Please use this identifier to cite or link to this item: https://repository.unej.ac.id/xmlui/handle/123456789/83590
Title: Rancang Bangun Aplikasi Customer Relationship Management (CRM) Untuk Identifikasi Tingkat Kepuasan Pelanggan Pada Perusahaan PT. TIKI Jalur Nugraha Ekakurir (JNE) Agen Mastrip Jember Menggunakan Metode K-Means Clustering
Authors: Maulana, Mochammad Bustommy
Slamin, Slamin
Juwita, Oktalia
Keywords: CRM
Identify the level of customer satisfaction
K-Means Clustering
Issue Date: 7-Dec-2017
Abstract: The level of customer satisfaction is a benchmark to get a better benefit. One of the strategies that relates to creation of customer satisfaction is customer relationship management (CRM). Tiki Jalur Nugraha Ekakurir (JNE) Ltd. in Mastrip Jember agency is a company that experienced consumers degression after the emergence of competitors that offered better services. Because of that it is necessary to evaluate the service quality, facilities, and rates compatibility that can affect customer satisfaction. Therefore, CRM method is needed to know the level of customer satisfaction. Questionnaire is used as the method to collect the customers’ responses as the data. After that, the data which is the questionnaires that contain the customer’s responses is processed using K-Means clustering method as the grouping algorithm of respondents. K-means is algorithm clustering by grouping the data which has similar criteria by determining the center point of the cluster (centroid). There are 4 Clusters, the first cluster has a role as a very dissatisfied customer, the second cluster is not satisfied customer, the third cluster is satisfied customer and the fourth cluster has a role as a very satisfied customer. The application examination used 100 respondents with 80 respondents are training set and 20 respondents are test set. The result showed the cluster which has the most members is cluster 3, it means that there are so many customers satisfied with the services. The results of the training test set 100% accurate with the test set. Thus, the appropriate centroid coordinate is the one from the cluster in the last training set iteration.
Description: Informatics Journal Vol. 2 No. 2 (2017)
URI: http://repository.unej.ac.id/handle/123456789/83590
ISSN: 2503-250X
Appears in Collections:LSP-Jurnal Ilmiah Dosen

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