Prediksi Nasabah Yang Berpotensi Membuka Simpanan Deposito Menggunakan Naive Bayes Berbasis Particle Swarm Optimization
DOI:
https://doi.org/10.33633/tc.v17i2.1648Keywords:
prediksi nasabah yang berpotensi membuka deposito, data mining, naive baiyes, particle swarm optimization, seleksi fiturAbstract
Deposito masih merupakan pilihan utama bagi masyarakat untuk berinvestasi saat ini dan hal itu merupakan kesempatan bagi bank-bank untuk menentukan strategi pemasaran dan promosi yang lebih efisien dengan tidak terlalu banyak menggunakan biaya sehingga masyarakat tertarik untuk berinvestasi pada produk deposito dari bank tersebut. Atas dasar permasalahan tersebut, maka dilakukan penelitian untuk memprediksi nasabah yang berpotensi membuka deposito dengan menggunakan teknik data mining khususnya algoritma Naive Bayes berbasis PSO. PSO pada penelitian ini akan digunakan untuk feature selection yaitu dengan memilih attribut terbaik dengan memilih attribut yang sudah diberikan bobot sehingga dapat meningkatkan hasil akurasi dari prediksi menggunakan algoritma Naive Bayes. Hasil dari prediksi nasabah yang berpotensi membuka deposito dengan menggunakan Naive Baiyes memiliki akurasi sebesar 82,19%. Sedangkan prediksi yang menggunakan Naive Baiyes berbasis PSO memiliki akurasi sebesar 89,70%. Penggunaan algoritma PSO ternyata meningkatkan akurasi sebesar 7,51% dan algoritma Naive Baiyes berbasis PSO tersebut dapat digunakan untuk decision support system nasabah yang berpotensi membuka deposito karena menjadi model algoritma yang terbaik.ÂReferences
Lembaga Penjamin Simpanan, "Distribusi Simpanan Bank Umum," Lembaga Penjamin Simpanan, Jakarta, 2013.
Dewan Perwakilan Rakyat Republik Indonesia, "Undang-Undang Republik Indonesia Nomor 10 Tahun 1998," Dewan Perwakilan Rakyat Republik Indonesia, Jakarta, 1998.
Satsya Yoga Baswara, "Analisa Nilai Hasil Investasi Deposito Rupiah, Deposito Dolar Amerika, dan Dinar Emas Dengan Emas Sebagai Alat Ukur," Skripsi Akuntansi Universitas Diponegoro, Semarang, 2012.
Charles X. Ling and Chenghui Li, "Data Mining for Direct Marketing: Problems and Solutions," in Proceedings of the 4th KDD conference, 1998, pp. 73-79.
Chuangxin Ou, Chunnian Liu, Jiajing Huang, and Ning Zhong, "On Data Mining for Direct Marketing," in Proceedings of the 9th RSFDGrC conference, 2003, pp. 491-498.
Carole Page and Ye Luding, "Bank Manager’s Direct Marketing Dilemmas – Customer’s Attitudes and Purchase Intention," International Journal of Bank Marketing, vol. 21, no. 3, pp. 147-163, 2003.
Daniel T. Larose, Discovering Knowledge In Data. United States of America: John Wiley & Sons, Inc., 2005.
Kusrini and Emha Taufiq Luthfi, Algoritma Data Mining, 1st ed. Yogyakarta, Indonesia: Andi, 2009.
Eko Prasetyo, Data Mining : Konsep dan Aplikasi menggunakan MATLAB, 1st ed. Yogyakarta, Indonesia: Andi, 2012.
Florin Gorunescu, Data Mining Concepts, Models and Techniques. Chennai, India: Springer, 2011.
Sergio Moro and Raul M.S. Laureano, "Using Data Mining for Bank Direct Marketing: An Application of The CRISP-DM Methodology," Instituto Universitário de Lisboa, Lisboa, 2011.
Nayyar A. Zaidi, Jesus Cerquides, Mark J. Carman, and Geoffrey I.Webb, "Alleviating Naive Bayes Attribute Independence Assumption by AttributeWeighting," Journal of Machine Learning Research, no. 14, pp. 1947-1988, 2013.
Sheng-Wei Fei, Yu-Bin Miao, and Cheng-Liang Liu, "Chinese Grain Production Forecasting Method Based on Particle Swarm Optimization-based Support Vector Machine," in Recent Patents on Engineering, Shanghai, 2009, pp. 8-12.
Syaeful Mujab, "Pencarian Model Terbaik antara Algoritma C4.5 dan C4.5 Berbasis Particle Swarm Optimization untuk Prediksi Promosi Deposito," Universitas Dian Nuswantoro, Semarang, Skripsi Teknik Informatika 2013.
Abbie Dwi Pradibyo, "Analisa Perbandingan Kinerja Algoritma Klasifikasi Data Mining Untuk Prediksi Nasabah Bank yang Berpotensi Membuka Simpanan Deposito Berjangka," Universitas Dian Nuswantoro, Semarang, Skripsi Teknik Informatika 2013.
Jie Lin and Jiankun Yu, "Weighted Naive Bayes Classification Algorithm Based on Particle Swarm Optimization," IEEE, 2011.
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