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All Journal Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Profisiensi : Jurnal Program Studi Teknik Industri Journal Of Science Education And Management Business Jurnal Sains Informatika Terapan (JSIT) JT-IBSI (Jurnal Teknik Ibnu Sina) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika JoDENS: Journal of Digital Ecosystem for Natural Sustainability
Hilda Herasmus
Program Studi Profesi Insinyur, Pasca Sarjana, Universitas Andalas
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Perbandingan Algoritma Naive Bayes dan K-Nearest Neighbor untuk Prediksi Risiko Penyakit Stroke dengan Teknik Oversampling SMOTE hilda herasmus; Agus Suryadi
Journal of Digital Ecosystem for Natural Sustainability Vol 6 No 1 (2026): Juli 2026
Publisher : Fakultas Komputer - Universitas Universal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63643/jodens.v6i1.389

Abstract

Stroke is a leading cause of death and permanent disability in Indonesia, where prevalence rose from 8.3 per 1,000 population in 2013 to 10.9 per 1,000 in 2018, creating an urgent need for accurate early detection. This study develops and compares stroke risk prediction models based on Naive Bayes (NB) and K-Nearest Neighbor (KNN), each integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. The dataset consists of 512 medical records from a hospital in Batam collected between 2020 and 2023, covering 12 demographic, clinical, and lifestyle predictors. SMOTE was applied only to the training data of an 80 to 20 split, yielding a balanced distribution of 258 samples per class. The KNN parameter K was tuned over eight odd values and 7 was found optimal. Evaluation on 103 test records shows that KNN consistently outperforms Naive Bayes, with accuracy of 91.3 against 81.6 percent, F1-Score of 89.2 against 79.9 percent, and AUC of 0.934 against 0.862. SMOTE raised recall of the Stroke class by 18.8 percentage points. Blood glucose, age, and systolic blood pressure were the three strongest predictors. The model is proposed as an Early Warning System module for hospital information systems.
Co-Authors Agus Suryadi Ali Basriadi David Saro Erlita Amrina Febrianti, Eka Lia Firnam Firnam Ghea Paulina Suri Harmen Harmen Larisang Mulyanto Mulyanto Panji Priantono Putra Sanusi Sanusi Tika Christy