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International Journal of Information Technology and Computer Science Applications (IJITCSA)
Published by Jejaring Penelitian dan Pengabdian Masyarakat
ISSN : 29643139     EISSN : 29855330     DOI : https://doi.org/10.58776/ijitcsa.v1i2
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering
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
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 63 Documents
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Comparative Analysis of K-Means and Hierarchical Clustering for Regional Welfare Disparity Identification in West Java Province Muhamad Dani Yusuf; Tb Ai Munandar; Khairunnisa Fadhilla Ramdhania
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.213

Abstract

This study aims to cluster regencies/cities in West Java Province based on public welfare indicators using the K-Means Clustering and Hierarchical Clustering methods. The data used includes health, economic, population density, and average length of schooling indicators in 2023. Cluster quality evaluation was performed using the silhouette score. The results show that K-Means Clustering with five clusters yields the highest silhouette score of 0.219. For comparison, Hierarchical Clustering with the Ward Linkage method and eight clusters was chosen, having a silhouette score of 0.202, which is the largest among other Hierarchical Clustering methods. The identification of each cluster's characteristics in K-Means reveals areas with multidimensional challenges (Cluster 1), industrial areas with unemployment issues (Cluster 2), areas with high stunting prevalence despite good access to basic facilities (Cluster 3), densely populated urban areas with good welfare but high unemployment (Cluster 4), and areas with very high health complaints and low welfare (Cluster 5). K-Means clusters (except Cluster 4) tend to have a low average length of schooling, below 12 years. Consistency in cluster patterns was found between K-Means and Ward Linkage, especially in advanced urban areas and areas with multidimensional welfare challenges in southern West Java. These findings are expected to serve as a reference for the government and policymakers in formulating more targeted and effective development strategies.
The Adoption of Analytics in Handling Netflix’s Business Challenges Amani Bunga Cahya; Orked Pavitra; Dharma Fahim Jamal
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.222

Abstract

Text analytics is a combination of various kinds of text analysis that combines a set of linguistic, statistical and machine learning that extract the meaning out of the text. For example, the analyzing of customer reviews written by Netflix’s customers can be used to find out common patterns and trends that happen around the business for better action-taking, customer experience, and new business models to be built in the future. By doing text analytics, the text data can be grouped with the purposes of creating word frequency distribution (word cloud) and sentiment analysis. Whereas web analytics is referring to the process of collecting data from website through data crawling before processing it into useful info to improve the website experience by using social media analytics (Saini et al., 2022). To find out ways to aid in the formulation of marketing strategies, clustering techniques have been used. The clustering techniques will include behavioral segmentation that focus on human behavior, demographic segmentation that focuses on age, and occupation, and psychographic segmentation that focus on human’s psychological characteristics such as interest, personalities, and so on.
An Overview of Business Intelligence Framework for Sentiment Analysis of United Airlines Using Data from Social Media Marvin Imbin De Castro
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.224

Abstract

Understanding customer sentiment in real time has become increasingly critical for service-oriented industries, particularly airlines operating within highly competitive and socially sensitive environments. This study proposes an integrated Business Intelligence (BI) framework for sentiment analysis of United Airlines using social media data sourced from Twitter. The framework aims to transform large volumes of unstructured, high-velocity text data into actionable insights that support informed decision-making, customer experience enhancement, and brand reputation management. The proposed architecture incorporates sequential analytical components including data ingestion, preprocessing, natural language processing, machine learning–based sentiment classification, and BI-driven visualization. Modern text analytics techniques such as tokenization, lemmatization, and vectorization are applied to prepare textual content for polarity detection, while supervised learning algorithms are evaluated to classify sentiment into positive, negative, and neutral categories. The study outlines the rationale for adopting a scalable, cloud-compatible architecture that supports both batch and stream processing to accommodate the dynamic nature of social media data. Key implementation challenges—such as handling noisy and ambiguous text, managing evolving linguistic patterns, overcoming API rate limitations, and ensuring data quality—are examined. The paper further discusses best practices to mitigate these challenges, including robust data-cleaning pipelines, periodic model retraining, careful feature engineering, and the incorporation of governance principles for ethical data use. The results demonstrate that integrating sentiment analytics within a BI context enables organizations such as United Airlines to monitor customer perceptions more effectively and respond proactively to emerging issues. The framework provides a practical foundation for organizations seeking to operationalize social media analytics for strategic and operational decision support.
Integrating Structured and Unstructured Data for Enhanced Marketing Intelligence through Text Mining and Business Analytics Sabreen Hashim Salman
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.226

Abstract

In the digital era, the rapid growth of social media and online platforms has led to an explosion of unstructured textual data that holds significant business value. Traditional marketing strategies, once reliant on structured data such as demographics and purchase history, now benefit from insights derived from text analytics and sentiment analysis. This paper explores the integration of structured and unstructured data to strengthen marketing intelligence and customer segmentation. By utilizing text mining techniques and Natural Language Processing (NLP), unstructured data such as customer reviews and comments can be analyzed to extract sentiments, identify emerging trends, and refine customer relationship strategies. The study proposes an integrated framework that combines data extraction, transformation, and loading (ETL) processes with a data warehouse system for unified analysis. Using clustering algorithms such as K-Means and visualization tools, insights into customer behavior, preferences, and market segmentation are revealed. The paper also discusses the challenges of handling multilingual and context-dependent text, ethical and privacy considerations, and the technical architecture necessary for business intelligence implementation. Findings suggest that effective integration of textual analytics with structured data can lead to more informed decision-making, improved marketing strategies, and stronger customer engagement.
Complications in healthcare integration models and correlated data infrastructure proposition Ohmar Shiraz Arfeen; Rauf Shahzad Shahrin; Ashraf Zeeshan Ahmad
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.228

