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All Journal Parameter: Jurnal Matematika, Statistika dan Terapannya
Kusman Sadik
IPB University, Indonesia
Mathematics

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Improving Land Use Classification Accuracy Using Zonal Statistics And Supervised Machine Learning Gede Awantara; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp181-194

Abstract

This study aims to improve land use classification accuracy by integrating zonal statistics with supervised machine learning using Sentinel-2 imagery. Two classification models were developed: Model A based on single-pixel values and Model B using aggregated zonal statistics derived from polygon shapefile data. Two algorithms, Random Forest and Classification and Regression Trees (CART), were implemented and evaluated through 5-fold cross validation. The results show that Model B consistently outperformed Model A, with the best performance achieved by Random Forest Model B, reaching an overall accuracy of 73.74% and a kappa coefficient of 0.5999. Class-wise evaluation based on F1-score revealed strong performance in dominant classes such as settlement, water bodies, and rice fields, while underrepresented classes like cropland and shrubland were more difficult to classify due to class imbalance. These findings highlight the effectiveness of zonal statistics in producing more representative training features and improving model stability and accuracy in land use classification tasks.
Bayesian Hierarchical Lognormal Modeling of Dengue Incidence with Area-Specific Temporal Effects Erwan Setiawan; Anang Kurnia; Kusman Sadik
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp139-152

Abstract

This study presents the development and validation of a Bayesian hierarchical model to estimate the incidence rate of dengue fever (DF) in West Java, Indonesia. Bayesian hierarchical models offer powerful tools for handling uncertainty and regional heterogeneity, yet their implementation remains challenging—especially in complex datasets with multilevel structures. The proposed model incorporates both random intercepts (for regencies/cities) and random slopes (for year), with various prior distribution scenarios tested to ensure robustness. Among the tested predictors, population density was found to significantly influence DF incidence. Model performance evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) yielded values of 31.26 and 48.77, respectively, indicating good predictive accuracy. This research highlights the effectiveness of hierarchical Bayesian modeling for epidemiological analysis and contributes to more targeted public health strategies in dengue-endemic regions
Co-Authors Agus Mohamad Soleh Anang Kurnia Cici Suhaeni Erwan Setiawan Gede Awantara