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All Journal Format : Jurnal Imiah Teknik Informatika IJISTECH (International Journal Of Information System & Technology) Journal of Information Systems and Informatics Madani : Indonesian Journal of Civil Society IJISTECH Jurnal Satya Informatika
Ifan Prihandi
Universitas Mercu Buana
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences Other

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Journal : IJISTECH (International Journal Of Information System

 1 
Design and Build A Customer-Finding Application For Leko Restaurant Using The K-Means Algorithm Mohamad Yusuf; Muhaimin Hasanudin; Ifan Prihandi
IJISTECH (International Journal of Information System and Technology) Vol 6, No 2 (2022): August
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.288 KB) | DOI: 10.30645/ijistech.v6i2.238

Abstract

Warung Makan Leko is one of the restaurants in the Jakarta area that offers local cuisine, a diverse menu and delivery orders via phone order. Customers are one source of income for Warung Makan Leko. The amount of competition makes Warung Makan Leko have difficulty in retaining loyal customers. For this reason, further analysis is needed to find out who these potential customers are. Then an application was developed to classify customer data using the K-Means (clustering) algorithm. The data used as an example in this study is the sales transaction data of Warung Makan Leko. Run the process to calculate the total sales to customers and the number of transactions with customers to classify customer data. The K-Means clustering method tries to group the existing data into groups. Data in groups have the same properties. Customer data is grouped into two clusters, no and implicit. Each cluster is then classified based on the prioritized criteria. The cluster with the highest centroid value is the cluster that is rewarded, and the cluster with the lowest centroid value is the non-rewarded cluster. The results of this process form clusters, which are used for advice and consideration to determine sales strategy, namely to reward customers who rank higher in the cluster
Co-Authors Ilham Maulana Mohamad Yusuf Muhammad Rifqi Nurullah Husufa Wawan Kurniawan