Implementasi Algoritma Random Forest untuk Prediksi Customer Churn pada Perusahaan Telekomunikasi
DOI:
https://doi.org/10.61124/sinta.v3i3.320Keywords:
Customer Churn, Random Forest, Machine Learning, SMOTE, TelekomunikasiAbstract
Customer churn merupakan permasalahan krusial dalam industri telekomunikasi yang berdampak langsung pada penurunan pendapatan perusahaan. Tantangan utama dalam prediksinya adalah ketidakseimbangan kelas, di mana jumlah pelanggan non-churn jauh lebih besar dibandingkan pelanggan churn. Penelitian ini mengimplementasikan algoritma Random Forest dikombinasikan dengan Synthetic Minority Oversampling Technique (SMOTE) untuk mengatasi permasalahan tersebut. Dataset yang digunakan adalah Telco Customer Churn dari Kaggle, terdiri dari 7.043 data pelanggan dengan 21 atribut. Pra-pemrosesan meliputi pembersihan data dan label encoding, kemudian dataset dibagi dengan rasio 80:20, dan SMOTE diterapkan eksklusif pada data pelatihan. Hasil pengujian menunjukkan model mencapai accuracy 77,15%, precision 55,65%, recall 68,45%, dan F1-score 61,39%. Analisis feature importance mengungkapkan bahwa Contract, OnlineSecurity, dan TechSupport merupakan faktor paling dominan yang memengaruhi churn. Integrasi Random Forest dan SMOTE terbukti menghasilkan model prediksi yang andal sekaligus memberikan wawasan strategis bagi perusahaan dalam merancang program retensi pelanggan yang lebih efektif.
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