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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management FORUM STATISTIKA DAN KOMPUTASI 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 Limits: Journal of Mathematics 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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A COMPARISON OF LOGISTIC REGRESSION AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) ON COVID-19 DATA IN WEST SUMATRA Haq, Irvanal; Aidi, Muhammad Nur; Kurnia, Anang; Efriwati, Efriwati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1749-1760

Abstract

An understanding of factors that affect the recovery time from a disease is important for the community, medical staff, and also the government. This research analyzed factors that affect the recovery time of Covid-19 sufferers in West Sumatra. In addition, the consumption of a herbal made from Sungkai leaves, which is believed by some people in West Sumatra to accelerate the healing from Covid-19, was also included in the analysis. The recovery time here was categorized into two classes (binary): 1 for within 2 weeks, and 0 for more than 2 weeks. The methods used were logistic regression and geographically weighted logistic regression (GWLR). GWLR provides estimates of parameters for each location. The data used in this study is Covid-19 data of 2021 taken from the Regional Research and Development Agency (Litbangda) of West Sumatra with a total of 764 observations collected from 19 regencies/cities in West Sumatra. The results showed that there was no difference between the logistic regression model and the GWLR models based on the values of AIC and the ratio of deviance and degrees of freedom (df). The addition of spatial factors through GWLR models did not provide additional information regarding the recovery of Covid-19 sufferers within 2 weeks or more than 2 weeks. The logistic regression model gives the result that, at significance level α = 10%, residence, vaccination status, and symptoms significantly affect the recovery time within 2 weeks or more for Covid-19 sufferers, while other variables, namely sex, age, Sungkai leaves consumption status, and ginger consumption status have no significant effects.
MULTILEVEL REGRESSIONS FOR MODELING MEAN SCORES OF NATIONAL EXAMINATIONS Nurfadilah, Khalilah; Aidi, Muhammad Nur; Notodiputro, Khairil A.; Susetyo, Budi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0323-0332

Abstract

National Exam known as UN score is the final evaluation to determine the achievement of national graduate competency standards in the school. The determinants of the achievement of the standards can’t be separated from the role of schools and local governments in which this regard is known as nested. In the field of statistics, this phenomenon can be described with a multilevel model, where level-1 is the school while level-2 is the district where the school is located. Several multilevel models are used to describe the phenomenon, the result shows that the two-level regression model without interaction is selected as the best model and the variables which affect the UN average scores significantly at level-1 are school status , the ratio between laboratories and students , while the variable at level-2 is expenditure per capita of district/city . From this study, that educational institutions' steps in achieving a graduation standard can be right on the target.
Identification of Earthquake Prone Zones in Sumatra using Density Based Spatial Clustering of Applications with Noise Sirodj, Dwi Agustin Nuriani; Aidi, Muhammad Nur; Sartono, Bagus; Syafitri, Utami Dyah; Pranata, Bayu
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): 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.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.
APPLICATION OF THE COKRIGING METHOD TO ESTIMATE IRON DEFICIENCY PREVALENCE BASED ON FERRITIN AND C-REACTIVE PROTEIN Mutiah, Siti; Aidi, Muhammad Nur; Saefuddin, Asep; Ernawati, Fitrah
Media Penelitian dan Pengembangan Kesehatan Vol. 35 No. 3 (2025): MEDIA PENELITIAN DAN PENGEMBANGAN KESEHATAN
Publisher : Poltekkes Kemenkes Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34011/jmp2k.v35i3.3167

