Perbandingan Kinerja Naïve Bayes, SVM, dan Random Forest dalam Klasifikasi Risiko Kehamilan
-
Reva Ekalia
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
- Dyah Ayu Megawaty Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia (*) Corresponding Author
Abstract
Classifying pregnancy risk levels is a crucial aspect in supporting early detection of potential complications in pregnant women. However, most previous studies have focused on a single algorithm and relied solely on accuracy metrics, thus failing to provide a comprehensive picture of model performance in multiclass classification. Furthermore, performance comparisons between algorithms using more comprehensive evaluation approaches are still limited. This study aims to analyze and compare the performance of the Naïve Bayes, Support Vector Machine (SVM), and Random Forest algorithms in classifying pregnancy risk levels using the Maternal Health Risk Dataset from the UCI Machine Learning Repository, which consists of 1,014 data sets with six maternal health attributes. The methods used include data preprocessing, hyperparameter optimization using GridSearchCV, and model evaluation using Stratified K-Fold Cross Validation with k = 10. Model performance was measured using accuracy, precision, recall, and F1-score metrics to provide a more comprehensive evaluation. The results showed that the Random Forest algorithm had the best performance with an accuracy value of 0.8629, precision of 0.8704, recall of 0.8629, and F1-score of 0.8635, followed by SVM and Naïve Bayes. The superiority of Random Forest is due to its ability to combine several decision trees and capture non-linear relationships between features, resulting in more accurate and stable predictions. Thus, Random Forest is recommended as the most effective method in pregnancy risk classification based on maternal health data.
Downloads
References
World Health Organization, “Trends in maternal mortality 2000–2020,” Geneva, Feb. 2023. [Online]. Available: https://www.who.int/publications/i/item/9789240068759
Kementerian Kesehatan Republik Indonesia, “Profil Kesehatan Indonesia 2023,” Jakarta, 2023. [Online]. Available: https://www.kemkes.go.id/id/profil-kesehatan-indonesia-2023
I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput. Sci., vol. 2, no. 1, pp. 1–21, 2021, doi: 10.1007/s42979-021-00592-x.
A. S. Nyamawe, M. M. Mjahiidi, N. E. Nnko, S. A. Diwani, G. G. Minja, and K. Malyango, Practical Machine Learning: A Beginner’s Guide with Ethical Insights. Boca Raton: CRC Press, 2025. doi: 10.1201/9781003486817.
M. Ahmed, “Maternal Health Risk Dataset,” UCI Machine Learning Repository. Accessed: Apr. 14, 2026. [Online]. Available: https://archive.ics.uci.edu/dataset/451/maternal+health+risk
J. J. Purnama, N. K. Hikmawati, and S. Rahayu, “Analisis algoritma klasifikasi untuk mengidentifikasi potensi risiko kesehatan ibu hamil,” Journal of Applied Computer Science and Technology, vol. 5, no. 1, pp. 120–127, 2024, doi: 10.52158/jacost.v5i1.809.
A. Kurniawan, “Perbandingan optimasi algoritma klasifikasi decision tree, naive bayes, dan knn menggunakan optimize parameter grid pada tingkat risiko ibu hamil,” Faktor Exacta, vol. 18, no. 2, p. 111, 2025, doi: 10.30998/faktorexacta.v18i2.28051.
M. Rosyid and Subektiningsih, “Klasifikasi tingkat risiko kesehatan ibu hamil menggunakan algoritma support vector machine,” Indonesian Journal of Computer Science Attribution, vol. 12, no. 5, pp. 2798–2807, 2023, doi: 10.33022/ijcs.v12i5.3372.
N. R. Muntiari, K. H. Hanif, Syamsiah, and Rokaya, “Klasifikasi penyakit preekslamsia pada ibu hamil menggunakan perbandingan algoritma machine learning,” Jurnal Khatulistiwa Informatika, vol. 13, no. 2, pp. 96–102, 2025, doi: 10.31294/jki.v13i2.11148.
L. Vasudevan et al., “Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review,” J. Med. Internet Res., vol. 27, p. e68225, 2025, doi: 10.2196/68225.
O. Rainio, J. Teuho, and R. Klén, “Evaluation metrics and statistical tests for machine learning,” Sci. Rep., vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-56706-x.
