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All Journal Jurnal Ilmu Komputer, Teknologi Dan Informasi
Saudurma Seven Septiana Sidabutar
Universitas HKBP Nommensen Pematangsiantar, Pematangsiantar
Computer Science & IT Control & Systems Engineering Engineering Social Sciences Other

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Analisis Perbandingan Algoritma Random Forest dan Support Vector Machine pada Prediksi Customer Churn Menggunakan IBM Telco Customer Churn Dataset Saudurma Seven Septiana Sidabutar; Ningsih Septi Uli Purba; Wulan Liviana Simbolon; Betharya Tampubolon; Jaya Tata Hardinata
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.377

Abstract

Customer churn is a condition in which customers decide to discontinue the services provided by a company. In the telecommunications industry, a high customer churn rate can reduce company revenue and customer loyalty. Therefore, an accurate prediction method is needed to identify customers who are likely to churn so that preventive strategies can be implemented at an early stage. This study aims to compare the performance of the Random Forest and Support Vector Machine (SVM) algorithms in predicting customer churn using the IBM Telco Customer Churn Dataset. The research stages include data collection, data preprocessing, model development using Orange Data Mining, model evaluation through Test and Score, Confusion matrix, and Receiver operating characteristic (ROC), as well as data visualization using Microsoft Power BI. The results indicate that both algorithms are capable of classifying customer data; however, the Random Forest algorithm achieves better performance than the Support Vector Machine based on the evaluation metrics obtained. Furthermore, data visualization using Microsoft Power BI provides a clearer understanding of customer characteristics and supports the interpretation of the research findings. Therefore, Random Forest is recommended as a more effective algorithm for customer churn prediction in the telecommunications sector.
Co-Authors Betharya Tampubolon Jaya Tata Hardinata Ningsih Septi Uli Purba Wulan Liviana Simbolon