Profit-Based HUI-Miner for Discovering Consumer Shopping Patterns in a Retail Store

2026-07-28T08:36:24+00:00

Retail transaction logs often reveal products that are frequently purchased together, but frequency alone does not show their economic contribution. This study applies a profit-oriented High Utility Itemset approach using HUI-Miner to identify valuable shopping patterns at YSL Grocery Store. The analysis followed CRISP-DM on 266,394 sales records from 2022. After attribute selection and missing-value removal, 251,225 records containing transaction, item, quantity, and price were processed. Item utility was represented by quantity multiplied by selling price, while the mining stage used a minimum utility of IDR 5,000,000, minimum support of 2%, and minimum confidence of 10%. Positive associations were retained when lift exceeded 1. The procedure produced 248 frequent itemsets, 77 confidence-qualified rules, 64 positive-lift rules, and 23 rules that satisfied all criteria. The strongest association linked two Sedaap instant-noodle variants with a lift of 3.26. The findings also show that support is not proportional to total utility: some cross-category combinations generated substantially greater utility despite lower occurrence. Therefore, retail decisions should combine utility, support, confidence, and lift when prioritizing shelf placement, bundles, promotions, and stock.

Detection of Disease in Platycerium Ornamental Plant Leaves Using Yolo 12

2026-07-28T08:35:53+00:00

Platycerium is an epiphytic ornamental plant with high aesthetic and economic value, thus requiring proper care. Identifying Platycerium leaf diseases based on visual symptoms often requires precision and experience, thus necessitating an image-based automated approach. This study aims to develop a Platycerium leaf disease detection model using the deep learning-based YOLO method. The model was developed using Kaggle Notebook with P100 GPU support. The dataset used consisted of three disease classes, namely Bacterial Leaf Spot, Fern Scale, and Rizoctonia Blight. Model training was carried out with variations in the number of epochs of 50, 75, and 100 epochs, and evaluated using the Precision, Recall, and Mean Average Precision (mAP) metrics. The results showed that training with 50 epochs gave the best results with a mAP50 value of 0.953 and mAP50–95 of 0.577. Testing using test data and data outside the dataset showed that the model was able to detect Platycerium leaf disease in test images by displaying bounding boxes and class labels. Based on these results, the YOLO model developed can be used as an image-based approach for detecting Platycerium leaf disease and can be further developed.

Jurnal Galaksi

Profit-Based HUI-Miner for Discovering Consumer Shopping Patterns in a Retail Store

Detection of Disease in Platycerium Ornamental Plant Leaves Using Yolo 12