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All Journal Journal of Computer System and Informatics (JoSYC) Transaction on Informatics and Data Science
Sholeha, Selfia Hafidatus
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Prediction of Customer Switching Using Support Vector Machine Method Tholib, Abu; Sholeha, Selfia Hafidatus; Aini, Qurrotu
Transactions on Informatics and Data Science Vol. 1 No. 2 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i2.12277

Abstract

Several studies on predicting customer switching focus on the telecommunications industry and online stores. This research aims to predict customer switching to get the best results; customers are the most critical mass; some companies must provide satisfying services so customer flow decreases. The support vector machine (SVM) method uses machine learning to find a hyperplane based on the SRM principle. A hyperplane is a decision boundary that helps classify data points. SVM stands out for its ability to take input data and make predictions based on its characteristics. This study uses data from Kaggle, structured it, cleaned it, identified patterns and inconsistencies (such as skewness, outliers, and missing values), and built and validated hypotheses. From the data processing, the plot shows the imbalance of data classes between churners and non-churners. This research applies several models where the most significant or best performance value is in the SVM model of 0.7996. The Neural Network model can be trained with better patterns to detect data and achieve high accuracy.
Prediksi Prediksi Perpindahan Pelanggan Pada Toko Online Menggunakan Metode Tree-Based Gradient Boosted Models Sholeha, Selfia Hafidatus; Faid, Mochammad; Yaqin, Moh. Ainol
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5215

Abstract

Customers are a critical asset to a company's success and ensuring their satisfaction is paramount. However, continuous churn can lead to reduced value flowing from customers, potentially jeopardizing a company's competitive advantage. Customer churn, where consumers choose products from other brands, is influenced by various factors such as promotion, price, product availability, and customer satisfaction levels. While much of the research on churn prediction is concentrated in the telecommunications, retail, and banking industries and only a few have conducted churn prediction research on online stores. This research aims to utilize data mining with a focus on machine learning algorithms, especially the tree-based gradient boosted models method that applies XGBoost, LightGBM, and CatBoost models, to predict customer churn in online stores. The research methodology involves data collection, data pre-processing, model selection and training, model evaluation, analysis and results. This research uses several libraries such as pandas library, numpy, matplotlib, and so on. The results of this study show that the XGBoost model achieved the highest accuracy in predicting customer churn, with an ROC curve of 0.66 and an accuracy value of 0.80032. The feature importance analysis highlights the gender variable as an important factor in model performance. This research contributes to improving customer service, minimizing churn, and ultimately increasing company profitability in the online store sector. Suggestions for future research include expanding data sources, testing with more evaluation metrics, exploring additional churn factors and comparing with other prediction methods for validation.
Co-Authors Abu Tholib Mochammad Faid Qurrotu Aini, Qurrotu Yaqin, Moh. Ainol