Abstract
With the rapid advancement of the telecommunications industry, and competition between telecommunications companies is increasing, companies need to predict their customers to determine the level of customer loyalty. One of them is by analyzing customer data by doing a Customer Churn Prediction. Predicting Customer Churn is an important business strategy for the company. To acquire new customers is much higher cost than retaining existing customers. The ease of operator switching is one of the serious challenges that the telecommunications industry must face. By predicting customer churn, companies can take immediate action to retain customers. To retain existing customers, the company must improve customer service, improve product quality, and must know in advance which customers have the possibility to leave the company. Prediction can be done by analyzing customer data using data mining techniques. In line with this, gathering information from the telecommunications business can help predict whether customer relationships will leave the company. The data used in this study are secondary data and amount to 7.403 data customers. The data has 21 variables. This study proposes to use the ensemble method namely adaboost, xgboost and random forest and compare them. Algorithm is validated through training data and testing data with a ratio of 80:20. From the results we got using python tools, it was found that the adaboost algorithm has an accuracy of 80%.Keywords—accuracy, adaboost, churn prediction, compare model, data mining.
Highlights
With the rapid advancement of the telecommunications industry, and competition between telecommunications companies is increasing, companies need to predict their customers to determine the level of customer loyalty
This study proposes to use the ensemble method namely adaboost, xgboost and random forest and compare them
“Penerapan Data Mining Untuk Menganalisis Penjualan Barang dengan Menggunakan Metode Apriori pada Supermarket Sejahtera Lhoksumawe,” J
Summary
Bidang telekomunikasi telah menjadi salah satu bisnis fundamental di negaranegara maju. Salah satu cara untuk menarik pelanggan adalah dengan menganalisis data pelanggan yang biasanya disimpan di sejumlah besar database perusahaan. Data ini dapat digunakan untuk menganalisis pelanggan mana yang loyal dan churn. Diantara metode data mining yang telah digunakan untuk menganalisis pelanggan adalah random forest dan decision tree [12] tetapi belum mencapai nilai yang sangat baik. Pada penelitian kali ini akan mencoba untuk mendapatkan akurasi yang lebih baik dari penelitian yang dilakukan sebelumnya yang berjudul Telecom Customer Churn Prediction dengan hasil akurasi tertinggi dengan algoritma random forest [12]. Pada penelitian ini akan mencoba menjawab bagaimana pengklasifikasian data yang paling berpengaruh terhadap tingkat churn pada perusahaan telekomunikasi menggunakan metode ensemble yaitu random forest, adaboost dan xgboost serta bagaimana akurasi dari ketiga algoritma tersebut
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