Implementation of Data Mining for Vertigo Disease Classification Using the Support Vector Machine (SVM) Method

Nadya Jasmin; Rozzi Kesuma Dinata; Ilham Sahputra

doi:10.29103/jacka.v1i4.17807

Implementation of Data Mining for Vertigo Disease Classification Using the Support Vector Machine (SVM) Method

Nadya Jasmin, Rozzi Kesuma Dinata, Ilham Sahputra

Abstract

This research aims to implement advanced data mining techniques for the classification of vertigo disorders using the Support Vector Machine (SVM) method. Vertigo, characterized by a spinning sensation, can be triggered by various factors such as nervous system disorders and inner ear infections. With the rising prevalence of vertigo patients, there is a pressing need for more effective and efficient diagnostic tools. This study was conducted at Puskesmas Jangka in Bireuen Regency, involving the collection of vertigo patient data from the years 2023-2024. The collected data underwent a comprehensive preprocessing pipeline, including data cleaning, partitioning into training and testing datasets, and subsequent implementation of the SVM algorithm. The performance of the model was evaluated using the Mean Absolute Percentage Error (MAPE), resulting in a MAPE value of 28.47%.

Keywords

Vertigo; Data Mining; Support Vector Machine (SVM); MAPE; Clasification

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DOI: https://doi.org/10.29103/jacka.v1i4.17807