Penerapan Algoritma Navi Bayes dalam Memprediksi Tingkat Resiko Penyakit Stroke Menggunakan Data Kesehatan
-
Angel Ariski Simatupang
*
Universitas HKBP Nommensen, Kota Pematangsiantar,
Indonesia
(*) Corresponding Author
Abstract
Stroke is a non-communicable disease and one of the leading causes of death and long-term disability worldwide, making early detection essential through intelligent computational systems that can assist healthcare professionals in clinical decision-making. The main challenge addressed in this study is the complexity of multifactorial stroke risk factors and the imbalance of medical record datasets, which may affect the performance of classification models. This study implements the Gaussian Naive Bayes algorithm as the classification method, supported by comprehensive data preprocessing stages, including mean value imputation for missing data, label encoding to transform categorical variables into numerical values, Min-Max Scaling normalization, and classification modeling using RapidMiner. The objective of this research is to accurately predict the level of stroke risk based on patients' medical records in order to support clinical decision support systems for preventive healthcare and early intervention. The experimental results demonstrate that the proposed model achieved an overall accuracy of 90.70%, a precision of 72.08%, a recall of 78.89%, and an F1-score of 75.33%. Furthermore, the model successfully classified posterior probabilities into hierarchical risk categories, providing more interpretable information for identifying high-risk patients and supporting more effective and efficient clinical decision-making processes in preventive stroke management.
References
S. Helmiyah and R. Pramestiawan, “Analisis Komparatif Algoritma Machine Learning dengan Metrik Akurasi, Presisi, Recall, dan F1-Score pada Dataset Kacang Kering,” Jurnal Ilmu Komputer dan Teknologi, vol. 6, no. 3, pp. 152–159, Oct. 2025, doi: 10.35960/ikomti.v6i3.2031.
“Referensi Stroke 2”.
Y. Oktarina and S. Mulyani, “Edukasi Kesehatan Penyakit Stroke Pada Lansia,” 2020.
A. Faturohman, D. Anggreani, and R. Yusliana Bakt, “Model Deep Learning Berbasis Convolutional Neural Network Untuk Identifikasi Stroke Iskemik Pada Citra CT Scan,” Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), vol. 5, no. 2, pp. 290–297, Aug. 2025, doi: 10.55382/jurnalpustakaai.v5i2.1150.
L. Pasiolo et al., “Penerapan Teknik Smote Pada Klasifikasi Penyakit Stroke Dengan Algoritma Support Vector Machine,” 2025. [Online]. Available: https://www.kaggle.com/fedesoriano/stroke-prediction-dataset.
A. Dwi et al., “Faktor Prediktor Terjadinya Stroke Iskemik: Tinjauan Literatur,” Journal of Medical and Therapeutical Sciences, vol. 1, no. 2, pp. 103–111, 2026, doi: 10.37253/jmts.v1i2.12324.
M. Yudhi Putra and D. Ismiyana Putri, “Pemanfaatan Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Klasifikasi Jurusan Siswa Kelas XI.”
M. K. Suryadewiansyah, T. Endra, and E. Tju, “Jurnal Nasional Teknologi dan Sistem Informasi Naïve Bayes dan Confusion Matrix untuk Efisiensi Analisa Intrusion Detection System Alert”, doi: 10.25077/TEKNOSI.v8i2.2022.081-088.
“Referensi Naïve Bayes2”.
L. Pasiolo et al., “Penerapan Teknik Smote Pada Klasifikasi Penyakit Stroke Dengan Algoritma Support Vector Machine,” 2025. [Online]. Available: https://www.kaggle.com/fedesoriano/stroke-prediction-dataset.
A. Rahma, S. Nisa’, H. Nugroho, and G. E. Yuliastuti, “SNESTIK Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika Implementasi Metode Naïve Bayes untuk Diagnosis Penyakit Stroke”, doi: 10.31284/p.snestik.2023.4307.
A. Byna and M. Basit, “Penerapan Metode Adaboost Untuk Mengoptimasi Prediksi Penyakit Stroke Dengan Algoritma Naïve Bayes,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 9, no. 3, pp. 407–411, Nov. 2020, doi: 10.32736/sisfokom.v9i3.1023.
A. F. Riany and G. Testiana, “Penerapan Data Mining untuk Klasifikasi Penyakit Stroke Menggunakan Algoritma Naïve Bayes,” Jurnal SAINTEKOM, vol. 13, no. 1, pp. 42–54, Mar. 2023, doi: 10.33020/saintekom.v13i1.352.
M. Nurkholifah, A. NofiarAm, F. Kurnia Oktorina, S. Amik Riau, and P. Kampar, “Analisa Penyakit Jantung Menggunakan Algoritma Naïve Bayes,” 2023. [Online]. Available: https://www.kaggle.com/datasets/zgeakyldz/heart-desease-data
Q. A’yuniyah et al., “Implementasi Algoritma Naïve Bayes Classifier (NBC) untuk Klasifikasi Penyakit Ginjal Kronik,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 4, no. 1, p. 72, Sep. 2022, doi: 10.30865/json.v4i1.4781.
