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Contact Name
Ai Munandar
Contact Email
ijitcsa@gmail.com
Phone
+62+6282111152015
Journal Mail Official
ijitcsa@gmail.com
Editorial Address
International Journal of Information Technology and Computer Science Applications (IJITCSA) Sekretariat Jejaring Penelitian dan Pengabdian Masyarakat (JPPM) : Ranau Estate Blok D.3, Kel. Panggungjati, Kp. Pantogan Kec. Taktakan - Kota Serang, Provinsi Banten, e-mail : jitcsa@jejaringppm.org web : www.jejaringppm.org
Location
Kota serang,
Banten
INDONESIA
International Journal of Information Technology and Computer Science Applications (IJITCSA)
ISSN : 29643139     EISSN : 29855330     DOI : https://doi.org/10.58776/ijitcsa.v1i2
he Journal of Information Technology and Computer Science Applications (JITCSA) is an information technology and computer science publication. Applications from both fields for solving real cases are also welcome. JITCSA accepts research articles, systematic reviews, literature studies, and other relevant ones. Several fields of science that are the focus of JITCSA include information technology and the like, computer science fields, including artificial intelligence, data science, data mining, machine learning, deep learning, and the like. IJITCSA is published three times a year, in January, May, and September. The first issue in January 2023 had eight articles. Focus and Scope International Journal of Information Technology and Computer Science Applications includes scholarly writings on scientific research or review, pure research, and applied research in the field of computer science, information systems, and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. Information systems System Software Artificial Intelligence Computer Architecture Distributed Systems System & Software Engineering Genomics & Bioinformatics Internet and Web AI & Expert systems Software Process and Life Cycle Database Systems Software Testing & Quality assurance Bioinformatics Information Technology Implementation Computing Languages & Algorithms E-commerce & M-Commerce Computer Networks & Communications Computing Systems Control Systems & Engineering Systems Engineering System Security Digital Forensics Data Mining & Machine Learning Data Modeling
Articles 2 Documents
Search results for , issue "Vol. 4 No. 1 (2025): January - April 2026" : 2 Documents clear
Enhancing Association Rule Mining with Metaheuristic Parameter Optimization: A Transactional Data Analysis in Micro-Enterprise Context Primanda, Ferdy Hartanto Primanda; Munandar, Tb Ai; Ramdhania, Khairunnisa Fadhilla
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 1 (2025): January - April 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v4i1.204

Abstract

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. Zahra, Nurul; Priatna, Wowon; Lestari, Tyastuti Sri
International Journal of Information Technology and Computer Science Applications Vol. 4 No. 1 (2025): January - April 2026
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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.

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