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International Journal of Informatics and Communication Technology (IJ-ICT)
Published by Institute of Advanced Engineering and Science
ISSN : 22528776     EISSN : 27222616     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Communication Technology (IJ-ICT) is a common platform for publishing quality research paper as well as other intellectual outputs. This Journal is published by Institute of Advanced Engineering and Science (IAES) whose aims is to promote the dissemination of scientific knowledge and technology on the Information and Communication Technology areas, in front of international audience of scientific community, to encourage the progress and innovation of the technology for human life and also to be a best platform for proliferation of ideas and thought for all scientists, regardless of their locations or nationalities. The journal covers all areas of Informatics and Communication Technology (ICT) focuses on integrating hardware and software solutions for the storage, retrieval, sharing and manipulation management, analysis, visualization, interpretation and it applications for human services programs and practices, publishing refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in ICT.
Arjuna Subject : -
Articles 46 Documents
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Search results for , issue "Vol 15, No 2: June 2026" : 46 Documents clear
A mHealth adoption model for diabetes self-management: patient-centered insights from UNRWA clinics Mohammad Faraj, Saleem; Yuan Kang, Haw; Raja Ikram, Raja Rina; Salahuddin, Lizawati
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp553-564

Abstract

This study develops and validates a mobile health (mHealth) adoption model to enhance diabetes self-management among type 2 diabetes mellitus (T2DM) patients in UNRWA primary healthcare clinics across Palestinian refugee camps. This study fills a gap in research on mHealth adoption in low-resource settings by combining the technology acceptance model (TAM), task-technology fit (TTF), and self-efficacy theory (SET). A descriptive, cross-sectional design was employed using a structured, validated questionnaire administered to 503 T2DM patients. Reliability analysis yielded high internal consistency (Cronbach’s α = 0.808–0.966). Structural equation modeling (SEM) using SPSS and AMOS validated the model fit, evidenced by a comparative fit index (CFI) of 0.941 and a root mean square error of approximation (RMSEA) of 0.048. Out of the eleven factors that were examined, Perceived Usefulness had the most positive impact on self-care management (β = 0.67, p < 0.001), while Task Requirement had the least. Notably, Perceived Self-Efficacy showed no significant effect on behavior (p > 0.05). These findings highlight usability, usefulness, and tool functionality as central to promoting mHealth use. The validated model can be modified for other chronic disease settings in comparable healthcare environments and provides practical advice for creating patient-centered mHealth interventions.
Mitigating gender bias in STEM study field classification using GRU and LSTM with augmented dataset technique Fitrianah, Devi; Safitri, Sarah; Intan Ghayatrie, Nadzla Andrita
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp447-455

Abstract

This study examines gender bias in artificial intelligence (AI), focusing on the classification of high school students into science, technology, engineering, and mathematics (STEM) and non-STEM fields. Using Indonesian student Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta, 11480 data, conditional variational autoencoder (CVAE) and multilabel synthetic minority over-sampling technique (MLSMOTE) were employed for data augmentation to mitigate bias before training gated recurrent unit (GRU) and long short-term memory (LSTM) models for prediction. The combination of MLSMOTE and GRU demonstrated superior performance, achieving accuracies of 93% for female students and 94% for males. These results indicate that MLSMOTE and GRU effectively predict fields of study while addressing gender bias. The findings contribute to advancing fairness in AI systems for education and beyond, ensuring equitable opportunities across diverse applications.
Designing a flutter-based community recipe mobile application Ahmad Uzair, Nik; Che Embi, Zarina
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp707-718

Abstract

This study focuses on developing a cross-platform mobile application for community-based recipe sharing, addressing the increasing role of mobile technology in daily life. Although recipe apps are globally popular, their adoption in Malaysia remains limited. The proposed application aims to fill this gap by providing users an interactive platform to explore, share, and try new recipes within a cooking-focused community. Key features include personalized recipe suggestions, and an intuitive, easy-to-use interface designed for all devices, enhancing user engagement and promoting community interaction. A background study is conducted to understand the existing landscape and user needs. It is followed by a design phase, which will lay the groundwork for addressing the identified challenges. Based on the insights gained from the background study and design outline, a mobile application is developed, aligning with the analyzed requirements and system design. This paper reports on the design and usability evaluation of this study. Based on the design guidelines, it has been found that this application could provide an intuitive and seamless user experience. Future works include the integration of smart kitchen features and personalized machine learning for better user experience.
Android mobile 3D augmented reality engineering devices design using marker-based technique Azim Ibrahim, Mohamad; Kassim, Murizah; Mohammad Zain, Jasni; Beeran Kutty, Suhaili; Mohd Yusoff, Marina; Isdaryanti, Barokah; Ahmadi, Farid; Mohd Pakhrudin, Nor Syazwani
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp683-698

