International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
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
Articles
146 Documents
<![CDATA[Experimental Evaluation of CLIP-Based Zero-Shot Classification of Imbalanced Remote Sensing Scenes: Addressing Quantity Disparities in Data ]]>
<![CDATA[Tanvir Ahmed]]>;
<![CDATA[Asfika Jaman Tanha]]>;
<![CDATA[Shekh Ifteesham Iftee]]>;
<![CDATA[Tanjoy Mahmud]]>;
<![CDATA[Ekra MD Emadur Rahman]]>;
<![CDATA[Hossain MD Maruf]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 1 (2025): INJIISCOM: VOLUME 6, ISSUE 1, JUNE 2025 ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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DOI: 10.34010/injiiscom.v6i1.14164
<![CDATA[This paper presents a zero-shot learning framework based on Contrastive Language Image Pretraining (CLIP) for Remote Sensing Scene Classification (RSSC). The proposed method addresses the challenge of imbalanced image quantities across different categories, which is often encountered in practical ap-plications. Traditional zero-shot learning methods in RSSC leverage pre-trained word embeddings to extract semantic features from category names or descriptions, which are then fixed during the learning process without adaptation to visual features. This leads to a gap between visual and semantic representations. We have integrated the Vision Transformer with CLIP to enhance the alignment between visual and semantic features. Extensive experiments conducted on WHU-RS19 dataset demonstrate the effectiveness of the proposed framework, show-casing improved classification performance and generalization capabilities. ]]>
<![CDATA[Chatbot Adoption Framework for Real-Time Customer Care Support ]]>
<![CDATA[Geoffrey Nyongesa]]>;
<![CDATA[Kelvin Omieno]]>;
<![CDATA[Daniel Otanga]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 1 (2025): INJIISCOM: VOLUME 6, ISSUE 1, JUNE 2025 ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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DOI: 10.34010/injiiscom.v6i1.14282
<![CDATA[As sectors increasingly digitize, adopting artificial intelligence (AI) chatbots to supplement human customer service is becoming essential. However, Kenyan telecom companies lack well-formulated rules for implementing these systems. This study proposes an adoption framework for deploying AI chatbots in the telecommunications sector. By assessing the current level of chatbot installation in Kenya and reviewing previous technology adoption models, primary metrics for chatbot dissemination were identified. A mixed-methods approach was used, collecting quantitative and qualitative data via questionnaires and interviews with AI experts from Safaricom PLC and the Communications Authority of Kenya using the Delphi method. The resulting framework provides crucial guidelines for telecommunication companies planning to adopt AI chatbots for real-time customer care support.]]>
<![CDATA[Deep Learning-Based Sonar Image Object Detection System ]]>
<![CDATA[Md Shahazul Islam]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025 ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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DOI: 10.34010/injiiscom.v6i2.14431
<![CDATA[Sonar image object detection is crucial for underwater tasks, yet practical applications of advanced deep learning in this field remain underexplored due to environmental challenges. To bridge this gap, this study proposes a modular, deep learning-based sonar image object detection system. The system comprises three interdependent subsystems: dataset generation, algorithm model training and testing, and model deployment. Designed for high accuracy, speed, portability, and deployment adaptability, it effectively processes challenging sonar data. Experimental results from underwater suspicious object detection tasks confirm that the system achieves reliable, accurate performance and excellent real-world application outcomes. This work significantly advances sonar-based target localization and exploration.]]>
<![CDATA[Advancing Particle Technology Research in Indonesia: Insights from Computational Bibliometric Analysis ]]>
<![CDATA[Kurniawan, Tedi]]>;
<![CDATA[Nandiyanto, Asep Bayu Dani]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 2 (2023): INJIISCOM: VOLUME 4, ISSUE 2, DECEMBER 2023 ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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DOI: 10.34010/injiiscom.v4i2.14686
<![CDATA[This study explored the progress and trends in particle technology research in Indonesia using computational bibliometric analysis based on the Scopus database. Examining academic publications, collaboration networks, and citation patterns identified key areas of focus, influential contributors, and emerging topics within the field. Insights gained from this study provide a comprehensive overview of the research landscape, highlighting Indonesia's contributions to particle technology and offering guidance for future research priorities and collaborations. The findings aim to strengthen the country's scientific presence in this domain and foster innovation and global engagement]]>
<![CDATA[Digital Competences and Trends in Applications to Support Health Lifestyle ]]>
<![CDATA[Rahayu, Nur Indri]]>;
<![CDATA[Muktiarni, M]]>;
<![CDATA[Ismail, Affero]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 1 (2023): INJIISCOM: VOLUME 4, ISSUE 1, JUNE 2023 ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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DOI: 10.34010/injiiscom.v4i1.14805
<![CDATA[This research aims to analyze trends in researchers' behavior when researching sports applications to support a healthy lifestyle. Bibliometric analysis was used as a method in this research. Research data was collected via the Scopus page starting May 22, 2024. The keywords used were "Sport" AND "Application" "Health" AND "Lifestyle." Search results on the Scopus database found 100 documents from 79 publication sources. The development of article publications regarding sports applications to support a healthy lifestyle can increase yearly. Although, from the highest total publications in 2021, there was a decrease in the number of publications from 2020 to 2024. Fifty-five countries have contributed to publications regarding the use of sports applications to support a healthy lifestyle. The research results show that exercise is most popularly used yearly in research regarding using exercise applications to support a healthy lifestyle. This indicates that researchers use many sports applications as a medium for physical training and training for exercise. Hopefully, this research will become a reference and primary source for further research on using sports applications to support a healthy lifestyle]]>
