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International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Published by Universitas Komputer Indonesia
ISSN : 28100670     EISSN : 27755584     DOI : https://doi.org/10.34010/injiiscom
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering
FOCUS AND SCOPE INJIISCOM cover all topics under the fields of Computer Engineering, Information system, and Informatics. Informatics and Information system IT Audit Software Engineering Big Data and Data Mining Internet Of Thing (IoT) Game Development IT Management Computer Network and Security Mobile Computing Security For Mobile Decision Support System Web and Cloud Computing Accounting Information system Electrical and Computer Engineering Sensors and Trandusers Signal, Image, Audio and Video processing Communication and Networking Robotic, Control and Automation Fuzzy and Neural System Artificial Intelligent
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 145 Documents
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Transform IT Governance and Management to Enhance Information Security using COBIT Framework 2019 Ibrahim, Achmad Najib; Suryani, Erma
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 2 (2026): INJIISCOM: VOLUME 7, ISSUE 2, DECEMBER 2026 (Online First)
Publisher : Universitas Komputer Indonesia

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Abstract

The rapid utilization of Information Technology in the operations of PT Kawasan Industri Terpadu Batang (KITB) increases the complexity of cybersecurity risks and regulatory compliance demands, thus requiring structured governance. This study aims to analyze the existing conditions, identify gaps, and develop an information security governance model using the COBIT 2019 framework. The research method involves Design Factor assessment and organizational needs analysis that sets four priority process objectives: EDM03, APO12, APO13, and DSS05. Data was collected through interviews, questionnaires, and document studies to assess the level of as-is capability compared to to-be targets. The results showed that the current level of governance capability is at Level 3 (Largely Achieved) with a target of Level 4 (Fully Achieved), leaving a gap of one level. Based on the gap analysis, strategic recommendations were developed focusing on strengthening governance, risk management, and security operations. This study provides a practical contribution in the form of a roadmap to improve measurable information security governance to support the operational stability of industrial estates and compliance with data security regulations.
Opportunities and Challenges: The Financial Impact of Net-Metering on the Mexico Area Branch Office of Pampanga I Electric Cooperative, Inc. Pacurza, John Alvie; Manalang, Marion Angelo; Manalili, Nelson S; Manaloto, Carl Jonash M; Medina, Russel Roel T; Bulanan, Eldren V; Tiria, Genesis C
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 8 No. 1 (2027): INJIISCOM: VOLUME 8, ISSUE 1, JUNE 2027 (Online First)
Publisher : Universitas Komputer Indonesia

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Abstract

This study investigated the present opportunities and challenges about the financial impact of the operation and implementation of the Net-metering program on the Mexico area branch office of (Pampanga I Electric Cooperative, Inc.) PELCO 1, derived from the quantitative and qualitative data from responses of the involved participants. It aims to identify the level of awareness and experience of both regular and net-metered consumers, assess the operation and financial implications of the program, identify the associated operational and regulatory challenges, and provide policy recommendations. The key themes identified include technological advances in billing and monitoring, cooperative sustainability with solar adoption, clearer regulations and sanctions, negative balances and potential financial risks, and importance of proactive guidance from PELCO 1. Participants expressed firm preference for integrated modern mechanisms into the Net-metering program, effective transition of energy models, establishing consumer engagement and discretion, proactive financial management, and commercial arrangements. The findings emphasized the financial liability PELCO 1 faces due to its Net-metered consumers, and the notable regulatory and operational gaps of the program. This study provided viable recommendations aimed to safeguard the utility against advancements of the program while promoting balanced compliance with policy requirements. By addressing these identified challenges PELCO 1 could significantly enhance its adoption of the Net-metering program and acquire a refined financial framework.
Seeing the Unseen? Opportunities and Challenges in Understanding Tourist Behaviors from UGC using Large Language Models (LLMs) Gusriza, Fondina; Priyambodo, Tri Kuntoro; Mustofa, Khabib; Mutiarin, Dyah
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 8 No. 1 (2027): INJIISCOM: VOLUME 8, ISSUE 1, JUNE 2027 (Online First)
Publisher : Universitas Komputer Indonesia

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Abstract

The era of digital tourism has resulted in various of User-Generated Content (UGC), such as online reviews, social media, and travel blogs. UGC is an important source of data for understanding tourist behaviours but often overlooked. This study explores the opportunities and challenges of using Large Language Models (LLMs) as a new approach to analyse UGC. The data were collected from TripAdvisor, focusing on 15 cultural heritage sites in Yogyakarta, Indonesia, with more than 13,000 user reviews. OpenAI’s Large Language Model was applied through API integration in Python, and a Natural Language Processing (NLP) approach was used to analyse tourist motivations, experiences, and satisfaction. The analytical process includes web scraping, text pre-processing, theory-driven classification, prompt engineering, and lexicon mapping. This paper contributes to the emerging discourse on the use of Artificial Intelligence in tourism studies, particularly in understanding tourist behaviours through textual data.
A Multi-Theoretical Perspective on Blockchain Adoption in Tea Supply Chain Optimization: Analyzing Drivers and Barriers through Theories Hillan, Ronoh
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 8 No. 1 (2027): INJIISCOM: VOLUME 8, ISSUE 1, JUNE 2027 (Online First)
Publisher : Universitas Komputer Indonesia

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Abstract

Blockchain technology has emerged as a transformative innovation in supply chain management (SCM), improving transparency, traceability, and efficiency. This study adopts a multi-theoretical perspective by integrating six organizational theories—Principal-Agent Theory, Transaction Cost Analysis, Resource-Based View, Network Theory, Institutional Theory, and Information Theory—to examine the drivers and implications of blockchain adoption in SCM. Each perspective explains how blockchain mitigates agency conflicts, lowers transaction costs, functions as a strategic resource, strengthens collaborative networks, responds to institutional pressures, and enhances information processing. Using the tea supply chain as an illustrative context, this study proposes a conceptual framework linking blockchain features—such as immutability, smart contracts, and decentralization—with SCM goals including trust, accountability, cost efficiency, and competitive advantage. The synthesis provides insights for developing scalable and context-relevant blockchain applications across supply chain systems.
MediPredict-from Symptoms to Smart Care Sahu, Mahendra; Sahu, Nidhi; Sinha, Nikhil; Potdar, Ravindra Manohar
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 8 No. 1 (2027): INJIISCOM: VOLUME 8, ISSUE 1, JUNE 2027 (Online First)
Publisher : Universitas Komputer Indonesia

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Abstract

This study proposes a user-friendly Personalized Medical Recommendation System that integrates Artificial Intelligence (AI) and Machine Learning (ML) to improve disease prediction and healthcare accessibility, especially for non-technical users in resource-limited settings. The system is built using a Flask-based RESTful API that enables real-time predictions and delivers context-aware health recommendations through a web interface. It utilizes datasets covering symptoms, diseases, precautions, diet plans, workouts, and medication information. Data preprocessing techniques, including noise removal, normalization, missing value imputation, and synonym mapping, are applied to ensure consistency and reliability. Five classification algorithms—Support Vector Classifier (SVC), Random Forest, K-Nearest Neighbors (KNN), Gradient Boosting, and Multinomial Naive Bayes—were evaluated, with SVC achieving the highest accuracy of 95.2%. The system predicts diseases based on user-input symptoms and provides personalized recommendations. Overall, the framework offers a scalable, efficient, and practical solution for integrating AI-driven diagnosis into digital healthcare platforms.

Page 15 of 15 | Total Record : 145

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