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All Journal Codeverse: Journal of Emerging Digital Realities
Nur Fadila Palisoa
Universitas Pattimura

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Support Vector Machine (SVM) For Classification of Disadvantaged Nur Fadila Palisoa
Codeverse: Journal of Emerging Digital Realities Vol. 1 No. 1 (2025): CODEVERSE: Journal of Emerging Digital Realities
Publisher : Codeverse: Journal of Emerging Digital Realities

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Abstract

Equitable development and development of regions is very important to ensure regional socio-economic equality and balance, for this reason it is necessary to classify regions in order to determine priorities in equitable develop  ent that is fast and on target. The classification method used is Support Vector Machine (SVM). This research aims to analyze the accuracy of the classification of disadvantaged areas in Maluku Province with the data source used is secondary data sourced from several Statistich Maluku Province publications. Based on the results of classification of disadvantaged areas using SVM with the RBF kernel, it has the best results with parameters cost = 0.1 and gamma = 1 and the resulting classification accuracy level is 95.4% and the AUC value = 0.9285 which is classified as very good classification results.
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