Analisis Kinerja Model Support Vector Machine dalam Prediksi Kasus HIV di Indonesia Berdasarkan Data Time Series
- Muhammad Al-Ghifari Erza Universitas Mercu Buana Yogyakarta, Yogyakarta, Indonesia
-
Putri Taqwa Prasetyaningrum
*
Universitas Mercu Buana Yogyakarta, Yogyakarta,
Indonesia
(*) Corresponding Author
Abstract
Accurate predictions of HIV cases are crucial in efforts to control the epidemic effectively in Indonesia. As the number of cases and the complexity of transmission factors increase, machine learning-based prediction methods are becoming increasingly relevant. This study analyzes the performance of the Support Vector Machine (SVM) model in forecasting the number of HIV cases in Indonesia using time series data from 2012 to 2024. The CRISP-DM methodology is used as the framework for the analysis process, starting from business understanding to model deployment. The dataset used includes secondary data from the Ministry of Health, such as SIHA, national surveillance, and reports from the Directorate General of Disease Prevention and Control (Ditjen P2P). The SVM model is selected due to its ability to handle non-linear data and limited data sizes, as well as its resilience to overfitting. Model evaluation is performed using MAE, RMSE, and MAPE metrics. The results of the study show that the SVR model with an RBF kernel provides good prediction accuracy, with MAE values of 691.34, RMSE of 823.11, and MAPE of 13% on the test data. Therefore, SVM can be an effective tool to support data-driven decision-making in HIV control efforts in Indonesia.
Downloads
References
E. Widiastuti, A. Ika Fibriana, “Kejadian HIV/AIDS di Kota Semarang Tahun 2021,” HIGEIA (Journal of Public Health Research and Development), vol 6, no 4, 2022, doi: 10.15294/higeia/v6i4/57060.
H. Wiguna, Y. Nugraha, F. Rizka R, A. Andika, J. I. Kanggrawan, and A. L. Suherman, “Kebijakan Berbasis Data: Analisis dan Prediksi Penyebaran COVID-19 di Jakarta dengan Metode Autoregressive Integrated Moving Average (ARIMA),” Jurnal Sistem Cerdas, vol. 3, no. 2, pp. 74–83, Aug. 2020, doi: 10.37396/jsc.v3i2.76.
W. Gunawan, Y. Devianto, and A. P. Sari, “Imbalanced Data NearMiss for Comparison of SVM and Naive Bayes Algorithms,” Computer Engineering and Applications Journal, vol. 13, no. 03, pp. 34–43, Oct. 2024, doi: 10.18495/comengapp.v13i03.485.
N. Rusmilawati and P. T. Prasetyaningrum, “Penerapan Data Mining Dalam Prediksi Hasil Produksi Kelapa Sawit PT Borneo Ketapang Indah Menggunaka Metode Linier Regression,” Journal of Information System and Artificial Intelligence (JISAI), vol. 1, no. 2, pp. 1–7, 2021, doi: https://doi.org/10.46338/jisai.v1i2.33.
H. Maika, Manajemen Epidemiologi, 1st ed. Padang: GET PRESS INDONESIA, 2024. [Online]. Available: https://www.researchgate.net/publication/387512413
M. L. Bovbjerg, Foundations of Epidemiology. Oregon State Open Educational Resources, 2020.
W. Wahyuningsih and P. T. Prasetyaningrum, “Enhancing Sales Determination for Coffee Shop Packages through Associated Data Mining: Leveraging the FP-Growth Algorithm,” Journal of Information Systems and Informatics, vol. 5, no. 2, pp. 758–770, May 2023, doi: 10.51519/journalisi.v5i2.500.
M. Oktafani and P. T. Prasetyaningrum, “Implementasi Support Vector Machine Untuk Analisis Sentimen Komentar Aplikasi Tanda Tangan Digital,” Jurnal Sistem Informasi dan Bisnis Cerdas, vol. 15, no. 1, Mar. 2022, doi: 10.33005/sibc.v15i1.2697.
