Clustering Credit Card Holder Berdasarkan Pembayaran Tagihan Menggunakan Improved K-Means dengan Particle Swarm Optimization

Penulis

Farhanna Mar'i, Ahmad Afif Supianto

Abstrak

Abstrak

Kartu kredit merupakan salah satu bentuk media bagi nasabah untuk melakukan kredit dalam sebuah proses transaksi yang telah disetujui oleh bank bersangkutan. Bank harus selektif dalam menganalisa nasabah yang ingin mengajukan penerbitan kartu kredit untuk menghindari adanya kredit macet yang dapat menimbulkan kerugian pada bank, sehingga sangat penting untuk mengetahui karakteristik nasabah dengan melakukan  clustering. Bank akan dapat mengambil keputusan untuk pertimbangan penerbitan kartu kredit dengan mencocokkan nasabah baru kedalam cluster-cluster yang telah dibentuk dan mengetahui kelayakan nasabah untuk diberikan akses kartu kredit dalam melakukan transaksi. K-Means adalah salah satu metode populer yang digunakan untuk clustering. Tetapi, metode K-Means tidak dapat memberikan solusi optimum karena keterbatasannya dalam penentuan titik centroid yang optimal, sehingga untuk memperbaiki metode K-Means dalam penelitian ini digunakan salah satu algoritma evolusi yaitu Particle Swarm Optimization (PSO) untuk generate titik centroid optimum yang digunakan dalam proses perhitungan K-Means. Hasil pengujian dilakukan dengan membandingkan nilai Silhouette Coefficient dari cluster yang dibentuk menggunakan K-Means murni dan Improved K-Means dengan PSO yang menghasilkan nilai masing–masing yaitu 0,3312 dan 0,3730.

 

Abstract

Credit card is one form of media for customers to credit in a transaction process that has been approved by the bank concerned. Banks should be selective in analyzing customers who want to apply for credit card issuance to avoid bad debts that can cause losses to banks, so it is very important to know the characteristics of customers by clustering. The Bank will be able to take decisions for credit card issuance by matching new customers into the established clusters and knowing the eligibility of customers to be granted credit card access in making transactions. K-Means is a popular method that is applied in the clustering process. However, the K-Means method can not provide the optimum solution because of its limitation in determining the optimal centroid point, so to improve the K-Means method in this research is used one of the evolution algorithm namely Particle Swarm Optimization (PSO) to generate optimum centroid point used in k-means calculation process. The test results were performed by comparing the coefficient silhouette values of the clusters formed using pure K-Means and Improved K-Means with PSO which yielded respective values of  0,31614 and 0,39484, respectively.

 

Kata Kunci


credit card holders, clustering, improved k-means, particle swarm optimization

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Referensi


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DOI: http://dx.doi.org/10.25126/jtiik.201856858