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All Journal International Journal of Information Technology and Computer Science Applications (IJITCSA)
Kristian Vieri, Jhon
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Exclusive Clustering Technique for Customer Segmentation in National Telecommunications Companies Kristian Vieri, Jhon; Munandar, Tb Ai; Srisulistiowati, Dwi Budi
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 1 (2023): January - April 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (917.495 KB) | DOI: 10.58776/ijitcsa.v1i1.19

Abstract

This study aims to empirically examine consumer behavior based on customer transaction history. Analyzing consumer behavior can provide very useful information for businesses in making decisions, particularly business decisions toward customers, in order to survive in such intense competition.Companies are becoming faster and more precise in reading environmental conditions and predicting what conditions may occur as a result of machine learning technology.This technology can also assist companies in making decisions that are more targeted according to actual secondary data provided for research. One of the machine learning methods, unsupervised learning, can help explicitly identify hidden structures or patterns in data and determine correlations. This method uses the Exclusive Clustering method, using two algorithms, namely, K-Means and K-Medoids, to use the comparison method to get optimal segmentation results. The results obtained are expected to be a reference for making a change in the company's marketing policy in order to retain and gain customers who are constantly decreasing.
Comparative Study of Classification Algorithms for Customer Decisions on Telecommunication Products Using Supervised Learning Kristian Vieri, Jhon; Munandar, Tb Ai; Srisulistiowati, Dwi Budi; Handayani, Dwipa; No’eman, Achmad; Sri Lestari, Tyastuti
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 2 (2023): May - August 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (801.899 KB) | DOI: 10.58776/ijitcsa.v1i2.34

Abstract

Customers are the main goal of all business fields, without customers the company will not be able to continue or compete in the business field it is in, even though the company has brilliant products, if it does not have an increase in the number of customers the business will not be able to develop or even go bankrupt. Therefore, it is necessary to make observations and make applications that are able to predict customers who will subscribe so that companies can predict customers who will subscribe correctly without having to wait for confirmation from customers whose possibilities are still unknown. This can be very useful for any company because companies no longer need to look for random customers where it only takes time to find customers. PT. Telekomunikasi Indonesia with its product (Indihome) which is struggling to compete in the business world in the telecommunications and internet sector. Therefore research and development of this application are carried out so that PT. Indonesian telecommunications can get its customers quickly without having to spend a lot of money and effort. Making this application uses a classification method from machine learning technology based on customer historical data. The classification method has many strong algorithms for predicting variables that have more than 1 label. Some of the algorithms used are Logistic Regression, Random Forest Classifier, Support Vector Machine and Decision Tree which are provided by modules in the python programming language, namely SkLearn. The four algorithms will be tested with data balanced using the Oversampling method from the Smote algorithm to get optimal results in automatically predicting customers.
Co-Authors Dwi Budi Srisulistiowati Dwipa Handayani No’eman, Achmad Sri Lestari, Tyastuti Tb Ai Munandar, Tb Ai