Penerapan Algoritma C4.5 untuk Prediksi Pola Pembelian Pelanggan pada Dataset Transaksi Retail
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Abstract
The increasing volume of sales transaction data has driven the need for data analysis to uncover patterns that are beneficial for companies. This study aims to classify product sales transaction data using data mining techniques in order to identify sales patterns based on product categories. The classification process is conducted with the assistance of RapidMiner Studio, encompassing data preprocessing, model construction, and evaluation of classification performance. Model evaluation is carried out using the Confusion Matrix with accuracy as the evaluation parameter. The results indicate that the classification model achieves an accuracy rate of 42.67%. These findings show that the model is able to correctly classify a portion of the data, but still has limitations due to the complexity and quality of the transaction data. Therefore, optimization at the data preprocessing stage as well as the selection of more relevant features are required to improve the model’s accuracy in future research
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