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All Journal INCODING: Journal of Informatics and Computer Science Engineering Jurnal Penelitian Rumpun Ilmu Teknik
Ary Prandika Siregar
Universitas Negeri Medan
Computer Science & IT Engineering

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Implementasi Algoritma K-Nearest Neighbors (KNN) dalam Deteksi Dini Hipertensi berdasarkan Analisis Tekanan Darah Ary Prandika Siregar; Said Iskandar Al Idrus; Zulfahmi Indra; Insan Taufik
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.1018

Abstract

This study aims to develop a web-based hypertension detection system using the K-Nearest Neighbors (KNN) algorithm and to analyze its accuracy in classifying hypertension status. The dataset was obtained from 447 patient medical records at RSKG Rasyida, consisting of eight variables: gender, age, systolic blood pressure, diastolic blood pressure, height, weight, body mass index (BMI), and hypertension status. The preprocessing stage involved three main steps—feature selection (age, systolic and diastolic blood pressure, BMI), data balancing using undersampling, and data normalization through the Min-Max method—resulting in 425 balanced data samples with five hypertension categories. The web application includes modules for login, dashboard, data input, detection results, and detection history, and has been evaluated using black box testing. The best KNN performance was achieved at k = 13 with 92.94% accuracy, 94% precision, 93% recall, and 93% F1-score. These results indicate that the proposed system can accurately classify hypertension and serve as an effective, data-driven screening tool for healthcare professionals.
Implementasi Algoritma Random Forest Dalam Klasifikasi Diagnosis Penyakit Stroke Ary Prandika Siregar; Dwi Priyadi Purba; Jojor Putri Pasaribu; Khairul Reza Bakara
Jurnal Penelitian Rumpun Ilmu Teknik Vol. 2 No. 4 (2023): November : Jurnal Penelitian Rumpun Ilmu Teknik
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juprit.v2i4.3039

Abstract

The most common disease in Indonesia is stroke, this disease occurs when blood flow to the brain is disrupted, either due to rupture of blood vessels or due to blockage of blood vessels. The data mining process can be a solution in identifying early symptoms of stroke. By using the Random Forest Method, it is hoped that it can be the right choice for preprocessing data in identifying early symptoms. The model results produce an adjustment of 96% of the training score and from the results table of precision, recall, F1-score, and accuracy which results in an accuracy of 0.95 or 95%, as well as the final result of AUC of 0.80 which shows that the model results are included in the good classification  
Co-Authors Dwi Priyadi Purba Insan Taufik Jojor Putri Pasaribu Khairul Reza Bakara Said . Iskandar Zulfahmi Indra, Zulfahmi