Analisis Perbandingan Algoritma Naïve Bayes dan Random Forest Dalam Klasifikasi Penyakit Stroke Pada Puskesmas
- Iwan Virgiawan Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia
-
Erizal Erizal
*
Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
One of the main reasons people become disabled or die is because of a stroke. The key to swift and effective therapy is an early diagnosis. This research examines the relative performance of the Naïve Bayes and Random Forest algorithms in identifying stroke cases using data collected from patients at the Cipayung Health Center. Age, gender, BMI, smoking status, hypertension, and other physical and mental health issues are some of the characteristics represented in the 644 samples used in the study. Collecting data, cleaning it up, and then evaluating the model using metrics like recall, precision, and accuracy are all part of the research process. With a 92% accuracy rate, the Random Forest algorithm outperformed Naïve Bayes (87% accuracy rate), according to the data. Medical professionals may use these results as a guide to improve stroke detection, which in turn accelerates treatment and lessens the likelihood of consequences. The findings of this study also pave the way for future research into machine learning algorithms.
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References
G. Ciaburro and G. Iannace, “Machine learning-based algorithms to knowledge extraction from time series data: A review,” Data (Basel), vol. 6, no. 6, pp. 1–30, 2021, doi: 10.3390/data6060055.
Wijoyo A, Saputra A, Ristanti S, Sya’ban S, Amalia M, and Febriansyah R, “Pembelajaran Machine Learning,” OKTAL (Jurnal Ilmu Komputer dan Science), vol. 3, no. 2, pp. 375–380, 2024, [Online]. Available: https://journal.mediapublikasi.id/index.php/oktal/article/view/2305
A. Jariah and I. Suaidah, “Efektivitas Penerapan Model Pembelajaran Inquiry Terbimbing Berbantuan Pictorial Riddle terhadap Pemahaman Konsep Fisika Peserta Didik di SMAN 14 Bulukumba,” Jurnal Pendidikan Tambusai, vol. 9, no. 2, pp. 26503–26510, 2025.
A. Q. Khan, M. Matskin, R. Prodan, C. Bussler, D. Roman, and A. Soylu, “Cloud storage tier optimization through storage object classification,” Computing, vol. 106, no. 11, pp. 3389–3418, 2024, doi: 10.1007/s00607-024-01281-2.
L. Dewi and E. Fitraneti, “Stroke Iskemik,” Scientific Journal, vol. 3, no. 6, pp. 365–374, 2024, doi: 10.56260/sciena.v3i6.173.
T. Inui et al., “The role of micronutrients in ageing asia: What can be implemented with the existing insights,” Nutrients, vol. 13, no. 7, pp. 1–27, 2021, doi: 10.3390/nu13072222.
J. Bekker and J. Davis, Learning from positive and unlabeled data: a survey, vol. 109, no. 4. Springer US, 2020. doi: 10.1007/s10994-020-05877-5.
D. J. Hand, P. Christen, and N. Kirielle, “F*: an interpretable transformation of the F-measure,” Mach Learn, vol. 110, no. 3, pp. 451–456, 2021, doi: 10.1007/s10994-021-05964-1.
A. D. P. Harefa, K. I. Nahampun, P. Ritonga, and Ivo. Andre, “Penerapan Metode Naive Bayes Untuk Memprediksi Hasil Belajar Peserta Didik ‘Studi Kasus SD Swasta Methodist-1 Rantauprapat,’” Journal Computer Science and Information Technology(JCoInT), vol. 5, no. 2, pp. 238–248, 2024.
N. C. Ramadhan, H. H. H, T. Rohana, and A. M. Siregar, “Optimasi Algoritma Machine Learning Menggunakan Seleksi Fitur Xgboost Untuk Klasifikasi Kanker Payudara,” TIN: Terapan Informatika Nusantara, vol. 5, no. 2, pp. 162–171, 2024, doi: 10.47065/tin.v5i2.5408.
K. Gajowniczek and M. Dudziński, “Influence of Explanatory Variable Distributions on the Behavior of the Impurity Measures Used in Classification Tree Learning,” Entropy, vol. 26, no. 12, pp. 1–35, 2024, doi: 10.3390/e26121020.
A. Durap, “Data-driven models for significant wave height forecasting: Comparative analysis of machine learning techniques,” Results in Engineering, vol. 24, no. November, p. 103573, 2024, doi: 10.1016/j.rineng.2024.103573.
P. Anggraini and W. Winarsih, “Komparasi Naïve Bayes, Support Vector Machine, Dan Random Forest Dalam Analisis Sentimen Aplikasi Shopee Di Google Play Store,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 3, pp. 4451–4457, 2025, doi: 10.36040/jati.v9i3.13675.
N. Ayub, Tayyaba, S. Hussain, S. S. Ullah, and J. Iqbal, “An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification,” Algorithms, vol. 16, no. 12, pp. 1–30, 2023, doi: 10.3390/a16120548.
A. Widianti and I. Pratama, “Penanganan Missing Values dan Prediksi Data Timbunan,” RABIT : Jurnal Teknologi dan Sistem Informasi Univrab, vol. 9, no. 2, pp. 242–251, 2024.
K. A. Barchard and J. A. Russell, “Distorted correlations among censored data: causes, effects, and correction,” Behav Res Methods, vol. 56, no. 3, pp. 1207–1228, 2024, doi: 10.3758/s13428-023-02086-5.
C. A. Ramezan, T. A. Warner, A. E. Maxwell, and B. S. Price, “Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data,” Remote Sens (Basel), vol. 13, no. 3, pp. 1–27, 2021, doi: 10.3390/rs13030368.
H. Oktavianto, H. W. Sulistyo, G. Wijaya, D. Irawan, and G. Abdurrahman, “Analisis Perbandingan Decision Tree dan Random Forest Pada Klasifikasi Teks Data Kesehatan,” Bina Insani Ict Journal, vol. 11, no. 1, p. 56, 2024, doi: 10.51211/biict.v11i1.2928.
J. Kozak, B. Probierz, K. Kania, and P. Juszczuk, “Preference-Driven Classification Measure,” Entropy, vol. 24, no. 4, pp. 1–24, 2022, doi: 10.3390/e24040531.
K. W. C. Wahyuditomo Imam; Sutrisno, Sutrisno, “Implementasi Integrasi K-Means dan Naïve Bayes dalam Identifikasi Tingkat Risiko Reksa Dana,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 7, pp. 3094–3102, 2021, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/9487/4284
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