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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Gaussian IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Applied Statistics Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Zero : Jurnal Sains, Matematika, dan Terapan MIND (Multimedia Artificial Intelligent Networking Database) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Inferensi International Journal of Advances in Data and Information Systems InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Applied Data Sciences Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science Prosiding Seminar Nasional Official Statistics Jurnal Natural Eduvest - Journal of Universal Studies Xplore: Journal of Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Parameter: Jurnal Matematika, Statistika dan Terapannya Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education
Kusman Sadik
Program In Statistics And Data Science, School Of Data Science, Mathematics, And Informatics, IPB University, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry 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 Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Analysis and Optimization of Rainfall Prediction in Makassar City Using Artificial Neural Networks Based on Data Augmentation, Regularization, and Bayesian Optimization Adib Roisilmi Abdullah; Kusman Sadik; Cici Suhaeni; Agus Muhammad Saleh
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8304

Abstract

This study develops a robust and efficient rainfall prediction model using an Artificial Neural Network (ANN), significantly enhanced through integrated data augmentation, regularization, and Bayesian optimization techniques. We utilized a dataset of 118 monthly rainfall records from Makassar City, spanning 2014–2022, sourced from the Meteorological, Climatological, and Geophysical Agency (BMKG). To effectively capture inherent temporal patterns, lag features (specifically lag-1, lag-3, and lag-6 rainfall values) were meticulously constructed as input variables. Subsequently, Min-Max normalization was applied across all features, ensuring input consistency and optimizing the ANN's learning process. An initial manual grid search identified the most effective baseline ANN architecture, featuring four hidden layers ([128, 32, 16, 64] neurons), a tanh activation function, and a learning rate of 0.01. While the baseline ANN model achieved a commendable initial performance with an RMSE of 0.1608, comprehensive experiments revealed the superior benefits of a fully integrated approach. This advanced model, which synergistically combined data augmentation (to address data limitations and enhance generalization), regularization (to mitigate overfitting), and Bayesian optimization (for efficient hyperparameter tuning), demonstrated significantly improved generalization capabilities and enhanced model stability. This integrated model yielded an RMSE of 0.1861, an MSE of 0.0346, and an MAE of 0.1359. These compelling findings unequivocally underscore that integrated optimization strategies are crucial for developing more robust and reliable ANN-based rainfall prediction models, particularly for critical applications in climate-based time series forecasting.
Bayesian Spatial BYM CAR Model for Estimating the Relative Risk of Dengue Hemmorhagic Fever in Bandung Ananda Shafira; Asep Saefuddin; Kusman Sadik
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9272

Abstract

Dengue Hemorrhagic Fever (DHF) is an endemic disease whose transmission is influenced by spatial and environmental factors, including population density, altitude, household sanitation, and clean and healthy living behaviors. In 2022, the city of Bandung reported a high incidence of DHF cases, highlighting the need for spatial modeling to capture interdependencies among geographic regions. This study aims to examine the impact of different parameter settings in hyperprior distributions on the Besag-York-Mollie conditional autoregressive (BYM CAR) model, estimate the relative risk (RR) of DHF, and map district-level risk to support the identification of priority areas for targeted prevention. The BYM CAR model was employed within a Bayesian framework, and the posterior distributions were obtained using Markov Chain Monte Carlo (MCMC) with the Gibbs sampling algorithm. Five hyperprior scenarios based on the Inverse-Gamma distribution were compared to evaluate their influence on model performance. The results show that hyperprior selection substantially affects model outcomes, with the best model obtained when the prior for the structured spatial component was specified as Inverse-Gamma(0.1, 0.1), and the unstructured spatial component as Inverse-Gamma(1, 0.01). Gedebage, Arcamanik, and Rancasari districts were identifies as high-risk areas, while Babakan Ciparay and Bandung Kulon exhibited the lowest RR estimates. This spatial risk mapping offers insights for policymakers in formulating more targeted and efficient DHF prevention strategies.
Classification Performance of Stacking Ensemble with Meta-Model of Categorical Principal Component Logistic Regression on Food Insecurity Data Dhita Elsha Pangestika; Anwar Fitrianto; Kusman Sadik
Scientific Journal of Informatics Vol. 11 No. 4: Special Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15315

