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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IPTEK The Journal of Engineering IPTEK Journal of Science TELKOMNIKA (Telecommunication Computing Electronics and Control) Seminar Nasional Informatika (SEMNASIF) Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Infotekmesin Halal Research Journal Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Sewagati
Aris Tjahyanto
Institut Teknologi Sepuluh Nopember
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Comparative Analysis of Feature Selection Method to Predict Customer Loyalty Heni Sulistiani; Aris Tjahyanto
IPTEK The Journal of Engineering Vol. 3 No. 1 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/joe.v3i1.2257

Abstract

The growth of Fast Moving Consumer Goods (FMCG) industry is still showing double-digit and Indonesia becomes a potential market for the products FMCG, so that the competition between companies will be intense. The company have to attempted to survive, one of the way is to maintain customer loyalty. Data mining techniques can be used to predict customer loyalty. In data mining pra-processing, feature selection is one of the important thing to reduces the number of features, removes irrelevant, redundant, or data noise, and brings the immediate effects for applications: speeding up a data mining algorithm, improving mining performance such as the accuracy of the prediction and the comprehensive result. This paper aims to identify the relevant factors that affect the performance of the classification of customer loyalty with several feature selection method and to compare the classification performance in customers loyalty prediction of FMCG products. Data was obtained from the results of fast moving consumer goods customers questionnaires towards several brands of instant noodles in Lampung that was ranked TOP Brand Award Phase 1 2016, using nonprobability sampling method and convenience sampling technique. The result in this paper, chi square feature selection methods with threshold > 0.01 showed the best results, it is indicated by the highest accuracy of random forest classification algorithm, that is 83.2% for thirteenth features.
Co-Authors Achmad Dwitama Karisma Afan Hamzah, Afan Afian Syafaadi Rizki Ali Altway Ano Rangga Rahardika Ary Mazharuddin Shiddiqi Atletiko, Faizal Johan Atria Pradityana Ayunda Kusuma Wardani Diah P Wulandari Diah P. Wulandari Faisal Johan Atletiko Fikri Baharuddin Hanim Maria Astuti Heni Sulistiani Heni Sulistiani Indasari, Sri Suci Irmasari Hafidz Lily Pudjiastuti, Lily Lukman Hadi Dwi Purnomo Mazharuddin Shiddiqi, Ary Mukti, Bayu Siddhi Mustapa, Dieki Rian Naseela, Qudsiyah Zahra Ilham Nugroho, Muhammad Aldi Nurul Fajrin Ariyani Pratama, Rivanda Putra Prida Novarita Trisanti, Prida Novarita Prita Dellia Purnama, I Putu Adhitya Pratama Mangku Rahmat Trialih Rangga Rahardika, Ano Riskandy, Yudi Hendra Tetrisyanda, Rizky Warlinda Eka Triastuti Yoyon K Suprapto Yoyon K. Suprapto Yudistira, Ahmadhian Daffa Zuchrillah, Daril Ridho Zuchrillah, Daril Ridho