Articles

Consumer Preferences for Purchasing Local Fruits at The Farmers Market Supermarket in Palembang City

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Fruit consumption in Palembang City remains below the World Health Organization (WHO) recommendation and has not reached an optimal level. Meanwhile, the increasing availability of imported fruits creates competition and affects consumer preferences toward local fruits. This study aims to identify the attributes influencing consumer preferences, analyze the dominance of physical and non-physical attributes, and examine the effect of imported fruit presence. A quantitative survey was conducted involving 100 respondents selected through accidental sampling. Data were analyzed using the Fishbein multi-attribute model, multiple linear regression, and path analysis. The results show that freshness, taste, and price are the main attributes shaping consumer attitudes. The attitude score (Ao) is categorized as high (101.167 or 67.44%), indicating that stronger positive attitudes are associated with higher consumer preferences for local fruits. Regression analysis reveals that product attributes significantly influence preferences, with physical attributes as the dominant factor. Meanwhile, the presence of imported fruits does not have a significant effect. These findings indicate that consumer preferences are mainly driven by product attributes and attitudes, suggesting that improving the quality of local fruits is essential to enhance their competitiveness.

Behavior-Based Purchase Intent Prediction in E-Commerce: A Machine Learning Approach

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This study investigates the use of machine learning to predict user purchase intentions based on behavioral data in a multi-category e-commerce platform. By analyzing seven months of user interaction logs—comprising product views, cart additions, and purchases—the research applies feature engineering to generate variables such as event weekday, product category levels, session activity count, and cart-to-view ratios. Four classification models were developed and evaluated: logistic regression, decision tree, random forest, and gradient boosting. Among these, the Random Forest algorithm outperformed the others, achieving the highest accuracy and F1-score, effectively balancing precision and recall. The results demonstrate that machine learning can reliably predict purchase intent and support more targeted marketing, personalized recommendations, and improved conversion strategies in e-commerce environments.