Sulistiani, Heni and Muludi, Kurnia and Syarif, Admi Implementation of Dynamic Mutual Information and Support Vector Machine for Customer Loyalty Classification. IOP Conf. Series: Journal of Physics: Conf. Series 1338 (2019) 012033. ISSN Online ISSN: 1742-6596


Download (446kB) | Preview


Fast Moving Consumer Goods (FMCG) is known one of the important industrial sectors worldwide. It includes household and personal care products as well as processed foods and beverages. Because of the tight competition company must develop good marketing strategies. So, it is important for the company to know customer loyalty and also to predict the income as reference in company development planning. Data mining now is becoming popular technique for predicting customer loyalty. One of the well known data mining strategies is retaining customer’s strategy. In this paper, we would present a new model for predicting customer loyalty. The model is based on Dynamic Mutual Information and Support Vector Machine (DMI-SVM) to identify the relevant factors that affect the performance of the classification of customer loyalty. The comparison of two classification methods and several selected features is given to show the effectiveness of the methods. We validated the model by 10-fold cross validation method. Classification accuracy, precision, recall and f-measure are used to evaluate classifier performance on a test/hold-out sample. A result in this paper is shown that SVM method gives better performance accuracy than Naïve Bayes.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Sistem Informasi
Depositing User: Kurnia Muludi
Date Deposited: 04 Nov 2021 01:10
Last Modified: 04 Nov 2021 01:10

Actions (login required)

View Item View Item