Identification of Key Factors in Children's Toy Product Marketing Strategy through Entropy and Gain Analysis
DOI:
https://doi.org/10.35870/ijsecs.v5i3.5690Keywords:
Data Mining, C4.5 Algorithm, WEKA, Product Sales, Decision TreeAbstract
This study aims to analyze the factors influencing product sales success using the C4.5 algorithm data mining method implemented through the WEKA application. The research data consists of 65 instances with six main attributes, namely introduction, durability, price, size, quality, and description. The testing process is carried out using the 10-fold cross validation method to obtain an accurate classification model. The analysis results show that the Price attribute has the highest information gain value ±0.764, so it is designated as the root of the decision tree. Low prices supported by long product durability proved to be the most dominant combination in increasing sales. Conversely, high prices tended to decrease sales levels even though supported by good quality. The resulting classification model has an accuracy of 83.07%, with 54 data correctly classified out of a total of 65 data. These calculation results indicate that consumers are more sensitive to price than quality, so a marketing strategy that emphasizes competitive pricing with guaranteed product durability is the most effective approach to increase purchasing interest. This research is expected to contribute to business decision making, especially in determining product sales strategies in a competitive market.
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Copyright (c) 2025 Siti Aliyah, Efani Desi, Mas Ayoe Elhias Nst, Enni Maisaroh, Fitri Pranita Nasution
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