PREDIKSI CHURN PELANGGAN INDUSTRI TELEKOMUNIKASI MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK BERBASIS STREAMLIT - Ningsih, Nilawati; Iskandar, Nur Aida Saskiya; Rizqiyah, Shofiatur; Sudriyanto, Sudriyanto (Fakultas Sains dan Teknologi, Universitas Ibrahimy, 2025)
Telecommunications companies face a major challenge in retaining customers as the cost of acquiring new customers is much higher than retaining existing customers. Customer churn, or the tendency of customers to stop using a service, can cause significant losses to the company. Customer churn prediction using Machine Learning techniques is crucial to address this issue. This research uses a Streamlit-based Artificial Neural Network (ANN) algorithm to predict customer churn in the telecommunications industry. Inspired by how the human nervous system works, ANN can learn complex customer data patterns, such as tenure, contract type, and monthly fee, resulting in more accurate predictions. Based on the research results, the Streamlit-based ANN method achieved 98% accuracy, higher than the previous method. However, the high accuracy indicates the potential for overfitting, so further testing with larger and more diverse datasets is needed to ensure better generalization. This model is expected to help telecommunication companies identify potentially churning customers and improve customer retention strategies effectively.