S. Sathyanarayanan and B. R. Tantri, “Confusion matrix-based performance evaluation metrics,” African Journal of Biomedical Research, vol. 27, no. 4s, pp. 4023–4031, 2024, doi: 10.53555/ajbr.v27i4s.4345.
E. Elgeldawi, A. Sayed, A. R. Galal, and A. M. Zaki, “Hyperparameter tuning for machine learning algorithms used for arabic sentiment analysis,” Informatics, vol. 8, no. 4, 2021, doi: 10.3390/informatics8040079.
I. Dwita, S. Tarigan, R. Habibi, R. Nuraini, and S. Fatonah, “Evaluasi algoritma klasifikasi machine learning kategori nilai akhir tunjangan kinerja pegawai,” Jurnal Sistem Cerdas, vol. 6, no. 3, pp. 251–261, 2023, doi: 10.37396/jsc.v6i3.246.
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, 2022, doi: 10.1016/j.gltp.2022.04.020.
S. S. Al Mashrafi, L. Tafakori, and M. Abdollahian, “Predicting maternal risk level using machine learning models,” BMC Pregnancy Childbirth, vol. 24, no. 1, 2024, doi: 10.1186/s12884-024-07030-9.
S. S. Berutu, H. Budiati, J. Jatmika, and F. Gulo, “Data preprocessing approach for machine learning-based sentiment classification,” JURNAL INFOTEL, vol. 15, no. 4, pp. 317–325, 2023, doi: 10.20895/infotel.v15i4.1030.
H. J. Park, Y. S. Koo, H. Y. Yang, Y. S. Han, and C. S. Nam, “Study on Data Preprocessing for Machine Learning Based on Semiconductor Manufacturing Processes,” Sensors, vol. 24, no. 17, 2024, doi: 10.3390/s24175461.
R. Syahri and D. Puspita, “Classification of outstanding students using support vector machine (SVM) based on data mining,” Journal of Information Technology and Engineering, vol. 9, no. 1, pp. 13–23, 2025, doi: 10.31289/jite.v9i1.13191.
R. Oktafiani, A. Hermawan, and D. Avianto, “Pengaruh komposisi split data terhadap performa klasifikasi penyakit kanker payudara menggunakan algoritma machine learning,” Jurnal Sains dan Informatika, vol. 9, no. 1, pp. 19–28, 2023, doi: 10.34128/jsi.v9i1.622.
R. Alfiyan, M. Hikmatyar, and S. S. Sundari, “Implementasi metode klasifikasi C4.5 penyebab faktor risiko penyakit stroke,” Indonesian Journal of Digital Business, vol. 4, no. 2, pp. 37–48, 2024, doi: 10.17509/ijdb.v4i2.69221.
P. J. B. Pajila, B. G. Sheena, and A. Gayathri, “A comprehensive survey on naive bayes algorithm: advantages, limitations and applications,” in Proc. International Conference on Smart Electronics and Communication (ICOSEC), 2023, pp. 1227–1233. doi: 10.1109/ICOSEC58147.2023.10276274.
R. Guido, S. Ferrisi, D. Lofaro, and D. Conforti, “An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review,” Information (Switzerland), vol. 15, no. 4, 2024, doi: 10.3390/info15040235.
R. Nanda, E. Haerani, S. K. Gusti, and S. Ramadhani, “Klasifikasi berita menggunakan metode support vector machine,” Jurnal Nasional Komputasi dan Teknologi Informasi, vol. 5, no. 2, pp. 269–278, 2022, doi: 10.32672/jnkti.v5i2.4193.
R. Ridwan, H. H. Handayani, S. A. P. Lestari, and Y. Cahyana, “Evaluasi kinerja algoritma random forest dan gradient boosting untuk klasifikasi penyakit jantung,” Jurnal Komtika (Komputasi dan Informatika), vol. 9, no. 1, pp. 112–124, 2025, doi: 10.31603/komtika.v9i1.13450.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Kinerja Naïve Bayes, SVM, dan Random Forest dalam Klasifikasi Risiko Kehamilan
Pages: 198-206
Copyright (c) 2026 Reva Ekalia, Dyah Ayu Megawaty
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).
