<![CDATA[Parkinson’s Disease is a progressive neurological disorder that affects motor functions and verbal communication of the patients. Early detection of this disease is crucial to improving patients’ quality of life. This study aims to develop an early detection system for Parkinson’s Disease by utilizing sound frequency as the primary feature. The algorithm employed in this research is Random Forest, with the analysis process following the CRISP-DM approach, which includes six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Based on the test results, the developed model achieved an accuracy of 94.92% on the dataset used. These findings indicate that the Random Forest algorithm can be effectively implemented as an early detection system for Parkinson’s Disease using sound frequency data.]]>
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