Penerapan Algoritma C4.5 Untuk Prediksi Loyalitas Nasabah PT Erdika Elit Jakarta
DOI:
https://doi.org/10.30865/mib.v4i1.1652Keywords:
Data Mining, C4.5 Algorithm, Customer LoyaltyAbstract
C4.5 algorithm is a decision tree algorithm group. This algorithm has input in the form of training samples and samples. While samples are data fields which we will use as parameters in classifying data. From the variable transaction frequency the company can see which customers are loyal to the company based on historical customer transaction data, but there are still some variables that make customers loyal to the company. These variables are age, customer gender, company sales gender, educational background, customer  transaction frequency. The company knows how to predict customers who will be loyal to the company based on the experience of some of the variables above, but the company does not know the most influential variable in the assessment of loyal customers because of some of the variables above are not interconnected and it is uncertain if one variable can make a decision whether the customer loyal. Based on the decision tree that has made the most influential attribute on customer loyalty is the educational background because it has the highest gain value of 1.545292721 and as the root of the decision tree while the client's gender does not significantly affect customer loyalty because it is always at the last node with the gain value which is 0.623919119.
References
Y. Ernawati, Y. D. Suseno, and Sunarso, “Pengaruh kualitas produk dan kualitas layanan terhadap loyalitas pelanggan dengan kepuasan pelanggan sebagai variabel mediasi (survei pada pelanggan dgc coffeeshop di solo, kuoarjo,sragen),†J. Manaj. Sumber Daya Mns., vol. 11, no. 2, pp. 175–187, 2017.
N. Iriadi and N. Nuraeni, “Kajian Penerapan Metode Klasifikasi Data Mining Algoritma C4.5 untuk Prediksi Kelayakan Kredit Pada Bank Mayapada Jakarta,†Tek. Komput. AMIK BSI, vol. II, pp. 132–137, 2016.
H. M. Nawawi, S. Rahayu, M. ja’far Shidiq, and J. J. Purnama, “Algoritma C4.5 untuk Memprediksi Pengambilan Keputusan Memilih Deposito Berjangka,†vol. 16, no. 1, pp. 65–72, 2019.
H. D. Tarigan, F. Destiawati, and A. Fitriansyah, “Implementasi Algoritma C4.5 Terhadap Kepuasan Pelanggan,†Ethos (Jurnal Penelit. dan Pengabdi. Masyarakat), vol. Vol. 6, No, pp. 80–86, 2017.
K. J. Atmaja, I. B. G. Anandita, and N. K. C. Dewi, “Penerapan Data Mining Untuk Memprediksi Potensi Pendonor Darah Menjadi Pendonor Tetap Metode Decision Tree C.45,†s@CIES, vol. 7, pp. 101–108, 2017.
Ihsan and R. Wajhillah, “Penerapan Algoritma C4.5 Terhadap Diagnosa Penyakit Demam Tifoid Berbasis Mobile,†SWABUMI, no. October 2015, 2015.
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