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All Journal Journal of Applied Information, Communication and Technology (JAICT) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Techno Nusa Mandiri : Journal of Computing and Information Technology JURTEKSI Jurnal Mantik Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Sisfotek Global Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat INOVTEK Polbeng - Seri Informatika
Reyhan Achmad Rizal
Universitas Prima Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Classification Of Hypertension Using K-Nearest Neighbor Based On Photoplethysmograph Data And Blood Pressure Estimator Jasmin William Natanael Sinaga; Tasya Rouli Christy Tampubolon; Ester Farida Simanjuntak; Delima Sitanggang; Reyhan Achmad Rizal
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/zswzf122

Abstract

Hypertension is a persistent cardiovascular condition, often termed the “silent killer” because it typically presents no symptoms in its early stages. To address the shortcomings of traditional blood pressure monitoring methods, this study develops a classification system that leverages photoplethysmography (PPG) signals in combination with the K-Nearest Neighbor (KNN) algorithm. PPG provides a promising non-invasive solution that is readily adaptable to portable devices. The classification process employs the Euclidean Distance method to determine the similarity between new data samples and previously labeled instances. Data were collected from 276 individuals spanning various age groups using PPG sensors connected to the MR-IAT Robot Covid platform. The system categorizes individuals into normotensive, prehypertensive, stage 1, and stage 2 hypertension groups. The study evaluates the performance of the KNN algorithm based on its ability to predict blood pressure categories from morphological features extracted from the PPG signals. Ultimately, the outcomes of this research are expected to advance the development of efficient, real-time, continuous blood pressure monitoring systems through user-friendly machine learning approaches.
Co-Authors Abdi Zulfikri Ajulio Padly Sembiring Amsal Yusuf Gultom Ananda Faridhatul Ulva Ardi Bernandustahi Miduk Napitupulu Cut Lika Mestika Sandy Dedy Ongky Wijaya Delima Sitanggang, Delima Despaleri Perangin-angin Ester Farida Simanjuntak Fadlisyah Fadlisyah Jasmin William Natanael Sinaga Kristina Sinaga Lidya Aprilla Siregar Marlince NK Nababan Muhathir, Muhathir Mutammimul Ula Nur Azizah Nurlela Octavia Purba Octavriana Della C. Sihombing Ona Rizal Fahmi Prabhu Warren RICO WIJAYA DEWANTORO Rinaldi Fauzan Sebastian Suhandinata Sonni Yudha Nugraha Arfan Srinjiwi Srinjiwi Suardin Gulo Taripar Jonibet Siagian Tasya Rouli Christy Tampubolon Taufik Ismail Simanjuntak