Model Machine Learning untuk Klasifikasi Loyalitas Pelanggan Menggunakan Random Forest
DOI:
https://doi.org/10.61132/prosemnasproit.v2i2.202Keywords:
Customer loyalty, churn prediction, Random Forest, machine learning, SMOTEAbstract
This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies
References
Azmi, A. F., & Voutama, A. (2024). Prediksi Churn Nasabah Bank Menggunakan Klasifikasi Random Forest Dan Decision Tree Dengan Evaluasi Confusion Matrix. Komputa : Jurnal Ilmiah Komputer Dan Informatika, 13(1), 111–119. https://doi.org/https://doi.org/10.34010/komputa.v13i1.12639
Baradja, A., & Tjendrowasono, T. I. (2024). Pengaplikasian Deep Reinforcement Q-Learning Untuk Prediksi Perdagangan Valas Otomatis. Jurnal Rekayasa Sistem Informasi Dan Teknologi, 1(3), 190–198. https://doi.org/10.59407/jrsit.v1i3.519
Firmansyah, & Yulianto, A. (2021). Prediksi Customer Churn Pada Bisnis Retail Menggunakan Algoritma Naïve Bayes. Remik: Riset Dan E-Jurnal Manajemen Informatika Komputer, 6(1), 41–47. https://doi.org/http://doi.org/10.33395/remik.v4i1.11196
Gomede, E. (2024). Understanding the Difference Between Algorithms and Models in Machine Learning. Medium.Com. https://medium.com/the-modern-scientist/understanding-the-difference-between-algorithms-and-models-in-machine-learning-71ebacd207fa
Harahap, A. A., Raihan, M., Amani, N., & Andini, P. R. (2023). Comparison of Unsupervised Learning Techniques for Clustering Data on the Number of Villages in Indonesia. Institut Riset Dan Publikasi Indonesia (IRPI). SENTIMAS: Seminar Nasional Penelitian Dan Pengabdian Masyarakat, 1(1), 163–170.
Husain, A., & Jamaluddin, S. R. W. (2025). Pemodelan Data Angka Kematian Bayi Menggunakan Regresi Robust. SAINTEK: Jurnal Sains, Teknologi & Komputer, 1(1), 1–7. https://doi.org/https://doi.org/10.56495/saintek.v1i1.326
Iskandar, M. A., & Latifa, U. (2023). Website Prediksi Customer Churn Untuk Mempertahankan Pelanggan Pada Perusahaan Telekomunikasi. JATI (Jurnal Mahasiswa Teknik Informatika), 7(2), 1308–1316. https://doi.org/https://doi.org/10.36040/jati.v7i2.6639
Jananto, A. (2025). Studi Komparatif Algoritma Klasifikasi Data Mining pada Prediksi Prestasi Siswa Berbasis Data Sosiodemografis. Jurnal Teknik Informatika Unika ST. Thomas (JTIUST), 10(2), 231–242. http://ejournal.ust.ac.id/index.php/JTIUST/article/view/5840
Lubis, A. H. (2025). Imputasi Data: Konsep, Metode, dan Peran Pentingnya dalam Pelatihan Model dan Pengambilan Keputusan. Andrehasudungan.Blog.Uma.Ac.Id. https://andrehasudungan.blog.uma.ac.id/2025/08/26/imputasi-data-konsep-metode-dan-peran-pentingnya-dalam-pelatihan-model-dan-pengambilan-keputusan/
Nurhalizah, R. S., Ardianto, R., & Purwono. (2024). Analisis Supervised dan Unsupervised Learning pada Machine Learning : Systematic Literature Review. Jurnal Ilmu Komputer Dan Informatika (JIKI), 4(1), 61–72. https://doi.org/https://doi.org/10.54082/jiki.168
Pratiwi, F. S., Barata, M. A., & Ardianti, A. D. (2025). Implementasi Metode Smote Dan Random Over- Sampling Pada Algoritma Machine Learning Untuk Prediksi Customer Churn Di Sektor Perbankan. Jurnal Sistem Informasi Dan Informatika (Simika)), 8(1), 87–98. https://doi.org/https://doi.org/10.47080/simika.v8i1.3678
Pritalia, G. L. (2022). Analisis Komparatif Algoritme Machine Learning pada Klasifikasi Kualitas Air Layak Minum. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 2(1), 43–55. https://doi.org/https://doi.org/10.24002/konstelasi.v2i1.5630
Rahma, S. A., & Putri, T. (2025). Klasifikasi Konsumen Berdasarkan Loyalitas Belanja Online Menggunakan Algoritma Random Forest. Jurnal ICT : Information Communication & Technology, 25(1), 81–86. https://doi.org/https://doi.org/10.36054/jict-ikmi.v25i1.304
Ramadhani, D., Soleh, A. M., & Erfiani. (2024). Machine Learning-Based Univariate Time Series Imputation Method for Estimating Missing Values in Non- Stationary Data. Jurnal Matematika, Statistika Dan Komputasi, 21(1), 307–320. https://doi.org/10.20956/j.v21i1.36468
Santoso, P., Abijono, H., & Anggreini, N. L. (2021). Algoritma Supervised Learning Dan Unsupervised. G-Tech Jurnal Teknlogi Terapan, 4(2), 315–318. https://doi.org/https://doi.org/10.33379/gtech.v4i2.635
Seftiani, A. (2024). Pengaruh Kualitas Pelayanan dan Inovasi Produk Terhadap Keunggulan Bersaing Minuman Boba Pattaya [Universitas Jambi]. https://repository.unja.ac.id/id/eprint/69303
Sidik, A. D., & Ansawarman, A. (2022). Prediksi Jumlah Kendaraan Bermotor Menggunakan Machine Learning. Formosa Journal of Multidisciplinary Research (FJMR), 1(3), 559–568. https://doi.org/10.55927/fjmr.v1i3.745
Singh, D. (2025). Customer Engagement and Churn Analytics Dataset. Kaggle. https://www.kaggle.com/datasets/dhairyajeetsingh/ecommerce-customer-behavior-dataset
Sudrajat, W., & Cholid, I. (2023). K-Nearest Neighbor (K-Nn) Untuk Penanganan Missing Value Pada Data Umkm. JRSIT: Jurnal Rekayasa Sistem Informasi Dan Teknologi, 1(2), 54–63. https://doi.org/10.59407/jrsit.v1i2.77
Wahyudi. (2023). studi kasus pengembangan dan penggunaan artificial intelligence (ai) sebagai penunjang kegiatan masyarakat indonesia. indonesian journal on software engineering, 9(1), 28–32. https://doi.org/10.31294/ijse.v9i1.15631
Widodo, A. P., & Setiawan, A. (2025). Systematic Literature Review : Peran Machine Learning dalam Manajemen Sumber Daya Manusia. Optimal: Jurnal Ekonomi Dan Manajemen, 5(4), 650–665. https://doi.org/https://doi.org/10.55606/optimal.v5i4.8089
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Prosiding Seminar Nasional Ilmu Teknik
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