F. Novaldy and A. Herliana, “Penerapan Pso Pada Naïve Bayes Untuk Prediksi Harapan Hidup Pasien Gagal Jantung,” Jurnal Responsif, vol. 3, no. 1, pp. 37–43, 2021, [Online]. Available: http://ejurnal.ars.ac.id/index.php/jti
A. Tangkelayuk and E. Mailoa, “Klasifikasi Kualitas Air Menggunakan Metode KNN, Naïve Bayes Dan Decision Tree,” vol. 9, no. 2, pp. 1109–1119, 2022, [Online]. Available: http://jurnal.mdp.ac.id
“Haris Metode Naïve Bayes Untuk Memprediksi Penyakit Stroke.”
A. F. Riany and G. Testiana, “Penerapan Data Mining untuk Klasifikasi Penyakit Stroke Menggunakan Algoritma Naïve Bayes,” Jurnal SAINTEKOM, vol. 13, no. 1, pp. 42–54, Mar. 2023, doi: 10.33020/saintekom.v13i1.352.
M. Nurkholifah, A. NofiarAm, F. Kurnia Oktorina, S. Amik Riau, and P. Kampar, “Analisa Penyakit Jantung Menggunakan Algoritma Naïve Bayes,” 2023. [Online]. Available: https://www.kaggle.com/datasets/zgeakyldz/heart-desease-data
Q. A’yuniyah et al., “Implementasi Algoritma Naïve Bayes Classifier (NBC) untuk Klasifikasi Penyakit Ginjal Kronik,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 4, no. 1, p. 72, Sep. 2022, doi: 10.30865/json.v4i1.4781.
F. Novaldy and A. Herliana, “Penerapan Pso Pada Naïve Bayes Untuk Prediksi Harapan Hidup Pasien Gagal Jantung,” Jurnal Responsif, vol. 3, no. 1, pp. 37–43, 2021, [Online]. Available: http://ejurnal.ars.ac.id/index.php/jti
A. Tangkelayuk and E. Mailoa, “Klasifikasi Kualitas Air Menggunakan Metode KNN, Naïve Bayes Dan Decision Tree,” vol. 9, no. 2, pp. 1109–1119, 2022, [Online]. Available: http://jurnal.mdp.ac.id
“Haris Metode Naïve Bayes Untuk Memprediksi Penyakit Stroke.”
A. F. Riany and G. Testiana, “Penerapan Data Mining untuk Klasifikasi Penyakit Stroke Menggunakan Algoritma Naïve Bayes,” Jurnal SAINTEKOM, vol. 13, no. 1, pp. 42–54, Mar. 2023, doi: 10.33020/saintekom.v13i1.352.
Q. A’yuniyah et al., “Implementasi Algoritma Naïve Bayes Classifier (NBC) untuk Klasifikasi Penyakit Ginjal Kronik,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 4, no. 1, p. 72, Sep. 2022, doi: 10.30865/json.v4i1.4781.
M. Nurkholifah, A. NofiarAm, F. Kurnia Oktorina, S. Amik Riau, and P. Kampar, “Analisa Penyakit Jantung Menggunakan Algoritma Naïve Bayes,” 2023. [Online]. Available: https://www.kaggle.com/datasets/zgeakyldz/heart-desease-data
F. Novaldy and A. Herliana, “Penerapan Pso Pada Naïve Bayes Untuk Prediksi Harapan Hidup Pasien Gagal Jantung,” Jurnal Responsif, vol. 3, no. 1, pp. 37–43, 2021, [Online]. Available: http://ejurnal.ars.ac.id/index.php/jti
A. Tangkelayuk and E. Mailoa, “Klasifikasi Kualitas Air Menggunakan Metode KNN, Naïve Bayes Dan Decision Tree,” vol. 9, no. 2, pp. 1109–1119, 2022, [Online]. Available: http://jurnal.mdp.ac.id
R. Mia et al., “Exploring Machine Learning for Predicting Cerebral Stroke: A Study in Discovery,” Electronics (Switzerland), vol. 13, no. 4, Feb. 2024, doi: 10.3390/electronics13040686.
D. S. Jodas, L. A. Passos, A. Adeel, and J. P. Papa, “PL-kNN: A Python-based implementation of a parameterless k-Nearest Neighbors classifier [Formula presented],” Software Impacts, vol. 15, Mar. 2023, doi: 10.1016/j.simpa.2022.100459.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Algoritma Navi Bayes dalam Memprediksi Tingkat Resiko Penyakit Stroke Menggunakan Data Kesehatan
Pages: 126 - 133
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).