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All Journal JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA International Journal of Computer Technology and Science
Syamsu Wahidin
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Computer Science & IT Education

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Advanced Machine Learning for Comprehensive Mapping and Risk Analysis of Dengue Fever in Purwokerto to Support Public Health Preparedness Rosa Ratri Kusuma Hariningsih; Diwahana Mutiara Candrasari; Endang Setyawati; Syamsu Wahidin; Jevon Nataniel Putra
International Journal of Computer Technology and Science Vol. 2 No. 3 (2025): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v2i3.285

Abstract

Dengue Fever (DF) continues to be a major public health threat in Indonesia, especially in urban areas with high population density, such as Purwokerto City. This study aims to develop a predictive model to identify high-risk areas for DF outbreaks by integrating Machine Learning (ML) algorithms and Geographic Information Systems (GIS). The research utilizes historical dengue case data, meteorological parameters (rainfall, temperature, humidity), and population density as predictive variables. Three ML classification algorithms—Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM)—were implemented to develop risk prediction models. Extensive data preprocessing, feature selection, and spatial integration were applied to ensure model robustness. The results show that the SVM model outperformed other methods, achieving the highest accuracy, precision, recall, and F1-score in classifying dengue risk zones. Risk maps generated through GIS visualization successfully identify priority areas for targeted interventions. The novelty of this research lies in the combination of local epidemiological data, multi-algorithm comparison, and geospatial mapping to improve early warning systems for DF in Purwokerto. This integrated approach is expected to support more effective prevention strategies and enhance public health preparedness.
Analisis Perbandingan Naive Bayes, Regresi Logistik Biner, dan Support Vector Machine untuk Prediksi Kasus Demam Berdarah di Purwokerto Ratri Kusuma Hariningsih, Rosa; Diwahana Mutiara Candrasari; Endang Setyawati; Syamsu Wahidin; Jevon Nataniel Putra5
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 12 No. 3 (2025): Jurnal Derivat (Desember 2025)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/j.derivat.v12i3.8408

Abstract

Dengue Hemorrhagic Fever (DHF) remains a significant public health issue in Purwokerto, with the increasing number of cases influenced by environmental factors such as temperature, humidity, rainfall, and population density. Accurate and adaptive predictive methods are essential to anticipate the spread of DHF, one of which involves the application of machine learning algorithms. This study aims to compare the performance of three algorithms, namely Naïve Bayes, Binary Logistic Regression, and Support Vector Machine (SVM), in predicting DHF risk in Purwokerto. Secondary data were obtained from the Health Office, Meteorology Agency (BMKG), and Statistics Bureau (BPS), covering DHF case records and environmental factors. The analysis was conducted using a quantitative predictive approach, employing 5-Fold Cross Validation and evaluation metrics including accuracy, precision, recall, and F1-score. The results indicate that the SVM model demonstrated the highest performance with an accuracy of 82% and a high recall rate for the positive class, making it effective for DHF risk mapping. The Naïve Bayes model showed adequate sensitivity but lower precision, while the Binary Logistic Regression model produced the lowest overall performance. This study recommends SVM as the most effective algorithm to support early warning systems and risk mitigation for DHF based on environmental data in Purwokerto.
Co-Authors Diwahana Mutiara Candrasari Hermanto Endang Setyawati Jevon Nataniel Putra Jevon Nataniel Putra5 Ratri Kusuma Hariningsih, Rosa Rosa Ratri Kusuma Hariningsih