Support Vector Machine Algorithm Optimization Using Particle Swarm Optimization for Prosperous Family Card Recipients
Optimasi Algoritma Support Vector Machine Menggunakan Particle Swarm Optimization untuk Penerima Kartu Keluarga Sejahtera
Abstract
Recipients of the Prosperity Card (KKS) are set quotas for the provision of assistance; however, many families submit incorrect data, making it impossible for officials to process the incomplete data. Social assistance has not achieved its goals for a very long time, despite potential growth. Support vector machine is the most commonly used classification method to categorize predictions about recipients of the Prosperous Family Card. Other techniques, such as Particle Swarm Optimization, can be used to optimize the SVM algorithm to improve accuracy. measurements based on a holdout approach to data sharing and support vector machine accuracy. Based on the findings, the accuracy of the support vector machine increased on average from 89.58% to 92.26%.
References
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