Analisis Perbandingan Algoritma Random Forest dan Support Vector Machine pada Prediksi Customer Churn Menggunakan IBM Telco Customer Churn Dataset
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Saudurma Seven Septiana Sidabutar
*
Universitas HKBP Nommensen Pematangsiantar, Pematangsiantar,
Indonesia
- Ningsih Septi Uli Purba Universitas HKBP Nommensen Pematangsiantar, Pematangsiantar, Indonesia
- Wulan Liviana Simbolon Universitas HKBP Nommensen Pematangsiantar, Pematangsiantar, Indonesia
- Betharya Tampubolon Universitas HKBP Nommensen Pematangsiantar, Pematangsiantar, Indonesia
- Jaya Tata Hardinata Universitas HKBP Nommensen Pematangsiantar, Pematangsiantar, Indonesia (*) Corresponding Author
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.
References
N. Januari, I. K. M. Bili, I. W. Sudiarta, M. Yuditia, N. K. A. Rosdiana, and P. Rafiana, “Analisis dan Prediksi Customer Churn pada Platform Streaming Berbasis Langganan Menggunakan Metode Random Forest membicarakan suatu layanan setelah jangka waktu tertentu . Dalam dunia bisnis , churn layanan . Data menunjukkan durasi akses , frekuensi penggunaan , tingkat konsumsi , dan layanan . Mengumpulkan data pengguna yang memengaruhi penggunaan layanan . Data ini,” no. November 2025, 2026.
D. M. Putri, R. D. Mulyani, and F. Mawarni, “Pemanfaatan Data Mining untuk Klasifikasi Customer Churn Menggunakan Algoritma Random Forest dalam Mendukung Strategi Bisnis,” J. Data Sci. Informatics Eng., vol. 1, no. 2, pp. 63–68, 2026, doi: 10.64803/jodsie.v1i2.32.
H. D. Syaputra, M. S. Wisnubroto, F. Farid, H. S. Ramadhan, and D. R. Luthfi, “Penerapan Metode Ensemble Learning Untuk Prediksi Churn Customer Pada Layanan Telekomunikasi,” J. RESTIKOM Ris. Tek. Inform. dan Komput., vol. 8, no. 1, pp. 515–525, 2026.
M. I. Fauzi, N. Fitriyah, and M. Karimah, “Prediksi Customer Churn Menggunakan Decision Tree dan Random Forest dengan Pendekatan SMOTE untuk Mendukung Customer Intelligence pada Industri Telekomunikasi,” J. Inf. Syst. Bus. Technol., vol. 2, no. 3, pp. 785–794, 2026.
A. Sikri, R. Jameel, S. M. Idrees, and H. Kaur, “Enhancing customer retention in telecom industry with machine learning driven churn prediction,” Sci. Rep., vol. 14, no. 1, pp. 1–13, 2024, doi: 10.1038/s41598-024-63750-0.
M. Z. Alotaibi and M. A. Haq, “Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods,” Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14572–14578, 2024, doi: 10.48084/etasr.7480.
Oktaviana Putri Agung, Fairuza Mayla Faizal, and Irenia Mascharenhas, “Penerapan Algoritma Random Forest Dalam Prediksi Customer Churn Untuk Mendukung Strategi Retensi Pelanggan,” J. Ris. Tek. Komput., vol. 3, no. 2, pp. 90–96, 2026, doi: 10.69714/84q65g10.
L. N. Wakhidah, A. K. Zyen, and B. B. Wahono, “Evaluation of Telecommunication Customer Churn Classification with SMOTE Using Random Forest and XGBoost Algorithms,” J. Appl. Informatics Comput., vol. 9, no. 1, pp. 89–95, 2025, doi: 10.30871/jaic.v9i1.8740.
J. I. Komputer et al., “Perbandingan Algoritma Random Forest dan Support Vector Machine dalam Memprediksi Customer Churn pada Perusahaan Telekomunikasi,” vol. 9, no. 99, pp. 1–10.
M. Basri, “A Comparative Study : Predicting Customer Churn in Banking Using Logistic Regression & Random Forest,” Ultim. J. Tek. Inform., vol. 17, no. 1, pp. 72–81, 2025, doi: 10.31937/ti.v17i1.4075.
N. Y. Nhu, T. Van Ly, and D. V. Truong Son, “Churn prediction in telecommunication industry using kernel Support Vector Machines,” PLoS One, vol. 17, no. 5 May, 2022, doi: 10.1371/journal.pone.0267935.
D. A. Kusuma, A. R. Dewi, and A. R. Wijaya, “Perbandingan Random Forest dan Convolutional Neural Network dalam Memprediksi Peralihan Pelanggan,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 10, no. 2, pp. 186–194, 2025, doi: 10.14421/jiska.2025.10.2.186-194.
Y. Setiawan, A. I. Hadiana, and F. R. Umbara, “Customer Churn Prediction Using the Random Forest Algorithm,” JIKO (Jurnal Inform. dan Komputer), vol. 7, no. 3, pp. 209–216, 2024, doi: 10.33387/jiko.v7i3.8711.
Aqilla Nurul Hasanah Aqilla and Hafiyyan Putra Pratama, “Klasifikasi Spesies Bunga Iris Menggunakan Logistic Regression Dan Support Vector Machine,” J. Komput. Teknol. Inf. Sist. Komput., vol. 5, no. 1, pp. 704–713, 2026, doi: 10.62712/juktisi.v5i1.1096.
P. Meilina, “Penerapan Data Mining dengan Metode Klasifikasi,” J. Teknol. Univ. Muhammadiyah Jakarta, vol. 7, no. 1, pp. 11–20, 2015, [Online]. Available: jurnal.ftumj.ac.id/index.php/jurtek
M. D. N. Alif and N. F. Fahrudin, “Performance Analysis of Oversampling and Undersampling on Telco Churn Data Using Naive Bayes, SVM And Random Forest Methods,” E3S Web Conf., vol. 484, 2024, doi: 10.1051/e3sconf/202448402004.
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