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Rizky Jumansyah
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INDONESIA
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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Experimental Evaluation of CLIP-Based Zero-Shot Classification of Imbalanced Remote Sensing Scenes: Addressing Quantity Disparities in Data Ahmed, Tanvir; Tanha, Asfika Jaman; Iftee, Shekh Ifteesham; Mahmud, Tanjoy; Rahman, Ekra MD Emadur; Maruf, Hossain MD
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 1 (2025): INJIISCOM: VOLUME 6, ISSUE 1, JUNE 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i1.14164

Abstract

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 slot deposit 5000 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.
Chatbot Adoption Framework for Real-Time Customer Care Support Nyongesa, Geoffrey; Omieno, Kelvin; Otanga, Daniel
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 1 (2025): INJIISCOM: VOLUME 6, ISSUE 1, JUNE 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i1.14282

Abstract

In our society today, most sectors are digitizing and automating their processes for efficiency. Human labour has become obsolete as a result of the disruption of labour markets brought about by the rising complexity and availability of software programs. When seen in this light, the adoption of artificial intelligence chatbots by businesses as a supplement to human customer service representatives serves as a crucial development. Computer programs or software that communicate with humans using natural language are referred to as chatbot applications. Through the use of speech, text, or both, the purpose of a chatbot is to simulate human interaction in response to input in natural language. For the purpose of providing customer care support services, there are no well-formulated rules for the implementation of artificial intelligence chatbots in Kenyan telecom companies. An adoption framework for the deployment of artificially intelligent chatbots in the telecommunications sector was proposed as the objective of the research. This was accomplished by determining the current level of the installation of chatbot apps in Kenya and identifying the primary metrics that might be used as indications for the dissemination of chatbots. A study of the earlier frameworks and models on technology adoption was conducted in order to determine the relevant metrics. A combination of research approaches was used in this study, with questionnaires and interview schedules being used to obtain quantitative and qualitative data, respectively. In order to examine qualitative data, content analysis was what was used. Using tables and charts, descriptive analysis was performed on the quantitative data, and the findings were presented. AI specialists working for Safaricom PLC and the Communications Authority of Kenya were the ideal candidates for this position. From the two different telecommunications companies, a sample was selected for the research study utilizing the Delphi approach. A descriptive analysis as well as a major component analysis were used because they serve as a guide on aspects to consider before using AI chatbots for customer support services provision. The results of this research are particularly important to all companies that are involved in providing telecommunication services
Deep Learning-Based Sonar Image Object Detection System Islam, Md Shahazul
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i2.14431

Abstract

Sonar image object detection is an important part of underwater exploration, submarine rescue, hostile object reconnaissance, and other critical maritime tasks. Accurate and efficient detection of objects in sonar imagery plays a key role in ensuring operational success in these domains. The breakthrough of deep learning-related technologies has brought new opportunities for the development of sonar image object detection. By leveraging advanced machine learning techniques, researchers have developed systems capable of achieving higher accuracy and robustness compared to traditional detection methods. However, despite these advancements, the relevant systematic research and practical applications remain insufficiently explored. Traditional approaches often struggle with challenges such as noise, low resolution, and the dynamic underwater environment, which limit their effectiveness. In contrast, deep learning models, with their data-driven advantages, have demonstrated significant potential in overcoming these challenges by learning robust feature representations from large-scale datasets. To address these gaps, a sonar image object detection system is designed to meet the requirements of accuracy, speed, portability, extensibility, and deployment adaptability in real-world scenarios. The system architecture is modular, consisting of three interdependent subsystems: dataset generation, algorithm model training and testing, and model deployment. The dataset generation subsystem ensures high-quality annotated sonar data, which is critical for effective model training. The training and testing subsystem incorporates state-of-the-art deep learning algorithms to optimize detection performance. Finally, the deployment subsystem focuses on translating the trained models into practical applications, ensuring they meet operational requirements under diverse environmental conditions. The system has been applied to underwater suspicious object detection tasks, addressing a range of scenarios requiring precise identification and localization of targets. The experimental results demonstrate that the object detection system achieves reliable and accurate performance, providing good test data and exhibiting excellent application outcomes. This work contributes to advancing the field of sonar image object detection, paving the way for future innovations in underwater exploration and related disciplines.
Advancing Particle Technology Research in Indonesia: Insights from Computational Bibliometric Analysis Kurniawan, Tedi; Nandiyanto, Asep Bayu Dani
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 2 (2023): INJIISCOM: VOLUME 4, ISSUE 2, DECEMBER 2023
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v4i2.14686

