ANALISIS SIMULASI PREDIKSI CUSTOMER CHURN E-COMMERCE MENGGUNAKAN ALGORITMA RANDOM FOREST BERBASIS DATA SINTETIS
DOI:
https://doi.org/10.35794/jmbi.v13i1.67545Abstrak
The high customer churn rate is a critical challenge for the e-commerce industry in Indonesia, with potential losses reaching billions of rupiah per year. This study aims to implement a customer churn prediction system as a proof-of-concept using machine learning algorithms. Given the limited access to private e-commerce data, this study uses a methodological approach with a synthetic dataset consisting of 1000 customer data and 9 key features including tenure, monthly spending, total transactions, support tickets, and last purchase days. Three machine learning algorithms are implemented, namely Logistic Regression, Decision Tree, and Random Forest to classify churn predictions. The results show that Random Forest provides the most stable performance with an accuracy of 87.5%, precision of 86%, recall of 87%, and F1-score of 86%. The decrease in performance compared to the deterministic model indicates that the model was tested on more realistic data conditions and did not experience overfitting to the generative rules
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