Comparison of K-Nearest Neighbor Algorithm Performance and Naïve Bayes in Predicting Stroke Disease

Abdul Roni; Maria Fransiska Fitriani; Nazwa Aurellia Ainanur; Sumanto; Ade Surya Budiman

doi:10.59934/jaiea.v5i1.1223

Authors

Abdul Roni Fakultas Teknik dan Informatika, Universitas Bina Sarana Informatika
Maria Fransiska Fitriani Fakultas Teknik dan Informatika, Universitas Bina Sarana Informatika
Nazwa Aurellia Ainanur Fakultas Teknik dan Informatika, Universitas Bina Sarana Informatika
Sumanto Fakultas Teknik dan Informatika, Universitas Bina Sarana Informatika
Ade Surya Budiman Fakultas Teknik dan Informatika, Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i1.1223

Keywords:

Stroke, Prediction, K-Nearest Neighbor, Naïve Bayes, Data Mining

Abstract

Stroke is one of the most dangerous diseases that can cause death and long-term disability. Early identification of stroke risk can help the prevention process. This study compares two classification algorithms, namely K-Nearest Neighbor (K-NN) and Naïve Bayes, in prediction stroke risk based on patient data. The dataset used is 1470 data that has several attributes such as age, hypertension, heart disease, glucose levels, and others. The evaluation results showed that Naïve Bayes algorithm performed better with 73.1% accuracy and 79.9% AUC, compared to K-NN which had 68.4% accuracy and 75.1% AUC. Based on these results, Naïve Bayes algorithm is considered more effective to be used in stroke risk prediction system.

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References

S. Mutmainah, “Penanganan Imbalance Data Pada Klasifikasi Kemungkinan Penyakit Stroke,” J. Sains, Nalar, dan Apl. Teknol. Inf., vol. 1, no. 1, pp. 10–16, 2021, doi: 10.20885/snati.v1i1.2.

T. TUNIK, “Faktor-Faktor Penyebab Dan Pencegahan Terjadinya Stroke Berulang,” Heal. J. Inov. Ris. Ilmu Kesehat., vol. 1, no. 2, pp. 101–108, 2022, doi: 10.51878/healthy.v1i2.1114.

A. Rohman and M. Rochcham, “Komparasi Metode Klasifikasi Data Mining Untuk Prediksi Kelulusan Mahasiswa,” Neo Tek., vol. 5, no. 1, pp. 73–79, 2019, doi: 10.37760/neoteknika.v5i1.1379.

B. A. C. Permana, M. Sadali, and R. Ahmad, “Penerapan Model Decision Tree Menggunakan Python Untuk Prediksi Faktor Dominan Penyebab Penyakit Stroke,” Infotek J. Inform. dan Teknol., vol. 7, no. 1, pp. 23–31, 2024, doi: 10.29408/jit.v7i1.23232.

N. Y. Paramitha, A. Nuryaman, A. Faisol, E. Setiawan, and E. Nurvazly, “Klasifikasi Penyakit Stroke Menggunakan Metode Naïve Bayes,” vol. 04, no. 01, pp. 11–16, 2023.

N. A. Iskandar, I. Ernawati, and Y. Widiastiwi, “Klasifikasi Diagnosis Penyakit Stroke Dengan Menggunakan Metode Random Forest,” pp. 432–441, 2022.

S. P. Keluarga, Buku_Pencegahan_Stroke_dan_Penatalaksanaan_Pre_Hospital_.

Awas Stroke.

P. Yayasan and K. Menulis, Pengenalan Data Mining.

L. Rahmawati, M. Hafid, and M. A. Sunandar, “Analisis Data Mining Untuk Memprediksi Penyakit Stroke Dengan Algoritma N aïve Bayes,” vol. 6, no. 2, pp. 55–60, 2023.

N. Ichsan et al., “IMPLEMENTASI ORANGE DATA MINING UNTUK,” vol. 4, no. 2, pp. 118–125, 2022.

M. M. Jakarta, “Penerapan Algoritma K-Nearest Neighbor ( KNN ) untuk Memprediksi Stroke pada Rumah Sakit Pusat Otak Nasional Prof .,” vol. 26, no. 1, pp. 144–153.

M. A. Salwa, “Optimasi Model Algoritma Klasifikasi Data Mining Menggunakan Metode Feature Selection Untuk Prediksi Penyakit Stroke,” vol. 26, no. 1, pp. 11–20, 2025.

F. Y. Febrieta Ditta, Statistika Dasar untuk Pemula. 2023.