Prediksi Keputusan Pelanggan Menggunakan Extreme Learning Machine Pada Data Telco Customer Churn - Daris Hadyan Tisantri; Randy Cahya Wihandika; Sigit Adinugroho (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
In this modern era many company and institution compete to sell their services like internet and telecommunication that use subscription system to sell their services. Because of that, company must compete via marketing strategy. Main factor for customer to continuously extend their subscription is loyalty. Loyalty have directly proportional with business performance. Because of marketing factor and customer loyalty, many customers changed or stopped their subscription from one and another similar company and makes some company lost their customer and revenue. If company or institution can predict churn, they can anticipate so that customer didn't churn. In this research, the dataset that used for this research is from Kaggle sourced from IBM Sample Data Sets. This dataset consists of 7043 data that have 20 features with two classes yes if the customer churn and no if the customer is not churn. After that, the feature on the dataset that not used will be eliminated with Pearson correlation. After that the data will be trained on Extreme Learning Machine to predict customer will churn or not. Result of this research is the system can get accuracy 76,96%, precision churn 65,45%, precision non churn 78,65%, recall churn 29,38%, recall non churn 94,19%