KLASIFIKASI RISIKO INFEKSI SALURAN PERNAPASAN AKUT MENGGUNAKAN ENSEMBLE SOFT VOTING BERBASIS REKAM MEDIS
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Abstract
This study proposes a risk classification model for Acute Respiratory Infections using a Soft Voting-based Ensemble Learning approach, which integrates prediction probabilities from the Random Forest and Extreme Gradient Boosting algorithms. The research utilized 456 patient medical record data from RSUD Latemmamala spanning January 2020 to December 2025. Comparative evaluation results show that the Random Forest model achieved an accuracy of 94.57%, Extreme Gradient Boosting reached 95.65%, and the Soft Voting Ensemble model delivered the best performance with an accuracy of 96.74%, precision of 96.97%, recall of 96.88%, and an F1-Score of 96.77%. Furthermore, the Soft Voting Ensemble model successfully achieved a perfect recall score for the Severe Acute Respiratory Infection category, ensuring that no high-risk patients went undetected. In conclusion, the Soft Voting Ensemble model serves as a reliable decision-support tool to assist medical professionals in triaging Acute Respiratory Infection patients quickly, objectively, and accurately.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Burhani, A. S., Faisal, M., Rachman, F. I., Darniati, Wahyuni, T., S. Kuba, M. S., & Gaffar, F. (2026). KLASIFIKASI RISIKO INFEKSI SALURAN PERNAPASAN AKUT MENGGUNAKAN ENSEMBLE SOFT VOTING BERBASIS REKAM MEDIS. Jurnal Informatika Progres, 18(2), 35-49. https://doi.org/10.56708/progres.v18i2.467
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[3] Trurly Santika, Salut Muhidin, Sugeng Budiharta, Kerrie A. Wilson, and Matthew J. Struebig, “Deterioration of respiratory health following changes to land cover and climate in Indonesia,” One Earth, vol. 6, pp. 290–302, Mar. 2023, doi: 10.1016/j.oneear.2023.02.012.
[4] M. Islam, K. Islam, K. Dalal, and M. D. Hossain Hawlader, “In-house environmental factors and childhood acute respiratory infections in under-five children: a hospital-based matched case-control study in Bangladesh,” BMC Pediatr., vol. 24, p. 38, Jan. 2024, doi: 10.1186/s12887-024-04525-4.
[5] R. A. Wulandari, S. Fauzia, and F. Kurniasari, “Investigations on the risk factors of Acute Respiratory Infections (ARIs) among under-five children in Depok City, Indonesia,” Ann. Ig. Med. Prev. E Comunita, vol. 36, no. 1, pp. 15–25, 2024, doi: 10.7416/ai.2023.2580.
[6] T. Z. Yehuala, B. M. Fente, S. M. Wubante, and N. M. Derseh, “Exploring machine learning algorithms to predict acute respiratory tract infection and identify its determinants among children under five in Sub-Saharan Africa,” Front. Pediatr., vol. 12, p. 1388820, Nov. 2024, doi: 10.3389/fped.2024.1388820.
[7] X. Yang, Y. Li, L. Liu, and Z. Zang, “Prediction of respiratory diseases based on random forest model,” Front. Public Health, vol. 13, p. 1537238, Feb. 2025, doi: 10.3389/fpubh.2025.1537238.
[8] C. C. B. Lirna, T. Trimono, and A. T. Damaliana, “Employee Voluntary Attrition Prediction At Pt.Xyz: Ensemble Machine Learning Approach With Soft Voting Classifier,” J. Tek. Inform. Jutif, vol. 5, no. 5, pp. 1231–1239, Oct. 2024, doi: 10.52436/1.jutif.2024.5.5.2007.
[9] A. Munandar, W. Maulana Baihaqi, and A. Nurhopipah, “A Soft Voting Ensemble Classifier to Improve Survival Rate Predictions of Cardiovascular Heart Failure Patients,” Ilk. J. Ilm., vol. 15, no. 2, pp. 344–352, Aug. 2023, doi: 10.33096/ilkom.v15i2.1632.344-352.
[10] A. Manconi, G. Armano, M. Gnocchi, and L. Milanesi, “A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19,” Appl. Sci., vol. 12, no. 15, p. 7554, Jul. 2022, doi: 10.3390/app12157554.
[11] M. Azad, T. Hasan, and M. Moshkov, “A novel ensemble learning method using majority based voting of multiple selective decision trees,” Computing, vol. 107, Dec. 2024, doi: 10.1007/s00607-024-01394-8.
[12] D. Dhiyaussalam, A. Yusuf, I. Wardiah, and N. L. Putri, “Predicting Respiratory Conditions Using Random Forest and XGBoost,” J. Inf. Syst. Inform., vol. 7, no. 2, pp. 1642–1657, Jun. 2025, doi: 10.51519/journalisi.v7i2.1124.
[13] W. Hong et al., “A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile,” Front. Cell. Infect. Microbiol., vol. 12, p. 819267, Apr. 2022, doi: 10.3389/fcimb.2022.819267.
[14] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2020, pp. 785–794. doi: 10.1145/2939672.2939785.
[15] P. Mahajan, S. Uddin, F. Hajati, and M. A. Moni, “Ensemble Learning for Disease Prediction: A Review,” Healthcare, vol. 11, p. 1808, Jun. 2023, doi: 10.3390/healthcare11121808.
[16] I. Markoulidakis and G. Markoulidakis, “Probabilistic Confusion Matrix: A Novel Method for Machine Learning Algorithm Generalized Performance Analysis,” Technologies, vol. 12, no. 11, 2024, doi: doi: 10.3390/technologies12070113.
[17] S. Sathyanarayanan and B. R. Tantri, “Confusion Matrix-Based Performance Evaluation Metrics,” Afr. J. Biomed. Res., vol. 27, no. 4S, pp. 4023–4031, Nov. 2024, doi: 10.53555/AJBR.v27i4S.4345.