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Journal : EMITTER International Journal of Engineering Technology

Collaboration FMADM And K-Means Clustering To Determine The Activity Proposal In Operational Management Activity Rolly Maulana Awangga; Syafrial Fachri Pane; Khaera Tunnisa
EMITTER International Journal of Engineering Technology Vol 7 No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (209.522 KB) | DOI: 10.24003/emitter.v7i1.317

Abstract

Indonesian government agencies under the Ministry of Energy and Mineral Resources still use manual methods in determining and selecting proposals for operational activities to be carried out. This study uses the Decision Support System (DSS) method, namely Fuzzy Multiple Attribute Decision Decision (Fmadm) and K-Means Clustering method in managing Operational Plan activities. Fmadm to select the best alternative from a number of alternatives, alternatives from this study proposed activity proposals, then ranking to determine the optimal alternative. The K-Means Clustering Method to obtain cluster values for alternatives on the criteria for activity dates, types of activities, and activity ceilings. The last iteration of the Euclidian distance calculation data on k-means shows that alternatives that have the smallest centroid value are important proposal criteria and the largest centroid value is an insignificant proposal criteria. The results of the collaboration of the Fmadm and K-Means Clustering methods show the optimal ranking of activities (proposal activities) and the centroid value of each alternative.
GURILEM : A Novel Design of Customer Rating Model using K-Means and RFM Rolly Maulana Awangga; Syafrial Fachri Pane; Diana Asri Wijayanti
EMITTER International Journal of Engineering Technology Vol 7 No 2 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v7i2.325

Abstract

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.