A. Kloska, A. Harmoza, S. M. Kloska, T. Marciniak, and I. Sadowska-Krawczenko, “Predicting preterm birth using machine learning methods,” Sci Rep, vol. 15, no. 1, p. 5683, Feb. 2025, doi: 10.1038/s41598-025-89905-1.
N. Shahira Pisal, S. Abdul-Rahman, M. Hanafiah, and S. I. Kamarudin, “Prediction Of Life Expectancy For Asian Population Using Machine Learning Algorithms,” Malaysian Journal of Computing, vol. 7, no. 2, pp. 1150–1161, 2022, doi: 10.24191/mjoc.v7i2.18218.
P. T. Prasetyaningrum, P. Purwanto, and A. F. Rochim, “Enhancing User Engagement in Mobile Banking Through Personalized Gamification: A Cognitive Evaluation Theory Approach,” International Journal of Intelligent Engineering and Systems, vol. 17, no. 4, pp. 788–807, 2024, doi: 10.22266/IJIES2024.0831.60.
S. U. Nisa, A. Mahmood, F. S. Ujager, and M. Malik, “HIV/AIDS predictive model using random forest based on socio-demographical, biological and behavioral data,” Egyptian Informatics Journal, vol. 24, no. 1, pp. 107–115, Mar. 2023, doi: 10.1016/j.eij.2022.12.005.
A. Mustika Rani and N. Hendrastuty, “Perbandingan Algoritma NBC Dan SVM Untuk Melakukan Analisis Sentimen Terhadap PP NO.82 Tahun 2021,” Technology and Science (BITS), vol. 6, no. 4, 2025, doi: 10.47065/bits.v6i4.6496.
R. N. Azizah and U. Indahyanti, “Analysis of the Predicted Number of HIV/AIDS Spreads in Sidoarjo Regency using Multiple Linear Regression Method,” Advances in Cancer Science, vol. 1, no. 1, p. 11, Apr. 2024, doi: 10.47134/acsc.v1i1.3.
M. M. Da Costa, “Sistem Pakar Mendeteksi Penyakit Hiv/Aids Di Ntt Dengan Metode Support Vector Regression (SVR),” Prosiding Seminar Nasional SEMMAU 2019, vol. tidak tersedia, no. tidak tersedia, pp. 856–862, 2022, doi: http://dx.doi.org/10.33364/algoritma/v.19-2.1168.
S. S. and M. Elkanzi, “A Review on the Role of Machine Learning in Predicting the Spread of Infectious Diseases,” Metaheuristic Optimization Review, vol. 2, no. 1, pp. 14–27, 2024, doi: 10.54216/MOR.020102.
D. Mesafint Belete, “Wrapper Based Feature Selection Techniques On EDHS-HIV/AIDS Dataset,” European Journal of Molecular & Clinical Medicine, vol. 7, no. 8, pp. 2642–2657, 2020, doi: https://doi.org/10.31838/ejmcm.07.08.274.
A. Abade et al., “A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-69580-4.
A. K. Sah et al., “Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes,” Life, vol. 15, no. 5, p. 745, May 2025, doi: 10.3390/life15050745.
D. Tang et al., “Study on the prediction performance of AIDS monthly incidence in Xinjiang based on time series and deep learning models,” BMC Public Health, vol. 25, no. 1, p. 780, Feb. 2025, doi: 10.1186/s12889-025-21982-3.
M. Jordan, J. Kleinberg, and B. Schölkopf, Pattern Recognition and Machine Learning. Springer Science, 2006.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Kinerja Model Support Vector Machine dalam Prediksi Kasus HIV di Indonesia Berdasarkan Data Time Series
Pages: 465-475
Copyright (c) 2025 Muhammad Al-Ghifari Erza, Putri Taqwa Prasetyaningrum
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
