporal components. This model achieved an RMSE of 84.05, MAE of 69.95, and MAPE of 52.67%. Meanwhile, ST-MPK model, incorporating robust median polish decomposition and ST-Kriging of residuals, produced a lower MAPE of 44.83% with higher RMSE (122.44) and MAE (91.35). This suggests that while ST-Kriging offers better absolute error performance, ST-MPK provides greater relative accuracy and improved robustness to outliers, critical advantages for modeling precipitation in regions undergoing environmental shifts, where anomalies and extremes are increasingly common. These findings highlight ST-MPK’s potential to produce more reliable forecasts under irregular precipitation conditions, supporting early warning systems and informed water resource planning. Scientifically, this research contributes a robust modeling framework suitable for data-scarce and outlier-prone contexts. Practically, it can aid policymakers in designing adaptive flood mitigation strategies and sustainable water management policies tailored to the evolving climate realities of East Kalimantan.
Spatial Panel Regression Modelling of Rainfall in Indonesia Saniyawati, Fang You Dwi Ayu Shalu; Astutik, Suci; Pramoedyo, Henny
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.29882

Abstract

Rainfall is amount of water that falls to the earth's surface in the form of rain during a certain period of time, usually measured in millimeters. Rainfall data in Indonesia usually includes temporal and spatial dimensions, so the appropriate method for its analysis is spatial panel regression analysis. This study aims to identify factors that influence the amount of rainfall in Indonesia. This type of research is quantitative using secondary data from the central statistics agency website. The predictor variables used include air temperature, sunshine radiation, humidity, wind speed, and air pressure, while the response variable is amount of rainfall in 34 provinces in Indonesia. Spatial panel regression analysis is carried out using maximum likelihood estimation, which is used to estimate the regression coefficient and intercept that maximizes the likelihood of the existing data. Based on the lagrange multiplier test, spatial autocorrelation was found in the lag, so the appropriate model is SAR-FE. This model can overcome spatial autocorrelation by taking into account spatial interactions between locations, as well as controlling unobserved heterogeneity through fixed effects. The results show that sunshine radiation, humidity, and wind speed have significant effect on the amount of rainfall in Indonesia. The AIC value of SAR-FE model (-4.352594×〖10〗^(-13)) is smaller than SEM-FE model (-1.642001×〖10〗^(-12)), indicating that SAR-FE model is better at explaining the data.
Co-Authors Achmad Efendi Alfi Fadliana Amelia, Okky Ariska Ani Budi Astuti Ani Budi Astuti Ardianti, Deby Arif, Nur Fitriyah Atiek Iriany Chairunissa, Abela Danang Ariyanto Fachri Faisal Fernandes, Adji Achmad Handoyo, Samingun Heni Kusdarwati Jaka Pratama Musashi Jannah, Friendtika Miftaqul Khoirunnisa, Elsa Azizah Korniasari, Leli Dwi Kurniawan, Galih Widhi Kusdarwati, Heni Liduina Asih Primandari, Liduina Asih Loekito Adi Soehono Luthfatul Amaliana, Luthfatul Maghfiroh, Aulia Maharani, Kasih Megasari, Dannys Mitakda, Maria Bernadetha Mudjiono Mudjiono, Mudjiono Ni Wayan Surya Wardhani Ni Wayan Surya Wardhani Novi Nur Aini, Novi Nur Nur Azizah Nurjannah Nurjannah Rahma Fitriani Ramadhan, Apry Zakaria Ramadhan, Rajuliaddin Ridwan, Yusrina Layli Riza, Sativandi Rizkiyani, Cirairina Sa’adah, Zumrotus Saniyawati, Fang You Dwi Ayu Shalu Sariyya, Hanah Rista Septiani, Kurniawati Setiarini, An Nisa Dwi Shara, Yoeniarti Suci Astutik Sumarminingsih, Eni Widya Reza Wigbertus Ngabu Yanuari, Anindita Yuli Triana, Eka Putri