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

doi:10.62951/ijcts.v2i3.285

Authors

Rosa Ratri Kusuma Hariningsih Sekolah Tinggi Ilmu Komputer Yos Sudarso
Diwahana Mutiara Candrasari Sekolah Tinggi Ilmu Komputer Yos Sudarso
Endang Setyawati Sekolah Tinggi Ilmu Komputer Yos Sudarso
Syamsu Wahidin Sekolah Tinggi Ilmu Komputer Yos Sudarso
Jevon Nataniel Putra Sekolah Tinggi Ilmu Komputer Yos Sudarso

DOI:

https://doi.org/10.62951/ijcts.v2i3.285

Keywords:

Dengue Fever, GIS, Machine Learning, Public Health Preparedness, Risk Mapping

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.

References

S. A. Kularatne and C. Dalugama, “Dengue infection: Global importance, immunopathology and management,” Clinical Medicine, Journal of the Royal College of Physicians of London, vol. 22, no. 1, pp. 9–13, Jan. 2022, doi: 10.7861/clinmed.2021-0791.

F. P. Rocha and M. Giesbrecht, “Machine learning algorithms for dengue risk assessment: a case study for São Luís do Maranhão,” Computational and Applied Mathematics, vol. 41, no. 8, Dec. 2022, doi: 10.1007/s40314-022-02101-z.

R. Indawati, L. Y. Hendrati, and S. Widati, “The Early Vigilance of Dengue Hemorrhagic Fever Outbreak in the Community,” Jurnal Kesehatan Masyarakat, vol. 16, no. 3, pp. 366–376, Mar. 2021, doi: 10.15294/kemas.v16i3.24114.

Z. A. Hadi and N. C. Dom, “Development of machine learning modelling and dengue risk mapping: A concept framework,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics, 2023. doi: 10.1088/1755-1315/1217/1/012038.

I. A. P. Salsabila, A. Santjaka, and N. Utomo, “DHF Endemicity and Aedes aegypti Larvae Density Mapping in West Purwokerto Community Health Center’s Working Area in 2023,” Jurnal Kesehatan Lingkungan Indonesia, vol. 23, no. 2, pp. 137–145, 2024, doi: 10.14710/jkli.23.2.137-145.

O. R. Pinontoan, O. J. Sumampouw, J. H. V. Ticoalu, J. E. Nelwan, E. C. Musa, and J. Sekeeon, “The variability of temperature, rainfall, humidity and prevalance of dengue fever in Manado City,” Bali Medical Journal, vol. 11, no. 1, pp. 81–86, 2022, doi: 10.15562/bmj.v11i1.2722.

M. Bari Antor et al., “A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer’s Disease,” J Healthc Eng, vol. 2021, 2021, doi: 10.1155/2021/9917919.

M. S. Rahman et al., “Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach,” One Health, vol. 13, Dec. 2021, doi: 10.1016/j.onehlt.2021.100358.

S. A. Arhin and A. Gatiba, “Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers,” Transportation Safety and Environment, vol. 2, no. 2, pp. 120–132, Jun. 2020, doi: 10.1093/tse/tdaa012.

N. Shahzad, X. Ding, and S. Abbas, “A Comparative Assessment of Machine Learning Models for Landslide Susceptibility Mapping in the Rugged Terrain of Northern Pakistan,” Applied Sciences (Switzerland), vol. 12, no. 5, Mar. 2022, doi: 10.3390/app12052280.

Z. Su, L. Xu, J. Xu, J. Li, and M. Huangfu, “SIG: Speaker Identification in Literature via Prompt-Based Generation,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 17, pp. 19035–19043, 2024, doi: 10.1609/aaai.v38i17.29870.

S. Bahri, “Pengembangan Model Prediksi Banjir Menggunakan Data Hidrologi dan Sistem Informasi Geografis (SIG),” WriteBox, vol. 1, no. 2, pp. 1–11, 2024, [Online]. Available: https://writebox.cloud/index.php/wb/article/view/53

B. Charbuty and A. Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20–28, Mar. 2021, doi: 10.38094/jastt20165.

G. Gupta et al., “DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms,” Diagnostics, vol. 13, no. 6, Mar. 2023, doi: 10.3390/diagnostics13061093.

S. Fadli, M. Ashari, P. Studi Sistem Informasi, and S. Lombok, “JISA (Jurnal Informatika dan Sains) Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results,” 2021.

A. K. Abed, “Utilizing Artificial Intelligence in Cybersecurity : A Study of Neural Networks and Support Vector Machines,” vol. 2025, pp. 14–24, 2025.

A. Arista, “Comparison Decision Tree and Logistic Regression Machine Learning Classification Algorithms to determine Covid-19,” Sinkron, vol. 7, no. 1, pp. 59–65, Jan. 2022, doi: 10.33395/sinkron.v7i1.11243.

K. N. Khikmah, I. Indahwati, A. Fitrianto, E. Erfiani, and R. Amelia, “Backwards Stepwise Binary Logistic Regression for Determination Population Growth Rate Factor in Java Island,” Jambura Journal of Mathematics, vol. 4, no. 2, pp. 177–187, Jun. 2022, doi: 10.34312/jjom.v4i2.13529.

S. Bhatia and J. Malhotra, “Naïve bayes classifier for predicting the novel coronavirus,” in Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, Institute of Electrical and Electronics Engineers Inc., Feb. 2021, pp. 880–883. doi: 10.1109/ICICV50876.2021.9388410.

S. I. Fallo, “ABSTRACT SUPPORT VECTOR MACHINE, NA¨IVENA¨ NA¨IVE BAYES CLASSIFIER AND ORDINAL LOGISTIC REGRESSION IN WEATHER PREDICTION.” [Online]. Available: http://etd.repository.ugm.ac.id/

P. Bach, M. S. Kurz, V. Chernozhukov, M. Spindler, and S. Klaassen, “DoubleML: An Object-Oriented Implementation of Double Machine Learning in R,” J Stat Softw, vol. 108, no. 3, pp. 1–56, 2024, doi: 10.18637/jss.v108.i03.

R. Anagora, R. Taufiq, A. Dedi Jubaedi, R. Wirawan, and A. Syah Putra, “The Classification of Phishing Websites using Naive Bayes Classifier Algorithm,” International Journal Of Science, [Online]. Available: http://ijstm.inarah.co.id

T. Ige, C. Kiekintveld, A. Piplai, A. Waggler, O. Kolade, and B. H. Matti, “An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey,” 2024, [Online]. Available: http://arxiv.org/abs/2411.16751

A. T. Owolabi, K. Ayinde, J. I. Idowu, O. J. Oladapo, and A. F. Lukman, “A New Two-Parameter Estimator in the Linear Regression Model with Correlated Regressors,” J Stat Appl Probab, vol. 11, no. 2, pp. 499–512, May 2022, doi: 10.18576/jsap/110211.

M. W. A. Ashraf, A. R. Singh, A. Pandian, R. S. Rathore, M. Bajaj, and I. Zaitsev, “A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things,” Sci Rep, vol. 14, no. 1, pp. 1–19, 2024, doi: 10.1038/s41598-024-78976-1.