Abstract

Engineering teaching and learning utilizing using augmented reality (AR) technologies is crucial with new technology adaptation. This study has developed an Android mobile based augmented reality of engineering device (ARED) with description using marker-based technique. Unity 3D, Vuforia, and Blender Animation were used to design 3D models of engineering devices on AR platforms. ARED is used to scan a marker and display an AR 3D model of engineering devices with its information. Ten engineering devices models were created using Blender Animation Tools and exported to Unity 3D which are Ups Power, Infrared Thermometer, Cisco Router, Multi meter, Poe Switch, Clamp Meter, Power Supply, Arduino Uno, Raspberry Pi and Oscilloscope. ARED mobile app is successfully tested which presents users can interact with the 3D model using touch input to enhance their learning experience. Result presents user’s evaluation analysis at 86.2% of ARED’s effectiveness and impact for future education. The technical analysis shows that ARED can handle the optimum distance range between 35 to 100 cm, operation angle is best between 45 and 135 degrees and occlusion average maximum of 55%. The significance of the research is to improve the quality and process of engineering education by using AR and promotes the learning society’s transition to digital learning with mixed reality in engineering, which creates a borderless learning environment.
When web apps heal themselves: a MAPE-K based approach to fault tolerance and adaptive recovery G. Aribe Jr., Sales; G. Oracion, Rov Japheth
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp729-740

Abstract

Ensuring the reliability and resilience of modern web applications remains a critical challenge due to increasing system complexity and dynamic runtime environments. This study proposes a modular self-healing framework based on the monitor–analyze–plan–execute over a shared knowledge base (MAPE-K) model, integrated with an AutoFix-inspired mechanism for adaptive fault recovery. Using a design and development research (DDR) approach, the system was implemented and evaluated through controlled fault injection experiments across twenty runtime failure scenarios, including service crashes, memory leaks, and database disconnections. Experimental results demonstrate that the proposed framework achieved a mean fault detection F1-score of 90.7% and a recovery success rate of 93.2%. The AutoFix module reduced the average time-to-recovery (TTR) by 56.2%, achieving an average recovery time of 3.92 seconds. System throughput was maintained between 88% and 95% during fault conditions, with only a 3.1% increase in response time. Additionally, iterative feedback mechanisms improved recovery efficiency by 18.6% over multiple cycles. These findings indicate that the proposed framework provides a practical and extensible approach to enhancing fault tolerance in web applications through feedback-driven adaptation. While the current implementation relies on predefined recovery strategies, the integration of learning-oriented feedback establishes a foundation for future development of more autonomous self healing systems.
Energy-efficient lightweight blockchain framework for scalable and secure sensor networks Swapna Kumar, Surendran; Satyanarayan Reddy, Kalli
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp655-664

Abstract

Wireless sensor networks (WSNs) integrated with the internet of things (IoT) are hybrid technologies of interconnected systems. The IoT connects various devices, from sensors to smart gadget networks, and leverages a framework to provide secure solutions. This paper presents a lightweight adaptive proof-of-stake (APoS) blockchain framework design specifically for IoT-WSN. It focuses on efficient energy, scalability, and robust security. The proposed model integrates a hybrid APoS-delegated PoS (DPoS) consensus mechanism, trust-based routing, and a random forest (RF)-driven intrusion detection system (IDS). Extensive simulations of 100 to 10,000 nodes display energy usage of 0.018–0.019 mJ/node, breach of privacy rates of 0.02%, and throughput up to 9.92 tx/round for 1,000 nodes and 3.40 tx/round for GreenOrbs validation. The IDS achieves 94.21% accuracy for 1,000 nodes and 88.89% for GreenOrbs against distributed denial-of-service (DDoS), Sybil, and Jamming attacks. Validated using the GreenOrbs dataset, the framework ensures real-world applicability in resource-constrained WSNs. Future research has validated and verified the use of APoS and PoS hybrid models for broader decentralised IoT–WSN deployments.
IoT-enabled smart hydroponic system using nutrient film technique for precision agriculture Kumara, Varuna; Naik, Akshatha; Tahsir, Fatima; Bommayya Devadiga, Sinchana; Ramesh Naik, Vinitha
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp900-908

Abstract

The study aims to develop an internet of things (IoT)-enabled automated hydroponic system using the nutrient film technique (NFT) to optimize plant growth with minimal human intervention. The system integrates sensors, microcontrollers, and cloud-based monitoring to maintain optimal conditions for crops. The system utilizes Arduino Uno, ESP8266 Wi-Fi module, and sensors including pH, TDS, DHT11 and water level sensors. Data collected from these sensors is processed in real time, allowing automated adjustments through relay-controlled water and nutrient pumps. The system transmits data to the ThingSpeak IoT platform, enabling remote monitoring and predictive analytics. The proposed hydroponic system ensures stable environmental conditions, improving plant growth efficiency. Key parameters such as pH, TDS levels and humidity are maintained within optimal ranges. The automated system reduces manual intervention, enhances water and nutrient efficiency, and increases yield consistency compared to traditional farming methods. The IoT-based NFT hydroponic system demonstrates significant potential in urban agriculture and controlled environment farming. By leveraging automation, AI-driven analytics, and cloud-based monitoring, it provides a scalable and sustainable solution for precision farming. Future advancements may include AI-based predictive analytics, solar-powered energy solutions, and robotic automation for further optimization.
Arobust outlier detection based filtering for noise removal in grayscale images Salem Al Rawash, Ali; Aini Abdullah, Farah; Kadri Junoh, Ahmad; Alshbeel, Abdallah; Banikhalid, Mohammed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp508-522