Abstract

Purpose: Stacking is one type of ensemble whose base-models use different algorithms. The classification results from its base-models are categorical and tend to be associated with each other. They then become input for the stacking meta-model. However, there are no currently definite rules for determining the classifier that becomes the meta-model in stacking. On the other hand, recent research has found that CATPCA-LR can work well on categorical predictor variables associated with each other. Therefore, this study focuses on the classification performance of the stacking algorithm with the CATPCA-LR meta-model. Methods: The study compared the classification performance stacking with CATPCA-LR meta-model to stacking with other meta-models (random forest, gradient boost, and logistic regression) and its base-models (random forest, gradient boost, extreme gradient boost, extra trees, light gradient boost). This research used food insecurity data from March 2022. Result: The stacking algorithm with the CATPCA-LR meta-model performs better insecurity data regarding sensitivity, balanced accuracy, F1-Score, and G-Means values. This model offers a sensitivity of 46.28%, a balanced accuracy of 59.82%, an F1-Score of 37.82%, and a G-Means of 58.26%. Meanwhile, regarding specificity values, the light gradient boost (LGB) algorithm gives the highest value compared to other algorithms. This model provides a specificity value of 88.40%. Generally, the stacking with the CATPCA-LR meta-model algorithm provides the best performance compared with other algorithms on food insecurity data. Novelty: This research has explored a stacking classification performance with CATPCA-LR as meta-model.
Co-Authors . Erfiani A.Tuti Rumiati Aam Alamudi ACHMAD FAUZAN Adib Roisilmi Abdullah Agus M Soleh Agus M Soleh Agus M. Soleh Agus Mohamad Soleh Agus Muhammad Saleh Ahmad Rifai Nasution Aji Hamim Wigena Aji Hamim Wigena Akbar Rizki Akbar Rizki Akbar Rizki Akmala Firdausi Amalia, Rahmatin Nur Anadra, Rahmi Ananda Shafira Anang Kurnia Andespa, Reyuli Andi Okta Fengki ASEP SAEFUDDIN Astari, Reka Agustia Astari, Reka Agustia Asysta Amalia Pasaribu Aulya Permatasari Azka Ubaidillah Bagus Sartono Baiq Dian Arianingsih Budi Susetyo Budi Susetyo Cici Suhaeni Cici Suhaeni Cici Suhaeni Dewi Zulyani Pomalingo Dhita Elsha Pangestika Dian Handayani Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Efriwati Efriwati Embay Rohaeti Eriski Isnanda Erwan Setiawan Evita Purnaningrum Fahira, Fani FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar Fradha Intan Arassah Freya, Wa Ode Rona Gede Awantara Gerry Alfa Dito Haikal, Husnul Aris Hari Wijayanto Hasnataeni, Yunia Hazan Azhari Zainuddin Hermawati, Neni I Gusti Ngurah Sentana Putra I Made Sumertajaya I Wayan Mangku Indahwati Iqbal, Teuku Achmad Jefita Resti Sari Khairi A N Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnul Khotimah Kusni Rohani Rumahorbo Latifah, Leli Lili Puspita Rahayu Logananta Puja Kusuma Logananta Puja Kusuma Lukmanul Hakim M Soleh, Agus Mochamad Ridwan Mochamad Ridwan, Mochamad Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Nur Aidi Muhammad Yusran Mulianto Raharjo Naima Rakhsyanda Naufalia Alfiryal Nisrina Az-Zahra, Putri Nur Khamidah Nurhidayah Nurhidayah Nusar Hajarisman Panji Lokajaya Arifa Parwati Sofan Purnama Sari Rifqi Aulya Rahman Rita Rahmawati Rizaldi Boer Rizki, Akbar Rizqi, Tasya Anisah ROCHYATI ROCHYATI Sabrina Adnin Kamila Sahamony, Nur Fitriyani Satriyo Wibowo Sentana Putra I Gusti Ngurah Siregar, Jodi jhouranda Siti Aisyah SITI NURADILLA Siti Raudlah Sitti Nurhaliza Soleh, Agus M Sri Kustiara Suhaeni, Cici Sundari, Marta Supriatin, Febriyani Eka Tendi Ferdian Diputra Titin Suhartini Titin Suhartini, Titin Tri Wahyuni Uswatun Hasanah Utami Dyah Syafitri Viarti Eminita Widhiyanti Nugraheni Yenni Angraini Yenni Kurniawati Yuli Eka Putri Zafira Fakhriyah