{"id":1729,"date":"2020-12-03T19:04:09","date_gmt":"2020-12-03T12:04:09","guid":{"rendered":"http:\/\/research.binus.ac.id\/airdc\/?p=1729"},"modified":"2021-11-01T10:28:08","modified_gmt":"2021-11-01T03:28:08","slug":"data-mining-for-predicting-customer-satisfaction-using-clustering-techniques","status":"publish","type":"post","link":"https:\/\/research.binus.ac.id\/airdc\/2020\/12\/data-mining-for-predicting-customer-satisfaction-using-clustering-techniques\/","title":{"rendered":"Data Mining for Predicting Customer Satisfaction Using Clustering Techniques"},"content":{"rendered":"<p style=\"text-align: justify\">Managing customer satisfaction has become an important business trend, including restaurants business. This study aims to determine the application of the K-means, Spectral Clustering (SC), and Agglomerative Clustering (AC) method for measuring customer satisfaction on a family restaurant in Taiwan. We contribute the data collection process and application of data mining in a family restaurant. The clustering analysis based on agglomerative clustering approach performs as well as the K-means approach to cluster the same characteristics of the customers. At last, this study shows the measurement result of customer satisfaction and provides improvement suggestion to the restaurant concerned.<\/p>\n<p style=\"text-align: justify\">International Conference on Information Management and Technology 2020<\/p>\n<p style=\"text-align: justify\"><strong>Kartika Purwandari, Join W C Sigalingging, Muhammad Fhadli, Shinta Nur Arizky, and Bens Pardamean<\/strong><\/p>\n<p style=\"text-align: justify\"><a href=\"https:\/\/www.researchgate.net\/publication\/343626126_Data_Mining_for_Predicting_Customer_Satisfaction_Using_Clustering_Techniques\">Read Full Paper<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Managing customer satisfaction has become an important business trend, including restaurants business. This study aims to determine the application of the K-means, Spectral Clustering (SC), and Agglomerative Clustering (AC) method for measuring customer satisfaction on a family restaurant in Taiwan. We contribute the data collection process and application of data mining in a family restaurant. [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":2044,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1729","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"_links":{"self":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/1729","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/comments?post=1729"}],"version-history":[{"count":1,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/1729\/revisions"}],"predecessor-version":[{"id":1731,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/1729\/revisions\/1731"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media\/2044"}],"wp:attachment":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media?parent=1729"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/categories?post=1729"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/tags?post=1729"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}