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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography SAINSMAT CAUCHY: Jurnal Matematika Murni dan Aplikasi JAM : Jurnal Aplikasi Manajemen JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Forum Geografi Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktik dalam Bidang Pendidikan dan Ilmu Geografi Sinkron : Jurnal dan Penelitian Teknik Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Eksakta : Berkala Ilmiah Bidang MIPA STI Policy and Management Journal SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Xplore: Journal of Statistics Scientific Journal of Informatics Media Penelitian dan Pengembangan Kesehatan Indonesian Journal of Statistics and Its Applications
Muhammad Nur Aidi
Department Of Statistics, Faculty Of Mathematics And Sciences, IPB University, Bogor 16680, Indonesia
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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation Other

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Risk Factors For Stunting Incidence In Urban and Rural Areas Of Indonesia Using Bayesian Spatial CAR Zulhijrah, Zulhijrah; Rifaldi, Destriana Aulia; Hapsari, Nimas Ayu; Sulaeman, Sulthan Naufal; Aidi, Muhammad Nur
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142712542025

Abstract

Stunting is a chronic growth disorder in children under five that requires evidence-based interventions. To understand the factors that contribute to stunting in different regions of Indonesia, Bayesian Conditional Autoregressive (CAR) modeling was used to estimate the relative risk of stunting. The analysis showed that the Besag-York-Mollié (BYM) model with covariates provided the best results in estimating the risk of stunting. The data for this study were obtained from the 2018 Basic Health Research Survey. In urban areas, immunization coverage has a significant effect on stunting risk, while in rural areas, in addition to immunization, vitamin supplementation coverage and poverty level are also significant factors. Based on the modeling, the region with the highest risk in urban areas is West Sulawesi Province with a relative risk of 1.638, while the lowest is Bali Province with 0.564. In rural areas, Papua Province had the highest risk of 1.820, while North Sulawesi Province had the lowest risk of 0.599. These findings suggest that immunization coverage is instrumental in reducing stunting, both in urban and rural areas. In addition, in rural areas, increasing vitamin supplementation coverage and decreasing poverty levels can help reduce the risk of stunting. Therefore, intervention policies should be tailored to the characteristics of each region to be more effective in addressing stunting in Indonesia.
Risk Factors For Stunting Incidence In Urban and Rural Areas Of Indonesia Using Bayesian Spatial CAR Zulhijrah, Zulhijrah; Rifaldi, Destriana Aulia; Hapsari, Nimas Ayu; Sulaeman, Sulthan Naufal; Aidi, Muhammad Nur
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 2 (2025): September
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat142712542025

Abstract

Stunting is a chronic growth disorder in children under five that requires evidence-based interventions. To understand the factors that contribute to stunting in different regions of Indonesia, Bayesian Conditional Autoregressive (CAR) modeling was used to estimate the relative risk of stunting. The analysis showed that the Besag-York-Mollié (BYM) model with covariates provided the best results in estimating the risk of stunting. The data for this study were obtained from the 2018 Basic Health Research Survey. In urban areas, immunization coverage has a significant effect on stunting risk, while in rural areas, in addition to immunization, vitamin supplementation coverage and poverty level are also significant factors. Based on the modeling, the region with the highest risk in urban areas is West Sulawesi Province with a relative risk of 1.638, while the lowest is Bali Province with 0.564. In rural areas, Papua Province had the highest risk of 1.820, while North Sulawesi Province had the lowest risk of 0.599. These findings suggest that immunization coverage is instrumental in reducing stunting, both in urban and rural areas. In addition, in rural areas, increasing vitamin supplementation coverage and decreasing poverty levels can help reduce the risk of stunting. Therefore, intervention policies should be tailored to the characteristics of each region to be more effective in addressing stunting in Indonesia.
Integrating Support Vector Regression and Kriging in Spatial Interpolation of Statistical Seismicity Parameters Sirodj, Dwi Agustin Nuriani; Aidi, Muhammad Nur; Sartono, Bagus; Syafitri, Utami Dyah; Pranata, Bayu
Indonesian Journal of Geography Vol 57, No 3 (2025): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.102153

Abstract

Spatial interpolation methods, such as Inverse Distance Weighting (IDW) and kriging, are commonly used in various fields. In Kriging method, semivariogram fitting is an important step, where empirical data are used to derive a theoretical model. However, when the known theoretical semivariogram model does not provide a satisfactory fit, the bias in the estimated values is increased. To address this limitation, Support Vector Regression (SVR) can be used to model the empirical semivariogram with a machine-learning method. This method has been applied in ordinary kriging interpolation for semivariogram fitting to estimate parameters related to the potential occurrence of earthquake. Specifically, the calculated parameters, based on the Gutenberg-Richter law, include the seismic activity (a-value) and rock fragility (b-value) in the Sumatera region. The results showed that SVR can model the empirical semivariogram better than the theoretical. The integration of SVR-Ordinary Kriging provides the best performance compared to other methods, such as IDW, with the smallest RMSEP values for both the b-value and a-value measuring 0.1378 and 0.7423, respectively. Aceh and Mentawai Islands tend to show low a and b values, suggesting that these areas are more vulnerable to earthquake with large magnitudes.
Identification of Earthquake Prone Zones in Sumatra using Density Based Spatial Clustering of Applications with Noise Dwi Agustin Nuriani Sirodj; Muhammad Nur Aidi; Bagus Sartono; Utami Dyah Syafitri; Bayu Pranata
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.v10i2.36120

