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All Journal FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika Jurnal Ilmu Dasar Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Disease Prevention and Public Health Journal JUITA : Jurnal Informatika Forum Geografi Desimal: Jurnal Matematika Jurnal Penelitian Pertanian Tanaman Pangan Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL METEOROLOGI DAN GEOFISIKA Jurnal Aplikasi Statistika & Komputasi Statistik Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Journal of Data Science and Its Applications ESTIMASI: Journal of Statistics and Its Application SRIWIJAYA JOURNAL OF ENVIRONMENT Xplore: Journal of Statistics Jurnal Meteorologi dan Geofisika Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications Limits: Journal of Mathematics and Its Applications
Anik Djuraidah
Department Of Statistics, IPB University, Jl. Lingkar Akademik Kampus IPB Dramaga, Bogor 16680, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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OPTIMIZING LANDSLIDE SUSCEPTIBILITY MAPPING IN CENTRAL SULAWESI WITH RECURSIVE FEATURE ELIMINATION AND RANDOM FOREST ALGORITHM Siregar, Indra Rivaldi; Djuraidah, Anik; Soleh, Agus Mohamad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1019-1034

Abstract

Landslides are among the most destructive natural hazards, causing severe casualties, economic losses, and environmental degradation. Central Sulawesi, characterized by active tectonics such as the Palu-Koro fault, is highly susceptible to landslides, as tragically demonstrated in 2018. Therefore, developing accurate landslide susceptibility maps is essential to support comprehensive landslide mitigation efforts in this region. While machine learning, particularly Random Forest (RF), has proven highly effective for landslide modeling, previous studies around Palu have often overlooked model simplification through feature selection and hyperparameter optimization. This study proposes an integrated approach combining RF with Recursive Feature Elimination (RFE) to reduce model complexity and enhance predictive accuracy. This research utilizes 498 landslide events with fifteen conditions, including topography, environment, geology, and anthropogenic influences. The RFE-RF model achieves superior classification performance, with accuracy, balanced accuracy, and F1-scores exceeding 0.81, outperforming the RF without RFE and Logistic Regression baselines. These findings underscore the urgent need to integrate feature selection methods such as RFE into landslide modeling frameworks to improve predictive accuracy. High accuracy enables government authorities and stakeholders to develop more targeted and effective mitigation priorities. Spatial analysis indicates that Donggala, Palu, and Sigi are the most critical areas requiring prioritized mitigation, with over 9% of their territories classified as highly susceptible. Feature importance analysis reveals that elevation, slope, and land cover are the most influential factors. This study suggests that mitigation efforts should focus on the hills and mountainous areas on both sides of the Palu Valley, with recommended strategies emphasizing land cover management practices, such as reforestation, to enhance slope stability and reduce landslide risk.
Spatial Analysis of the Number of BPJS Ketenagakerjaan Participants using Geographically Weighted Panel Regression Sofia, Ayu; Zul’aina, Restu Apriani; Djuraidah, Anik; Pitri, Rizka
Desimal: Jurnal Matematika Vol. 9 No. 1 (2026): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v9i1.30408

Abstract

Uneven participation in employment-based social protection remains a major challenge in decentralized labor markets where regional economic structures, labor informality, and institutional capacity vary substantially across locations. Conventional panel regression and standard spatial econometric models generally assume homogeneous relationships across regions, potentially obscuring localized determinants of participation behavior. This study examines spatially varying determinants of BPJS Ketenagakerjaan participation in South Kalimantan Province, Indonesia, using Geographically Weighted Panel Regression (GWPR) applied to panel data from thirteen districts and cities during 2018–2022. The GWPR approach is employed because it allows regression coefficients to vary across space and time, enabling the identification of spatial nonstationarity that cannot be captured by global panel models. The results reveal clear spatial heterogeneity in participation dynamics. The number of registered companies emerges as the most consistent determinant, showing statistically significant positive effects across all districts with coefficients ranging from 0.099 to 0.143. In contrast, informal worker income demonstrates localized negative effects in several districts (−0.104 to −0.088), suggesting substitution between informal earnings and participation in formal protection schemes. Average years of schooling shows strong positive effects in selected regions (0.554–0.699), indicating the importance of human capital in increasing social insurance awareness and participation. Model adequacy testing further confirms that the GWPR specification provides a better representation of spatial variation than the global panel model.
Evaluasi Regresi Terklaster Fuzzy Spasial Simultan dengan Pendekatan Simulasi Siti Hasanah; Muhammad Nur Aidi; Anik Djuraidah
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 | DOI: 10.12962/limits.v22i3.5425