Abstract

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
Digital Competences and Trends in Applications to Support Health Lifestyle Rahayu, Nur Indri; Muktiarni, M; Ismail, Affero
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 1 (2023): INJIISCOM: VOLUME 4, ISSUE 1, JUNE 2023
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v4i1.14805

Abstract

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
Global Scientific Trends On Nutrition Apps to Support Healthy Lifestyle in Digital Age Muktiarni, Muktiarni; Rahayu, Nur Indri; Ismail, Affero
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 2 (2023): INJIISCOM: VOLUME 4, ISSUE 2, DECEMBER 2023
Publisher : Universitas Komputer Indonesia

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

Abstract

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
A Research on Positioning Algorithm Based on RPCA in Sparse Fingerprint Environment Xie, Yaqin; Ekra, Md Emadur Rahman; Gu, Tianyuan; Wang, Xiaoli
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i2.14980

Abstract

The indoor positioning method based on Wi-Fi fingerprinting has the advantages of simple acquisition and low cost. However, during signal collection, the presence of significant noise in the environment can cause fluctuations in signal strength measurements due to environmental variations. Additionally, a large number of fingerprints usually need to be collected to achieve high positioning accuracy. To address these issues, this paper proposes a positioning method based on a robust principal component analysis algorithm (RPCA) in a sparse fingerprint environment. Firstly, considering the outlier noise present in the collected signals, purification is performed based on signal measurement weights, and the refined fingerprints are stored in the fingerprint database. Secondly, given the high cost of collecting fingerprints, this paper generates some virtual fingerprints near reference points based on a transmission loss model, all of which are stored in an offline fingerprint database. Finally, adaptive K-value fingerprint matching is used to obtain the final results. The results show that the proposed algorithm can improve positioning accuracy in a sparse fingerprint environment.
TextGuard: Identifying and Neutralizing Adversarial Threats in Textual Data Albtsoh, Luay; Omar, Marwan
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025
Publisher : Universitas Komputer Indonesia

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

Abstract

Adversarial attacks inside the text domain pose a serious risk to the integrity of Natural Language Processing (NLP) systems. In this study, we propose "Text-Guard," a unique approach to detect hostile instances in natural language processing, based on the Local Outlier Factor (LOF) algorithm. This paper compares TextGuard's performance against that of more traditional NLP classifiers such as LSTM, CNN, and transformer-based models, while also experimentally verifying its effectiveness on a variety of real-world datasets. TextGuard significantly surpasses earlier state-of-the-art methods like DISP and FGWS, with F1 recognition accuracy scores as high as 94.8%. This sets a new benchmark in the field as the first use of the LOF technique for adversarial example identification in the text domain
Data Mart of Climate Changes: a Proposed Approach to Support Sustainable Decision-Making Hamoud, Alaa Khalaf; Ali Al Muhyi, Abdul-Haleem; Salim Abdullah, Sadiq; Mohammed Dahr, Jasim
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 : Universitas Komputer Indonesia

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

Abstract

The increasing interest in climate changes necessitates different effective solutions of data-driven decision-making in order to promote sustainability. In this paper, a proposed framework of data mart model designed to manage and analyze dataset of climate change in a station in Basrah, Iraq. The main aim is to examine the proposed data mart for support sustainable decisions by diving in different factors such as temperature, solar radiation, rain, wind speed at (10, 30, 50, 52) meters, wind direction, and humidity. The proposed schema for data mart is star schema to facilitate on-line analytical processing operations (roll up and drill down) through hierarchy of date, and for future advanced analytics. Power BI is used to design different dashboards to visualize data, and provide actionable insights for environmental researchers, decision makers, and stakeholders. The proposed model demonstrates real-time indicators and key performance indicators, with many customizable dashboards which ultimately provide stakeholders to make short-term decisions regarding climate change effects. The future works focus on integrating many other climate changes dataset from different locations and integrating predictive models of machine learning algorithms for forecasting patterns that support sustainability.
Unveiling the Potential of Local Outlier Factor in Credit Card Fraud Detection Jones, Angel; Omar, Marwan
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 : Universitas Komputer Indonesia

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

Abstract

This study evaluates the Local Outlier Factor (LOF) algorithm for credit card fraud detection, emphasizing its effectiveness with imbalanced datasets. Unlike traditional methods that struggle with the rarity and variability of fraudulent transactions, LOF uses local density deviations to identify anomalies. Through a rigorous methodology involving data preprocessing, parameter tuning, and comparison with other machine learning algorithms, 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 system integration, algorithm combination, and advanced feature engineering. The study underscores LOF's strengths and limitations, contributing to enhanced fraud detection strategies

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