Application Of K-Means Clustering In Grouping Customer Preferences For K-Pop Albums And Merchandise

2025-07-14T14:18:51+07:00

The increasing popularity of K-Pop in Indonesia is particularly in the purchase of physical products. THJMINE Store faces challenges in inventory management and promotional strategies due to the lack of product grouping for albums and merchandise. This study applies the K-Means Clustering algorithm to 110 sales transaction data from July 2022 to January 2025. The method used in this study is the CRISP-DM approach, which consists of the following stages: business understanding, data understanding, data preparation, modeling, and evaluation discussion. The result of the study shows that the K-Means algorithm successfully formed three clusters with customer classification: loyal customers (cluster 0), general customers (cluster 1), and premium or collector customers (cluster 2). The model evaluation results in a DBI score of 0.6342, indicating good cluster quality. These clustering results can help THJMINE Store understand customer segmentation, develop more targeted marketing strategies, and improve inventory management efficiency.

Comparing Holt-Winters Variants Accuracy in Forecasting Indonesia LQ45 Stock Prices

2026-04-27T12:21:57+07:00

This study applies the Holt–Winters method, an exponential smoothing approach incorporating level, trend, and seasonal components, to compare the predictive accuracy of four variants (multiplicative, additive, OR, and average) of Holt-Winter Method in forecasting stock prices of companies listed in the LQ45 index. The dataset consists of stock prices from 2016–2021 for training and January–February 2022 for testing, with forecasting accuracy evaluated using Mean Absolute Percentage Error (MAPE), visualized through boxplots, and assessed using the nonparametric Kruskal–Wallis test. The Holt–Winters computations were performed using Microsoft Excel, while boxplot visualization and the Kruskal–Wallis test were conducted using the R programming language. The results indicate significant differences in predictive performance among the four methods with p-value = 0.04059 in Kruskal-Wallis test. The Additive Holt–Winters method achieves the best performance with the lowest MAPE, while the multiplicative method performs the worst. Among LQ45 stocks, INDF records the lowest forecasting error (1.6799%), whereas TPIA exhibits the highest (83.0783%). These results suggesting that the additive Holt–Winters method is more suitable for forecasting LQ45 stock prices under the observed conditions

International Journal of Information Technology and Computer Science Applications

Application Of K-Means Clustering In Grouping Customer Preferences For K-Pop Albums And Merchandise

Comparing Holt-Winters Variants Accuracy in Forecasting Indonesia LQ45 Stock Prices