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Abulawafa Muhammad
Universitas Putra Indonesia YPTK Padang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Customer Relationship Management to Retain Customers with the Topsis Method Muhammad Nuzul Ikhsan; Abulawafa Muhammad; Raja Ayu Mahessya
Journal of Computer Scine and Information Technology Volume 9 Issue 2 (2023): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v9i2.73

Abstract

In the era of globalization, the increasingly rapid development of technological sophistication is an aspect that can be utilized to achieve convenience, including the flow of information. Each company has its own way of retaining customers. At the Metacom store, efforts to retain customers have not been carried out in an updated and online manner. Customer Relationship Management (CRM) is one way of retaining customers to fulfill this goal by combining CRM and TOPSIS methods. For this reason, customer data analysis was carried out at the Metacom store using the Topsis method, from 10 transaction data, 2 were recommended to be given a discount on the goods. The results of applying TOPSIS use ranking results from goods transaction results. This Topsis analysis can be used to change a CRM application, where this application is equipped with a sales information system as well as goods data collection (Cashier) and a sales application using the Topsis method with a combination of these two methods in the Metacom Store. From the processed data
Grouping Building Material Sales for Management Optimization with the K-Means Algorithm Nofet Putri; Abulawafa Muhammad; Raja Ayu Mahessya
Journal of Computer Scine and Information Technology Volume 9 Issue 3 (2023): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v9i3.74

Abstract

Sales transactions at the Pedi Building Store are recorded in the daily logbook provided by the store, so that it cannot be immediately known which types of products are selling well or which are not selling well. To overcome this problem, an analysis of sales data is carried out using the K-Means Algorithm. The K-Means algorithm is an algorithm that requires as many input parameters and divides a set of objects into clusters so that the level of similarity between members in one cluster is high, while the level of similarity with members in other clusters is very low. This study aims to determine the types of products that sell well and don't sell well. The data used in this analysis are 100 data samples with 2 clusters formed, namely unsold goods (Cluster 1) and salable goods (Cluster 2). With the analysis of the K-Means Algorithm, it produces 4 data items that are not selling well (Cluster 1), while 96 data items that sell well (Cluster 2). With the results of the analysis of the K-Means Algorithm, a sales application was developed for the Pedi Building Store which is managed by the analysis of the K-Means Algorithm. The application makes it easy for sellers to manage data to form sales processes of salable and less salable goods
Co-Authors Muhammad Nuzul Ikhsan Nofet Putri Raja Ayu Mahessya