Abstract

Salt-and-pepper noise severely degrades the visual quality of digital images, par ticularly at high noise densities where conventional denoising techniques often fail. Median- and mean-based filters tend to oversmooth images and blur fine structures when the majority of pixels within a local window are corrupted. This paper proposes a robust dual-layer denoising framework for grayscale images that integrates rank-based prescreening, interquartile range (IQR)-based statis tical outlier detection using Tukey fences, and a lightweight post-processing sharpening stage. In the first layer, a rank-4 trimmed estimator suppresses ex treme impulse values and stabilizes local statistics. In the second layer, adap tive IQR thresholds are employed to detect and replace residual outliers, even in heavily corrupted neighborhoods. A final step involving selective sharpen ing combined with mild smoothing enhances edge details without amplifying residual noise. Extensive experiments on standard grayscale images (Lenna, Barbara, lake, boat, and living room) across salt-and-pepper noise levels from 10% to 90% demonstrate that the proposed approach consistently outperforms conventional methods, including mean, median, Gaussian, modified decision based unsymmetrical trimmed median filter (MDBUTMF), and pixel density based filter (BPDF). Quantitative evaluation indicates peak signal-to-noise ratio (PSNR) values reaching 38.23dB, structural similarity index (SSIM) values up to 0.99, and significant reductions in mean squared error (MSE), particularly at higher noise densities. These results confirm that the proposed framework ef fectively suppresses noise while preserving edges and textures, making it well suited for practical applications such as medical imaging, remote sensing, and surveillance.
Enhancing road damage detection performance using the YOLOv9 model Farkhan Adhitama, Muhammad; Sutikno, Sutikno; Rismiyati, Rismiyati
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp616-624

Abstract

Roads are essential infrastructure that support community mobility, and their condition significantly impacts road user safety. However, manual road damage detection remains inefficient, time-consuming, costly, and prone to human error. To address this issue, this study proposed the YOLOv9 model for automated road damage detection and explored parameter combinations to optimize its performance. The proposed solution leverages the YOLOv9 model, which offers enhanced detection speed and accuracy compared to previous YOLO versions, due to its improved backbone and dynamic label assignment techniques. The method uses pre-trained weights and performs parameter tuning to adapt the model for identifying common road defects, including potholes, longitudinal, lateral, and alligator cracks. A publicly available dataset of road condition images was used for training and evaluation. Experimental results demonstrated that the optimized YOLOv9 model achieved a mean average precision (mAP) of 62.8%, indicating a promising ability to detect multiple types of road damage accurately. This study highlights the potential of YOLOv9 as an effective tool for road monitoring systems, contributing to proactive maintenance strategies and more efficient infrastructure management.
Harnessing NLP and AI to decode political discourse: speech patterns, sentiment analysis, and public perception Kumar, Malayaj; Kumar Singh, Anuj; Das, Soumitra
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp674-682

Abstract

Using natural language processing (NLP) and artificial intelligence (AI), this study analyzes the frequencies of words and phrases in political leaders’ speeches to track patterns in political discourse. The objective is to identify language patterns, sentiments, and topics of political addresses using state of-the-art methods like automatic transcription (Whisper), Bidirectional gated recurrent unit (GRU) for sentiment analysis, and BERTopic. Through the use of Whisper’s state-of-the-art transcription service, we were able to transcribe the political speeches into machine-readable text, which in turn provides for other types of analysis. Bidirectional GRU classifies sentiment as positive, negative, or neutral with the aim to study how politicians use sentiment to manipulate their listeners. Furthermore, we use BERTopic for tracking the evolution of rhetoric, key trend summarisation, and topic mining and analysis. It illustrates how politicians employ discursive strategies and epilinguistic elements to manage the public mind and reality. Achievements and objectives are framed with positive and defensive emotions aimed at threats or criticisms. The emotional grab of it all is still important. It locates in these the thematic coherence and shifting sentiment that lie at the heart of political storytelling. It shows how political communication is evolving to stay relevant in the digital media age and delivers language – even real-time language pattern tracking – via the use of AI and big data. Further study is needed of multimodal and flexible techniques for analysing political discourse across languages and time periods.

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