Enhancing Association Rule Mining with Metaheuristic Parameter Optimization: A Transactional Data Analysis in Micro-Enterprise Context

Ferdy Hartanto Primanda Primanda, Tb Ai Munandar, Khairunnisa Fadhilla Ramdhania — Wed, 25 Feb 2026 00:00:00 +0700

Nasi Uduk Mama Ipan is a micro-enterprise that conducts sales through both offline and online platforms. However, only online transaction data is available in analyzable form, while the owner lacks the knowledge to process it. This situation highlights the urgency of leveraging data mining techniques to uncover hidden patterns that can inform effective promotional strategies. This study aims to apply association rule mining using Apriori and FP-Growth algorithms, enhanced through metaheuristic-based hyperparameter tuning, to extract meaningful product bundling insights from transactional data. The research begins with data preprocessing, which involves eliminating irrelevant columns and transforming transactional records into a binary format. Four metaheuristic algorithms—Genetic Algorithm, ACO, PSO, and SA—are employed to determine optimal support and confidence values for both Apriori and FP-Growth. The modeling phase is conducted using Python with the mlxtend.frequent_patterns library, with rules filtered using a lift ratio threshold above 1. Results show that both Apriori and FP-Growth algorithms produce identical bundling recommendations using parameters derived from the Genetic Algorithm. Apriori performs faster, while FP-Growth is more memory-efficient. This study demonstrates that combining association rule mining with metaheuristic optimization can effectively support MSMEs in making data-driven marketing decisions.

Public Sentiment Analysis on the Service Quality of PT PLN on X Using Naïve Bayes and K-Nearest Neighbor Algorithms.

Nurul Zahra, Wowon Priatna, Tyastuti Sri Lestari — Wed, 25 Feb 2026 00:00:00 +0700

Quality services since electricity is a primary public need. However, numerous complaints still highlight PLN’s lack of responsiveness, especially on the X platform (formerly Twitter). This study aims to analyze public sentiment toward PLN’s service quality expressed on X and compare the performance of the Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in classifying sentiments into positive, negative, and neutral categories. The research employs the Knowledge Discovery in Databases (KDD) approach, involving data collection through tweet scraping using Tweet-Harvest, preprocessing (case folding, tokenizing, filtering, stemming), transformation with TF-IDF weighting, and data mining using Naïve Bayes and KNN. Evaluation through a confusion matrix shows that Naïve Bayes achieved an accuracy of 87%, outperforming KNN with an accuracy of 86%. These findings provide insights for PLN to better understand public perception and serve as a reference for future sentiment analysis research using machine learning.

International Journal of Information Technology and Computer Science Applications

Enhancing Association Rule Mining with Metaheuristic Parameter Optimization: A Transactional Data Analysis in Micro-Enterprise Context

Public Sentiment Analysis on the Service Quality of PT PLN on X Using Naïve Bayes and K-Nearest Neighbor Algorithms.