Abstract

In healthcare systems, proper data integration models are necessary in order to provide swift treatment for patients. Without integrity and proper management of patient data, it can result in losing many lives due to unwanted delays in getting the necessary data. This study aims to solve this problem by looking at different data-related technological perspectives and discussing which is best suited for the healthcare sector. Multiple papers on different technological perspectives are reviewed to identify the underlying problems and how they can be tackled individually without getting drawbacks in return. Most impactful problems are highlighted and discussed extensively. The findings show that a data warehouse is the most viable option for tackling the highlighted problems due to its highly centralized infrastructure and data consistency. Elaborations are made on the viability of a data warehouse and how it can help healthcare systems in terms of effective data management.
NeoCare: Telehealth System with Intelligent Notification for Neonatal Care Tb Ai Munandar; Tyastuti Sri Lestari; Achmad Noeman
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.239

Abstract

Neonatal mortality in low- and middle-income countries remains high, partly because early physiological deterioration is detected late and continuous monitoring is limited outside specialized units. To address this gap, this study presents NeoCare: Telehealth System with Intelligent Notification for Neonatal Care, a multi-actor platform that integrates neonatal data management, vital-sign monitoring, and machine-learning–based alerts. The research followed a software engineering approach comprising stakeholder and context analysis, requirements engineering, clinical data acquisition, system and database design, intelligent notification model design, and prototype implementation. Retrospective neonatal records from two Indonesian referral hospitals were used to characterize heterogeneous and homogeneous clinical populations and to inform the design of classification features for vital-sign–based risk assessment. NeoCare is realized as a layered architecture with sensor, device, communication, processing-intelligence, and application layers. The prototype includes web and mobile interfaces tailored to four actor groups: hospital administrators, doctors, midwives, and parents. Administrators manage users, hospitals, vital-sign data, and machine-learning models while supervising alert output. Doctors and midwives access dashboards that display neonatal lists, detailed histories, trend graphs, and consultation management, supporting triage and longitudinal follow-up. Parents use a simplified mobile interface to view their baby’s status, monitor vital-sign trends, receive alerts, and schedule consultations. The system embeds an intelligent notification mechanism that flags abnormal patterns and presents them through color-coded indicators and concise messages. The results demonstrate the technical feasibility and coherence of a role-based, data-driven telehealth platform for neonatal care, providing a solid foundation for future work on clinical validation, device integration, and large-scale deployment.
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
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. Nurul Zahra; Wowon Priatna; Tyastuti Sri Lestari
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.
Data Infrastructure Application in Education: An Integrated Architecture for Secure Learning Analytics and Student Performance Prediction Dinesh Pranav Mukerjea
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.v4i1.245

Abstract

Data infrastructure has become a strategic backbone of contemporary education because digital learning environments continuously generate student traces that can be transformed into actionable evidence for teaching, advising, and institutional planning. Yet the practical value of educational data depends on much more than storage capacity. Institutions must integrate heterogeneous sources, manage raw and curated data simultaneously, enforce privacy constraints, and deliver analytics outputs that are operationally useful and ethically defensible. This study develops a layered educational data infrastructure architecture that connects raw learning data, extract-transform-load processes, governance mechanisms, curated analytics repositories, and machine-learning services. This paper includes a reproducible empirical evaluation using the real xAPI-Edu-Data benchmark collected from the Kalboard 360 learning management environment. Three machine-learning models are compared under a common preprocessing pipeline, and an ablation analysis quantifies the incremental value of integrated behavioral, parental, and contextual features. The best-performing model achieves a test macro-F1 of 0.797 and a macro one-vs-rest ROC-AUC of 0.919, while the ablation study shows that the full integrated feature set clearly outperforms demographic-only and behavior-only alternatives. The paper contributes structured architecture, mathematical formalization of integrated learning analytics, and empirical evidence that richer, better-governed data pipelines produce more useful predictive signals for educational decision support.
Application of data warehouse and OLAP processes for retail analytics Victorio Palben Medel
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.v4i1.244

Abstract

Retail organizations increasingly rely on heterogeneous operational platforms, including point-of-sale systems, customer relationship management applications, cloud data stores, and locally administered databases. Although these platforms are valuable for transaction processing, they often generate fragmented, duplicated, and semantically inconsistent data that constrain enterprise reporting, forecasting, and customer intelligence. This paper substantially extends a conceptual SwiftMart case into a full design-and-evaluation study of a retail data warehouse and Online Analytical Processing (OLAP) framework. The proposed artifact combines a Kimball-style dimensional architecture, a governed extract-transform-load (ETL) pipeline, conformed dimensions, and materialized OLAP summaries for managerial analytics. To ground the case empirically, the framework is evaluated using the open-access UCI Online Retail dataset, which contains 541,909 transaction records from a UK-based online retailer covering 1 December 2010 to 9 December 2011. The experiment transforms raw transactions into a star schema with 524,878 curated fact rows, 19,960 orders, 4,355 customer members, 4,158 product members, and 38 countries. Four representative analytical workloads are benchmarked across three storage designs: a normalized operational data store, a dimensional warehouse, and materialized aggregate tables. The dimensional warehouse reduces mean latency by 42.3% relative to baseline joins, while materialized aggregates reduce latency by approximately 99.9%. A forecasting demonstration on warehouse-generated daily revenue aggregates further shows that a random forest model outperforms a naive benchmark, achieving an RMSE of 23,715.84 versus 34,055.29. The paper contributes an end-to-end reference architecture for retail analytics, together with dimensional design rationale, mathematical formulations, algorithms, empirical results, and implementation guidance relevant to both academic researchers and practitioners.

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