Abstract

Analisis data spasial memiliki peranan penting dalam bidang kesehatan, khususnya ketika distribusi masalah kesehatan tidak merata di seluruh wilayah. Salah satunya adalah metode Cokriging, yang diterapkan untuk memprediksi prevalensi di daerah yang belum teramati, sekaligus mengatasi tantangan ketidaklengkapan data spasial akibat keterbatasan biaya, sumber daya, atau akses ke lokasi tertentu. Penelitian ini bertujuan untuk mengestimasi prevalensi kekurangan zat besi di Indonesia menggunakan metode Cokriging. Sebagai analisis lanjut dari data Riset Kesehatan Dasar (Riskesdas) 2018, penelitian ini menggunakan data dari 15.045 individu yang memiliki informasi kadar ferritin dan C-Reactive Protein (CRP), yang tersebar di 154 kabupaten/kota di empat pulau: Sumatera, Jawa, Kalimantan, dan Sulawesi. Ferritin digunakan sebagai variabel utama, sementara CRP sebagai variabel sekunder. Evaluasi model dilakukan dengan Leave-One-Out-Cross-Validation (LOOCV), dan akurasi model diukur menggunakan Mean Error (ME) dan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan prevalensi kekurangan zat besi bervariasi signifikan antar wilayah. Kabupaten Batang dan Minahasa Selatan teridentifikasi dalam kategori "tidak ada masalah kesehatan". Selain itu 274 kabupaten/kota di Indonesia berada pada kategori prevalensi ringan, seperti Kabupaten Berau, Gunung Mas, dan Bangkayang, sementara 132 kabupaten/kota tercatat dengan prevalensi sedang seperti Kabupaten Sidenreng Rappang, Tapanuli Tengah, dan Sukoharjo. Kabupaten Pare-pare terdeteksi pada prevalensi tinggi (≥40%), tingginya prevalensi di wilayah ini perlu dicermati lebih lanjut karena kemungkinan disebabkan oleh jumlah sampel yang sangat sedikit. Temuan ini menunjukkan bahwa sebagian besar kabupaten/kota di Indonesia tergolong dalam kategori prevalensi ringan hingga sedang. Gambaran ini dapat menjadi dasar penting dalam merancang kebijakan kesehatan terkait penanggulangan kekurangan zat besi di 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.
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.
Evaluasi Regresi Terklaster Fuzzy Spasial Simultan dengan Pendekatan Simulasi Hasanah, Siti; Aidi, Muhammad Nur; Djuraidah, Anik
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 3 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Spatial data refers to data that contains information related to the geographical characteristics of a region. As spatial data evolves into large-scale datasets, efficient analytical methods are required for processing the data. One such method suitable for analyzing large-scale spatial data is spatial fuzzy clustering. This method allows for the adjustment of cluster weights based on data likelihood, making it more capable of capturing the actual local variations present in spatial data. In this study, two types of spatial fuzzy clustering methods were evaluated through simulation: the method with a spatial penalty, Spatial Fuzzy Clustered Regression (SFCR), and the method without a spatial penalty, Fuzzy Geographically Weighted Clustering Regression (FGWCR). SFCR is a method that combines spatial clustering and regression modeling simultaneously, resulting in more efficient computation time. FGWCR produces clusters by considering both spatial proximity and attribute similarity, making it effective for spatial data analysis. The data were designed to form six clusters during the simulation process. The simulation results showed that the SFCR method was more capable of accurately capturing data variation and cluster distribution. The R² values for SFCR at a fuzziness degree of 2 and under weak, moderate, and strong spatial autocorrelation were 99.7%, 99.6%, and 99.5%, respectively, while the R² values for FGWCR were 98.5%, 98.6%, and 98.1%. Model performance was evaluated using RMSE, where lower RMSE values indicate better performance. The RMSE values for the SFCR method at a fuzziness degree of 2 and under weak, moderate, and strong spatial autocorrelation were 0.30, 0.289, and 0.298, respectively, while the RMSE values for the FGWCR method were 0.659, 0.541, and 0.551.
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 Budi Susetyo Budi Susetyo Budi Utomo Choirun Nisa Cynthia Wulandari 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 Qital, Dari Aulia Rahma Anisa Retno Ariyanti Pratiwi Rifaldi, Destriana Aulia Rika Rachmawati Rizki Ananda Sandhi Imam Maulana Siti Hasanah Sugi Haryanto Sukisman Tjitrosemito Sulaeman, Sulthan Naufal Syella Sumampouw Tiara, Yesan Ulfa Afilia Shofa Utami Dyah Syafitri Widoretno, Widoretno Zulhijrah, Zulhijrah Zunita Sari