GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM
A rating system or reviews are generally used to assist in making decisions. Rating system widely used as a technique in the recommendation of one of them used by the customer, as in determining the resort to be used. However, the credibility of the rating looks vague because the rating could only r...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Politeknik Elektronika Negeri Surabaya
2019-12-01
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| Series: | Emitter: International Journal of Engineering Technology |
| Subjects: | |
| Online Access: | https://emitter.pens.ac.id/index.php/emitter/article/view/325 |
| _version_ | 1819239805975789568 |
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| author | Rolly Maulana Awangga Syafrial Fachri Pane Diana Asri Wijayanti |
| author_facet | Rolly Maulana Awangga Syafrial Fachri Pane Diana Asri Wijayanti |
| author_sort | Rolly Maulana Awangga |
| collection | DOAJ |
| description | A rating system or reviews are generally used to assist in making decisions. Rating system widely used as a technique in the recommendation of one of them used by the customer, as in determining the resort to be used. However, the credibility of the rating looks vague because the rating could only represent some points of service. So that customer preference with each other is very different. Personalized recommendation systems offer more personalized advice, precisely knowing the preferences or tastes of the customers. Especially for customers who have a transaction history or reservation as at their resorts provide good information used by managers to design a recommendation model for their customers. In this study aims to create a model of resort recommendations based on a rating of frequency. This frequency is the number of resort use by the customer within the specified time frame. With the frequency can represent the preferences of customers. The RFM method is used to measure the reservation frequency value of the customer. The K-Means method is used to categorize customer data with its frequency and classify the type of resort. Recommendation resort to the customer based on the dominant use in one of the resort types. The recommended type of resort based on the similarity between the types of resorts used with other types of resorts. |
| first_indexed | 2024-12-23T13:57:58Z |
| format | Article |
| id | doaj.art-802dac82ec404164b1ce7d2a6e35230f |
| institution | Directory Open Access Journal |
| issn | 2355-391X 2443-1168 |
| language | English |
| last_indexed | 2024-12-23T13:57:58Z |
| publishDate | 2019-12-01 |
| publisher | Politeknik Elektronika Negeri Surabaya |
| record_format | Article |
| series | Emitter: International Journal of Engineering Technology |
| spelling | doaj.art-802dac82ec404164b1ce7d2a6e35230f2022-12-21T17:44:24ZengPoliteknik Elektronika Negeri SurabayaEmitter: International Journal of Engineering Technology2355-391X2443-11682019-12-017210.24003/emitter.v7i2.325325GURILEM : A Novel Design of Customer Rating Model using K-Means and RFMRolly Maulana Awangga0Syafrial Fachri Pane1Diana Asri Wijayanti2Politeknik Pos IndonesiaPoliteknik Pos IndonesiaPoliteknik Pos IndonesiaA rating system or reviews are generally used to assist in making decisions. Rating system widely used as a technique in the recommendation of one of them used by the customer, as in determining the resort to be used. However, the credibility of the rating looks vague because the rating could only represent some points of service. So that customer preference with each other is very different. Personalized recommendation systems offer more personalized advice, precisely knowing the preferences or tastes of the customers. Especially for customers who have a transaction history or reservation as at their resorts provide good information used by managers to design a recommendation model for their customers. In this study aims to create a model of resort recommendations based on a rating of frequency. This frequency is the number of resort use by the customer within the specified time frame. With the frequency can represent the preferences of customers. The RFM method is used to measure the reservation frequency value of the customer. The K-Means method is used to categorize customer data with its frequency and classify the type of resort. Recommendation resort to the customer based on the dominant use in one of the resort types. The recommended type of resort based on the similarity between the types of resorts used with other types of resorts.https://emitter.pens.ac.id/index.php/emitter/article/view/325customer preferencesratingrecommendationRFMK-Means. |
| spellingShingle | Rolly Maulana Awangga Syafrial Fachri Pane Diana Asri Wijayanti GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM Emitter: International Journal of Engineering Technology customer preferences rating recommendation RFM K-Means. |
| title | GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM |
| title_full | GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM |
| title_fullStr | GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM |
| title_full_unstemmed | GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM |
| title_short | GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM |
| title_sort | gurilem a novel design of customer rating model using k means and rfm |
| topic | customer preferences rating recommendation RFM K-Means. |
| url | https://emitter.pens.ac.id/index.php/emitter/article/view/325 |
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