Pengunaan Data Minig Untuk Mengidentifikasi Pelanggan Beresiko Tinggi Dalam Penjualan Mengunakan Algoritma Decition Tree C4.5
Year: 2025
Type: article
Abstract: In the competitive world of business, identifying high-risk customers is critical to minimizing churn rates and increasing profitability. This research uses data mining techniques using the C4.5 decision tree algorithm to classify customers based on their churn risk. The research stages include data collection, cleaning, data processing, as well as dividing the data into training and testing sets. The implementation of this algorithm was carried out using RapidMiner software, which facilitates customer clustering and predicting behavior based on historical attributes. The evaluation results show the model has an accuracy of 74.59%, with precision and recall indicating the model's ability to identify high-risk customers. Thus, the Decision Tree C4.5 algorithm is proven to be effective in supporting decision making for customer churn risk mitigation strategies.
Source: Jurnal Ilmiah Multidisiplin Ilmu
DOI: 10.69714/s91z1k09
Authors: Zuhrian Nur Saputra, Zaehol Fatah
Institution: Universitas Ibrahimy
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4407064270