Penerapan Data Mining Menggunakan Algoritma pada Sistem Informasi Deteksi dini Parkinson Diseas Berbasis Rapidminer : Penelitian

Taufiqurohman Taufiqurohman; Adani Febriyan; Firman Nasty Aulia; Hezron Perez Gelu Bogo; Muhamad Sadam Basra; Syahrul Abdul Rohim

doi:10.31004/jerkin.v4i3.4977

Authors

Taufiqurohman Taufiqurohman Universitas Bina Sarana Informatika
Adani Febriyan Universitas Bina Sarana Informatika
Firman Nasty Aulia Universitas Bina Sarana Informatika
Hezron Perez Gelu Bogo Universitas Bina Sarana Informatika
Muhamad Sadam Basra Universitas Bina Sarana Informatika
Syahrul Abdul Rohim Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31004/jerkin.v4i3.4977

Keywords:

Parkinson's Disease, Data Mining, RapidMiner, Machine Learning, Classification

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.

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