Abstract

Data spasial merupakan data yang memuat informasi yang berkaitan dengan karakteristik geografis suatu wilayah. Perkembangan data spasial yang mengarah pada data berskala besar membutuhkan metode analisis yang efisien dalam proses pengolahannya. Salah satu metode analisis yang dapat digunakan untuk mengolah data spasial berskala besar adalah spatial fuzzy clustering. Metode ini memungkinkan adanya penyesuaian bobot kelompok berdasarkan kemungkinan data, sehingga lebih mampu menangkap variasi lokal yang sebenarnya terjadi dalam data spasial. Metode spatial fuzzy clustering dengan penalti spasial, Spatial Fuzzy Clustered Regression (SFCR) dan tanpa penalti spasial, Fuzzy Geographically Weighted Clustering Regression (FGWCR) dievaluasi melalui simulasi pada penelitian ini. SFCR merupakan metode yang menggabungkan klasterisasi spasial dan pembentukan persamaan regresi secara simultan, sehingga waktu komputasi menjadi lebih efisien. FGWCR menghasilkan klaster yang mempertimbangkan kedekatan spasial dan kesamaan atribut sehingga efektif digunakan pada data spasial. Data dirancang sehingga terdapat 6 klaster dalam proses simulasi. Hasil simulasi menunjukkan metode SFCR lebih mampu mencerminkan keragaman data dan pembagian klaster dengan akurat. Nilai untuk metode SFCR pada derajat fuzziness 2 dan autokorelasi spasial lemah, sedang, dan kuat berturut-turut yaitu 99.7%, 99.6%, dan 99.5%, sedangkan untuk metode FGWCR yaitu 98.5%, 98.6%, dan 98.1%. Kebaikan persamaan dievaluasi oleh nilai RMSE. Semakin kecil nilai RMSE maka persamaan yang dihasilkan semakin baik. Nilai RMSE untuk metode SFCR pada derajat fuzziness 2 dan autokorelasi spasial lemah, sedang, dan kuat berturut-turut yaitu 0.30, 0.289, dan 0.298, sedangkan untuk metode FGWCR yaitu 0.659, 0.541, dan 0.551. 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 . . Aunuddin Aam Alamudi Abqorunnisa, Farah Agus M. Soleh Agus Mohamad Soleh Agusta, Madania Tetiani Aji H Wigena Aji Hamim Wigena Aji Hamin Wigena Alfa Nugraha Pradana Alfa Nugraha Pradana Alfan, Tony Alwi Aliu, Muftih Anang Kurnia Anisa, Rahma Ardiansyah, Muhlis Aris Yaman Asep Andri Fauzi ASEP SAEFUDDIN Aunuddin Aunuddin Ayu Sofia Azizah Desiwari Bagus Sartono Banan Nabila Bimandra Djaafara Cici Suhaeni Cici Suheni Dani Al Mahkya Dewi Retno Sari Saputro, Dewi Retno Erfiani Erfiani Fauziah, Ghina Fitrianto, Anwar Hanifa Izzati Hardinsyah Haryanto, Sugi Herlina Hanum Herlina Hanum, Herlina I Made Sumertajaya I Wayan Mangku Ida Mariati Hutabarat Indahwati Intan Lukiswati Ira Yulita Ismah . Ismah, Ismah Itasia Dina Sulvianti Lismayani Usman Lusi Eka Afri Mely Amelia Miranti, Ita Miranti, Ita Mohamad Arif Pramarta Muhammad Nur Aidi Novi Hidayat Pusponegoro Oryza Sativa Pigitha, Nindi Pika Silvianti Pitri, Rizka Pranata, Ismail Putri Astrini, Yufan Putri Astrini Rahardiantoro, Septian Rahma Anisa Resti Cahyati Resty Fanny Retna Nurwulan Retno Ariyanti Pratiwi Retsi Firda Maulina Ristiyanti Tarida, Arna Rita Rahmawati Rizki, Akbar Sarah Fadhlia Sari, Mutia Dwi Permata Sarimah Sarimah Septemberini, Cintia Setiawan Setiawan Sinaga, Enny Keristiana Siregar, Indra Rivaldi Siti Hasanah Siti Nur Laila Sony Sunaryo Sugi Haryanto Syam, Ummul Auliyah Tarida, Arna Ristiyanti Tasya Meilania, Gusti Titin Agustin Utami Dyah Syafitri Winda Chairani Mastuti Yoga Primanda Zulkarnain, Rizky Zul’aina, Restu Apriani _ Aunuddin