Abstract

This study investigates the spatial distribution of earthquakes in Sumatra using the DBSCAN clustering algorithm applied to seismic data spanning 1 January 2000 to 31 December 2023. The analysis identified two distinct seismic clusters: one in the northern region (Aceh and North Sumatra) and another in the southern region (Lampung, Bengkulu, and West Sumatra), while several events in central areas were classified as noise. Cluster validity assessment confirmed that the identified groups are compact and well separated, reflecting meaningful seismotectonic segmentation. Statistical testing further revealed significant differences in earthquake depth and magnitude between the clusters, supporting the robustness of the findings. Notably, the southern cluster corresponds to the Mentawai Fault system, whereas the northern cluster aligns with the subduction zone and the Sumatran Fault. DBSCAN proved particularly effective in this context as it can capture clusters of arbitrary shapes, consistent with the complex geological structures governing seismicity in Sumatra.
THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL Nuramaliyah Nuramaliyah; Asep Saefuddin; Muhammad Nur Aidi
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i3.564

Abstract

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.
KAJIAN SIMULASI OVERDISPERSI PADA REGRESI POISSON DAN BINOMIAL NEGATIF TERBOBOTI GEOGRAFIS UNTUK DATA BALITA GIZI BURUK Puput Cahya Ambarwati; Indahwati Indahwati; Muhammad Nur Aidi
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i3.684

Abstract

One type of geographically weighted regression (GWR) that can be used to explain the relationship between the response variables in the form of count data and explanatory variables is the geographically weighted Poisson regression (GWPR). In the GWPR, there is an assumption that should be fulfilled called equidispersion, a condition where the variance equals the mean. If that condition is ignored, overdispersion will occur. Overdispersion is a condition when the variance is greater than the mean. The use of GWPR analysis in an overdispersion situation will produce a smaller standard error than it should be (underestimate). This may produce a significant test result leading to the rejection of the null hypothesis. One of the classic approaches commonly used to handle overdispersion in GWR is geographically weighted negative binomial regression (GWNBR). GWNBR is derived from a mixture of Poisson and Gamma distributions which is similar to the negative binomial distribution. Simulation data and real data were used in this study. The results showed that the application of GWPR on overdispersion data could increase the number of rejections of H0 or the number of p-values. The application of GWNBR on the East Java malnutrition toddler data in 2017 showed that the GWNBR model is better than GWPR based on the comparison of AIC, Pseudo R2, and RMSE.
KAJIAN VARIANCE MEAN RATIO PADA SIMULASI SEBARAN DATA BINOMIAL NEGATIF Choirun Nisa; Muhammad Nur Aidi; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 4 No 4 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i4.689

Abstract

The negative binomial distribution is one of the data collection counts that focuses on success and failure events. This study conducted a study of the distribution of negative binomial data to determine the characterization of the distribution based on the value of Variance Mean Ratio (VMR). Simulation data are generated based on negative binomial distribution with a combination of p and n parameters. The results of the VMR study on negative binomial distribution simulation data show that the VMR value will be smaller when the p-value is large and the VMR value is more stable as the sample size increases. Simulation data of negative binomial distribution when p≥0.5 begins to change data distribution to the distribution of Poisson and binomial. The calculation VMR value can be used as a reference for detecting patterns of data count distribution.
Co-Authors . Erfiani Agus Mohamad Soleh Ahmad Nur Rohman Aji Hamim Wigena Akbar Rizki Anang Kurnia Andi Setiawan Anik Djuraidah Anis Dyah Rahmawati ASEP SAEFUDDIN Bagus Sartono Bayu Pranata Bayu Pranata, Bayu Budi Susetyo Budi Utomo Choirun Nisa Cynthia Wulandari Dari Aulia Qital Dwi Agustin Nuriani Sirodj Dwi Agustin Nuriani Sirodj Efriwati Efriwati Erfiani Erfiani Faqih Udin dan Jono M. Munandar Meivita Amelia Fildzah Hanum Syazwina Fitrah Ernawati Hapsari, Nimas Ayu Haq, Irvanal Hari Wijayanto Haryanto, Sugi Hikmah, Zetil I Made Sumertajaya Indahwati Irene Muflikh Nadhiroh Itasia Dina Sulvianti Khairani, Fitri Khairil Anwar Notodiputro La Ode Abdul Rahman Lailan Syaufina LILIK BUDIPRASETYO Lulu Mahdiyah Sandjadirja Mahfuz Hudori Maulana Achiar, Anshari Luthfi Muhammad Nuruddin Prathama mutiah, siti Nadya Amelia Dewi Suryana Nuramaliyah, Nuramaliyah Nurfadilah, Khalilah Pramana, Setia Pranata, Ismail Puput Cahya Ambarwati Putu Gita Karlina Jayanti Rahma Anisa Retno Ariyanti Pratiwi Rifaldi, Destriana Aulia Rika Rachmawati Rizki Ananda Sandhi Imam Maulana Sugi Haryanto Sukisman Tjitrosemito Sulaeman, Sulthan Naufal Syella Sumampouw Tiara, Yesan Ulfa Afilia Shofa Utami Dyah Syafitri Widoretno, Widoretno Zulhijrah, Zulhijrah Zunita Sari