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Journal : JOIV : International Journal on Informatics Visualization

Optimizing Artificial Neural Network for Customer Churn: Advanced Data Balancing and Feature Selection Hermawan, Aditiya; Wijaya, Willy; Daniawan, Benny
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3064

Abstract

Customers are valuable assets in the dynamic business world. However, service dissatisfaction often leads them to switch to competitors, a phenomenon known as customer churn. In the telecommunications industry, churn poses a significant challenge as it directly impacts revenue and influences other customers within their social networks to do the same. Consequently, predicting churn has become essential, with numerous researchers employing various methods to classify potential churners. This study builds upon prior research that utilized Artificial Neural Networks (ANN) or Deep Learning to predict churn, achieving an accuracy of 88.12%. To improve model performance, this research implements an Artificial Neural Network (ANN) as the primary algorithm, along with Random Over-Sampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE) for data balancing, and three feature selection methods: Minimum Redundancy Maximum Relevance (mRMR), Lasso Regression, and XGBoost. The results demonstrate a 0.38% increase in accuracy compared to previous studies. The finding suggests opportunities for further exploration. Future studies can consider alternative feature selection techniques, such as Wrapper Methods or Heuristic/Metaheuristic approaches, which may produce more optimal feature combinations. Other data balancing methods, such as Undersampling techniques (e.g., Random Undersampling, Tomek Links) or Hybrid Methods (e.g., SMOTE combined with Tomek Links), could be explored to address imbalanced datasets effectively. These approaches are expected to provide better combinations and to improve overall prediction performance, enabling researchers to develop more robust and accurate models for customer churn prediction in subsequent studies.
Co-Authors A Damiyati Abidin Abidin Agus Setiawan Alvin Rahayu Amin Suyitno Andre Sahulata Andri Wijaya Andrie Suak Tiwa Anton Halim Anwan Chailes Aprilyanti, Rina Ardiane Rossi Kurniawan Maranto Arvin Lawistra Benny Daniawan Ceng Giap, Yo Culadi, Rafael Daniel Daniawan, Benny Dera Susilawati Deviastati Putri Sugiarta Karlim Edy edy Edy Edy Edy Edy Ellysha Dwiyanthi Kusuma Eva Eva Evan - Evien Fernando, Albert Gustayo, Teven Halim Wijaya, Ardie Halim, Ardie Hargiani, Fransisca Xaveria Hartana Wijaya Henry Henry Intan Anjali Putri Jelvin Putra Halawa Jessen Laorenza Suwandi Johan Santoso Jowensen, Indrico JUNAEDI Junaedi Junaedi Junaedi Kevin Ivone Sim Kevin kevin Khanti Kusuma Dewi Kumala, Sonya Ayu Kurniawan Maranto, Ardiane Rossi Leonardo Lianata Lianny Wydiastuty Kusuma Lidya Lunardi Luis Alpianto Lunardi, Lidya Maranto, Ardiane Rossi Kurniawan Margaretha Natalya Margita, Santa Mariana Purnamasari Mesakh Septiadi Simijaya michael vernannes marpaung Nandivadhano, Revatta Manggala Nathaniel, Joese Niki Destiandi Oscar Hasan Putra Pannavira Philip Kristy Wijaya Raditya Rimbawan O Raditya Rimbawan Oprasto Rheza Vincentius Riki Riki Riki RIKI RIKI, RIKI Rino Rossi Kurniawan Maranto, Ardiane Rossi Sevtian Ferdian Stanley Ananda Sutopo, Prihantoro Syahdu Suwitno Tia Nurapriyanti Wicaksono, Baghas Budi Willy Wijaya, Willy Wiyono Wydiastuty Kusuma, Lianny Wydiastuty, Lianny Yance Gusnadi Yanti, Lia Dama Yo Ceng Giap Yo Ceng Giap Yuliastati Putri Sugiarta Karlim Yunia Oktari Yusuf Kurnia Yusuf Kurnia, Yusuf