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All Journal QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama Harmoni El-Usrah: Jurnal Hukum Keluarga Al-Mal:Jurnal Akuntansi dan Keuangan Islam Indonesian Journal of Social Science Research Tasyri' Academic Hospital Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan Al-Ahkam: Jurnal Ilmu Syari'ah dan Hukum USRATUNA: Jurnal Hukum Keluarga Islam
Taufiqurohman, Taufiqurohman
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Religion Arts Humanities Dentistry Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Penerapan Data Mining Menggunakan Algoritma pada Sistem Informasi Deteksi dini Parkinson Diseas Berbasis Rapidminer : Penelitian Taufiqurohman, Taufiqurohman; Febriyan, Adani; Aulia, Firman Nasty; Bogo, Hezron Perez Gelu; Basra, Muhamad Sadam; Rohim, Syahrul Abdul
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4977

Abstract

This study utilized a 20-sample dataset with 23 acoustic attributes reflecting the patient's vocal characteristics, including parameters such as MDVP:Fo(Hz), MDVP:Fhi(Hz), MDVP:Flo(Hz), jitter, shimmer, and harmonic values. The target attribute used was status, which indicates whether the patient is indicated as having Parkinson's (1) or not (0). The study aimed to build a data mining-based prediction model using RapidMiner to support an information system for early detection of Parkinson's Disease. The analysis stages followed the CRISP-DM framework, which includes data understanding, preprocessing, modeling, and evaluation. The algorithms tested included Support Vector Machine, Random Forest, and Naïve Bayes using a cross-validation scheme. The experimental results showed that Random Forest provided the most consistent performance on this small dataset with more stable accuracy and prediction confidence values ​​compared to other algorithms. These findings confirm that acoustic features of voice can serve as effective early indicators for detecting Parkinson's, while strengthening RapidMiner's role as an efficient analytical platform in data-driven medical research.
Co-Authors Abdurrahman, Landy Trisna Adnyana, I Made Susila Amalia, Khoirunnisa Aulia, Firman Nasty Basra, Muhamad Sadam Bogo, Hezron Perez Gelu Citra Etika Dwi Hartini Febriyan, Adani Ilhami, Nuzula Khasanah, Siti Badrotil Mahmud Arif Malik Ibrahim Mawardani, Annisa Pungki Muhyidin Muhyidin Musaffa, M. Ulul Albab Musaffa, Muhamad Ulul Albab Nelli Fauziah, Nelli Nurihsan, Achmad Juntika Rohim, Syahrul Abdul Satibi Satibi Shoum A, Sholahuddin Suryanto, Totok Tanjung, Rahmadiyah S. Yeni Rachmawati