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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Sufajar Butsianto
Universitas Pelita Bangsa, Bekasi
Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : bulletin of computer science research

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Implementasi Algoritma Apriori dalam Menemukan Pola Asosiasi pada Data Penjualan Produk Retail Sufajar Butsianto; Candra Naya; Anggi Muhammad Rifa'i
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.731

Abstract

This study aims to implement the Apriori algorithm in finding association patterns in retail product sales data, using the Association Rule Mining approach. Evaluating the ruler or association rules formed based on the support, confidence, and lift parameters, in finding association patterns in retail product sales data with a focus on the relationship between product categories. The data used consists of 500 sales data as sample data and 5,972 transactions as test data. The data mining process was carried out on the main product categories such as Milk, Coffee, Tea, Drinks, Detergent, and Biscuit/Snacks, to find association rules that appear simultaneously with the Bulk Products category in one transaction time. The minimum support parameter was set at 0.02 and the minimum confidence was set at 0.5. By using these parameters, several significant association rules were obtained. One of the strongest rules shows that if products in the Milk, Coffee, Tea, Drinks, Detergent, and Biscuit/Snacks categories are purchased together, then there is a 64.3% probability (confidence) that products in the Bulk Products category are also purchased at the same time. The support value of this rule reached 3.8%, and the lift value was 1.49, indicating a positive association and not a coincidence. Evaluation of the test data showed that this pattern was consistently found across 5,972 transactions, with a repeatability rate of 61.7%. The results of this study demonstrate that the Apriori algorithm is effective in identifying consumer purchasing patterns that can be utilized for product placement strategies, bundling offers, and inventory planning in retail management.
Perbandingan Kinerja Naïve Bayes dengan dan Tanpa SMOTE untuk Klasifikasi Gangguan Kecemasan Mahasiswa pada Data Tidak Seimbang Nurhadi Surojudin; Sufajar Butsianto; Andri Firmansyah
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.1021

Abstract

Anxiety disorders are one of the most common mental health problems experienced by university students and may affect learning concentration and academic performance. The analysis of psychological survey data using machine learning techniques can support early detection of student anxiety conditions. However, one of the main challenges in mental health data analysis is the presence of class imbalance within the dataset. This study aims to analyze the effect of applying the Synthetic Minority Oversampling Technique (SMOTE) on the performance of the Naïve Bayes algorithm for multi-class classification of student anxiety levels, which are categorized into three classes: No Stress, Eustress, and Distress. The dataset used in this research was obtained from student questionnaire data and underwent several preprocessing steps including data cleaning, feature transformation, and dataset splitting using the hold-out method with a ratio of 80% training data and 20% testing data. Model performance was evaluated using a confusion matrix with evaluation metrics including accuracy, precision, recall, and F1-score. The results show that the Naïve Bayes model without SMOTE achieved an accuracy of 0.84, precision 0.78, recall 0.41, and F1-score 0.54. After applying SMOTE, the model achieved an accuracy of 0.82, precision 0.74, recall 0.69, and F1-score 0.71. These results indicate that SMOTE improves the model's ability to detect minority classes in multi-class classification problems, although a slight decrease in overall accuracy is observed.
Co-Authors Andri Firmansyah Anggi Muhammad Rifa'i Arif Siswandi Arya Rifaldi Baharudin Asep Supriyanto Candra Naya Nurhadi Surojudin