RASKIN Recipient Classification Model Using LibSVM Based on Particle Swarm Optimization

Nuranisah Nuranisah; Subhan Hafiz Nanda Ginting

Penulis

Nuranisah Nuranisah Universitas Battuta Penulis
Subhan Hafiz Nanda Ginting Universitas Battuta Penulis

Kata Kunci:

LibSVM, RASKIN, Optimize, Data Mining , Discovery in Database

Abstrak

Receiving subsidized rice assistance (RASKIN) is a government program to distribute basic food assistance to underprivileged families. The distribution is carried out once every three months in accordance with the predetermined allocation. However, in practice, there are still often inaccurate targets in determining beneficiaries. This research aims to assist the village government in determining the eligibility of prospective RASKIN beneficiaries objectively and on target. The classification model used is Support Vector Machine (SVM) with the Library for Support Vector Machine (LibSVM) approach, combined with the Knowledge Discovery in Database (KDD) method. To improve classification performance, model parameter optimization is performed using the Particle Swarm Optimization (PSO) algorithm. Radial Basis Function (RBF) kernel is used in this process. The evaluation results show that the LibSVM model optimized with PSO is able to achieve an accuracy rate of 92.21%. The proposed model is expected to be an effective decision support system in selecting recipients of government social assistance more fairly and efficiently.

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