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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal 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 Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 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
Departemen Statistika, IPB University
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 Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

 1 
Performance Comparison of Random Forest and XGBoost Optimized with Cuckoo Search Algorithm for Coconut Milk Adulteration Detection Using FTIR Spectroscopy I Gusti Ngurah, Sentana Putra; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
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.v8i2.7817

Abstract

Coconut milk has emerged as a strategic food commodity in the global tropical region, with market demand growing at 7.2% per annum since 2021. This increasing demand has led to sophisticated adulteration practices, including dilution with water. Such adulteration not only reduces the nutritional value but also poses serious health risks, including food poisoning and allergic reactions. This study developed an innovative detection method combining Fourier Transform Infrared (FTIR) spectroscopy with a sophisticated machine learning algorithm. We analyzed 719 coconut milk samples (wavelength range 2500-4000 nm) consisting of traditional market products and instant commercial products. This study aims to develop an FTIR-based coconut milk adulteration detection model by optimizing RF and XGBoost parameters using CSA and evaluating the comparative performance of the two models in identifying different types of adulterants. The spectral data underwent rigorous preprocessing using a combination of Standard Normal Variate (SNV) and Savitzky-Golay (SG) techniques to overcome the effects of noise and light scattering, which significantly improved feature extraction. The results show that CSA-optimized XGBoost achieves superior performance with 92% accuracy and 91% F1 score, outperforming Random Forest in all evaluation metrics. The model shows particular strength in precision (98%), indicating its outstanding ability to minimize false positives in adulteration detection. Stability tests through 30 experimental repetitions reveal that the combination of XGBoost+CSA maintains consistent performance with minimal variance, confirming its reliability for industrial applications. Comparative analysis shows that the combination of SNV+SG preprocessing improves the accuracy of the baseline model by 9-12%, while CSA optimization provides an additional performance improvement of 10-15%. This research makes significant contributions to food science and safety. This study demonstrates the effectiveness of CSA in optimizing spectroscopic models, achieving 19.5% higher precision. The combination of SNV+SG preprocessing improves the baseline accuracy by 9-12%, while CSA optimization provides an additional performance improvement of 10-15%. This study not only provides a rapid and non-destructive adulteration detection solution but also proves the effectiveness of the CSA approach in optimizing the spectroscopic model. These findings have important implications for strengthening food safety regulations and developing real-time quality control systems in the coconut milk industry.
Effect of Feature Normalization and Distance Metrics on K-Nearest Neighbors Performance for Diabetes Disease Classification Yusran, Muhammad; Sadik, Kusman; Soleh, Agus M; Suhaeni, Cici
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.v8i2.8012

Abstract

Diabetes is a global health issue with a steadily increasing prevalence each year. Early detection of the disease is an important step in preventing severe complications. The K-Nearest Neighbors (KNN) algorithm is often used in disease classification, but its performance is highly influenced by the choice of normalization method and distance metric used. This study aims to evaluate the effect of various normalization methods and distance metrics on the performance of the KNN algorithm in diabetes disease classification. The three normalization methods were employed: z-score normalization, min-max scaling, and median absolute deviation (MAD). In addition, the seven distance metrics were assessed: Euclidean, Manhattan, Chebyshev, Canberra, Hassanat, Lorentzian, and Clark. The dataset used is Pima Indians Diabetes which consists of 768 observations and 8 features. The data were split into 80% training data and 20% test data, and using 5-fold cross-validation to determine the optimal k value. The results show that the MAD-Canberra combination produces the highest overall accuracy, recall, and F1-score of 87.32%, 82.33%, and 81.94%, respectively. The highest precision was obtained from the Baseline-Hassanat combination at 86.96%, while the lowest performance was observed for the Z-Score-Chebyshev combination with F1-Score 58.02%. These results highlight that no single combination universally outperforms others, underscoring the need for empirical evaluation. Nonetheless, combining MAD normalization with metrics such as Canberra or Hassanat can serve as a strong starting point for developing KNN-based classification systems, especially in medical contexts that are sensitive to misclassification.
Analysis and Optimization of Rainfall Prediction in Makassar City Using Artificial Neural Networks Based on Data Augmentation, Regularization, and Bayesian Optimization Abdullah, Adib Roisilmi; Sadik, Kusman; Suhaeni, Cici; Saleh, Agus Muhammad
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.
Co-Authors . Erfiani . Indahwati A.Tuti Rumiati Aam Alamudi Abdullah, Adib Roisilmi Achmad Fauzan Agus Mohamad Soleh Ahmad Rifai Nasution 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 Aulya Permatasari Azka Ubaidillah Bagus Sartono Budi Susetyo Cici Suhaeni Cici Suhaeni Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Efriwati Efriwati Embay Rohaeti Eminita, Viarti EVITA PURNANINGRUM Fahira, Fani FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar Haikal, Husnul Aris Hari Wijayanto Hasnataeni, Yunia Hazan Azhari Zainuddin Hermawati, Neni I Gusti Ngurah, Sentana Putra I Made Sumertajaya I Wayan Mangku Indahwati Indahwati Indahwati Intan Arassah, Fradha Iqbal, Teuku Achmad Isnanda, Eriski Khairi A N Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnul Khotimah Kusni Rohani Rumahorbo Latifah, Leli Lili Puspita Rahayu Logananta Puja Kusuma M Soleh, Agus Mochamad Ridwan Mochamad Ridwan, Mochamad Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Yusran Mulianto Raharjo Naima Rakhsyanda Nisrina Az-Zahra, Putri Nur Khamidah NURADILLA, SITI Nusar Hajarisman Pangestika, Dhita Elsha Parwati Sofan, Parwati Purnama Sari Rifqi Aulya Rahman Rizki, Akbar Rizqi, Tasya Anisah ROCHYATI ROCHYATI Sahamony, Nur Fitriyani Saleh, Agus Muhammad Satriyo Wibowo Siregar, Jodi jhouranda Siti Raudlah Sitti Nurhaliza Soleh, Agus M Suhaeni, Cici 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