<![CDATA[Global Scientific Trends On Nutrition Apps to Support Healthy Lifestyle in Digital Age ]]>
<![CDATA[Muktiarni, Muktiarni]]>;
<![CDATA[Rahayu, Nur Indri]]>;
<![CDATA[Ismail, Affero]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 2 (2023): INJIISCOM: VOLUME 4, ISSUE 2, DECEMBER 2023 ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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<![CDATA[Technological developments have brought about significant changes in the way people manage their health and fitness, especially through nutrition apps. Bibliometric analysis is used in the article to help discover current trends in the use of nutrition apps that support a healthy lifestyle. Key focuses of this trend include personalization, easy access to nutritional information, and integration with other health devices. In addition, this article also explores the positive impact of using nutrition apps on users' health awareness and behavior, as well as the challenges faced, such as data privacy issues and the need for scientific validation of the recommendations provided. The Scopus database was used to search for article data. Search results on the Scopus database found 107 documents. The development of publication of articles regarding nutritional applications to support a healthy lifestyle can be said to be quite increasing every year, although in 2023, 2019 and 2017 there has been a decline. There are a total of 175 countries that have contributed to publications regarding the use of nutrition applications to support a healthy lifestyle. Through this analysis, it is hoped that it can provide insight into the important role of nutritional applications in supporting a healthy lifestyle in the digital era]]>
<![CDATA[A Research on Positioning Algorithm Based on RPCA in Sparse Fingerprint Environment ]]>
<![CDATA[Yaqin Xie]]>;
<![CDATA[Md Emadur Rahman Ekra]]>;
<![CDATA[Tianyuan Gu]]>;
<![CDATA[Xiaoli Wang]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025 ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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DOI: 10.34010/injiiscom.v6i2.14980
<![CDATA[Wi-Fi fingerprinting for indoor positioning is cost-effective but struggles with environmental noise and requires extensive data collection for high accuracy. To address these challenges in sparse fingerprint environments, this paper proposes a positioning technique using a Robust Principal Component Analysis (RPCA) algorithm. First, gathered signals are purified using measurement weights to mitigate outlier noise, saving the refined fingerprints in a database. Second, to reduce collection costs, virtual fingerprints are generated near reference points using a transmission loss model and stored offline. Finally, adaptive K-value fingerprint matching is applied to estimate the user's location. Results demonstrate that the proposed RPCA-based algorithm significantly improves positioning accuracy in sparse indoor environments. ]]>
<![CDATA[TextGuard: Identifying and Neutralizing Adversarial Threats in Textual Data ]]>
<![CDATA[Luay Albtsoh]]>;
<![CDATA[Marwan Omar]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025 ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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DOI: 10.34010/injiiscom.v6i2.15232
<![CDATA[Adversarial attacks in the text domain pose a serious risk to the integrity of Natural Language Processing (NLP) systems. In this study, we propose "TextGuard," a unique approach to detect hostile instances in NLP, based on the Local Outlier Factor (LOF) algorithm. This paper compares TextGuard's performance against traditional NLP classifiers like LSTM, CNN, and transformer-based models, experimentally verifying its effectiveness across various real-world datasets. To contextualize our findings and demonstrate its superiority, TextGuard significantly surpasses earlier state-of-the-art methods like DISP and FGWS, achieving F1 recognition accuracy scores up to 94.8%. Consequently, this sets a new benchmark as the first application of the LOF technique for adversarial example identification within the text domain.]]>
<![CDATA[Data Mart of Climate Changes: a Proposed Approach to Support Sustainable Decision-Making ]]>
<![CDATA[Alaa Khalaf Hamoud]]>;
<![CDATA[Abdul-Haleem Ali Al Muhyi]]>;
<![CDATA[Sadiq Salim Abdullah]]>;
<![CDATA[Jasim Mohammed Dahr]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST) ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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DOI: 10.34010/injiiscom.v7i1.14878
<![CDATA[The growing focus on climate change requires effective data-driven decision-making to promote sustainability. This study proposes a data mart framework to manage and analyze climate datasets from a station in Basrah, Iraq. The model aims to support sustainable decisions by examining factors like temperature, solar radiation, rainfall, wind direction, humidity, and wind speeds at various altitudes. A star schema facilitates online analytical processing (OLAP) and future advanced analytics. Power BI is utilized for data visualization, providing actionable insights to stakeholders via real-time indicators and customizable dashboards. This framework aids short-term decision-making regarding climate impacts, with future work incorporating machine learning for predictive pattern forecasting.]]>
<![CDATA[Unveiling the Potential of Local Outlier Factor in Credit Card Fraud Detection ]]>
<![CDATA[Angel Jones]]>;
<![CDATA[Marwan Omar]]>
<![CDATA[ International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST) ]]>
Publisher : <![CDATA[Universitas Komputer Indonesia ]]>
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DOI: 10.34010/injiiscom.v7i1.15319
<![CDATA[This study evaluates the Local Outlier Factor (LOF) algorithm for credit card fraud detection, emphasizing its effectiveness with highly imbalanced datasets. Unlike traditional methods that struggle with the rarity and variability of fraudulent transactions, LOF utilizes local density deviations to identify anomalies. Through a rigorous methodology involving data preprocessing, parameter tuning, and comparative machine learning analysis, LOF demonstrated a high recall rate and a balanced precision-recall trade-off, excelling at detecting subtle, localized fraud. Challenges like threshold setting and false positives were noted, with future research suggested on real-time integration and advanced feature engineering. The study underscores LOF's strengths, contributing to enhanced financial security strategies. ]]>
