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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 462 Documents
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Malware detection using Gini, Simpson diversity, and Shannon-Wiener indexes Ling, Yeong Tyng; Chiew, Kang Leng; Phang, Piau; Zhang, Xiaowei
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp737-750

Abstract

The increasing number of malware attacks poses a significant challenge to cyber security. This paper proposes a methodology for static malware analysis using biodiveristy-inspired metrics that is Gini coefficient, Simpson diversity, and Shannon-Wiener index for malware detection. These metrics are used to build the structural feature representation on the raw binary file as the feature space. The effectiveness of these metrics are evaluated using multilayer perceptron (MLP) neural network and extreme gradient boosting (XGBoost) models. A deterministic algorithm is used to generate these features that represent the feature signature of the executable file. Additionally, we investigated the effectiveness of different byte sizes as the input feature for these two classifiers. According to the results, Gini coefficient with on chunk size of 128 has successfully achieved average F1 score of more than 98.7% by using XGBoost model.
Pioneering the digital readiness for Malaysian museums: custom framework Khan, Rehman Ullah; Sze Yee, Sabine Chung; Bee, Oon Yin
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp728-736

Abstract

A museum is a hub for public exploration and education of community or country culture and traditions. Digital technologies transform museums into interactive experiences, engaging visitors and bringing cultural values to life. However, Malaysian museums struggle to adopt digital technologies due to limited infrastructure, expertise, exhibition technology, and budgets. These constraints hinder effective audience engagement and limit growth and modernisation efforts. To help Malaysian museums in digitalisation, this study aims to contextualise a digital readiness index (DRI) questionnaire. The findings of this pioneering study have yielded a unique and customised version of the DRI questionnaire specifically designed for Malaysian museums, marking the first-ever initiative of its kind in the country. The DRI serves as a pivotal scale or tool for managers and researchers, facilitating the evaluation and validation of a museum’s digitalisation status while guiding strategic planning for future advancements. This questionnaire enables researchers and museum managers to gain insights into the museums and understand which dimensions require focus and enhancement to ensure a successful and comprehensive transition towards digital transformation.
Prediction and classification of diabetic retinopathy using machine learning techniques Chaouki, Makhlouf; Laouar, Mohamed Ridda; Cheddad, Abbas; Salima, Bourougaa; Eom, Sean
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp516-528

Abstract

Diabetic retinopathy (DR) is a progressive and sight-threatening complication of diabetes mellitus, characterized by damage to the blood vessels in the retina. Early detection of DR is vital for timely intervention and effective management to prevent irreversible vision loss. This paper provides a comprehensive review of recent advancements in integrating machine learning (ML) and deep learning (DL) techniques for diagnosing DR, aiming to assist ophthalmologists in their manual diagnostic process. The paper presents a comprehensive definition of DR, elucidating the underlying pathological processes, clinical signs, and the various stages of DR classification, ranging from mild non-proliferative to severe proliferative DR. Integrating ML and DL in DR diagnosis has developed the field by offering automated and efficient methods and techniques to analyze retinal images. With high sensitivity and specificity, these techniques demonstrate their efficacy in accurately identifying DR-related lesions, such as microaneurysms, exudates, and hemorrhages. Furthermore, the paper examines diverse datasets employed in training and evaluating ML and DL models for DR diagnosis. These datasets range from publicly available repositories to specialized datasets curated by medical institutions. The role of large-scale and diverse datasets in enhancing model robustness and generalizability is emphasized.
Cloud application design for financial reporting in Indonesia’s small and medium enterprises Erin, Erin; Gui, Anderes
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp457-466

Abstract

Small and medium enterprises (SMEs) in Indonesia are increasingly developing, but the application of information technology (IT) in small medium businesses is still lacking because for small medium business owners, doing their own bookkeeping without a system will maximize profits. However, this makes bookkeeping ineffective and inefficient because it requires manual data input and reconciliation. Utilizing a cloud-based accounting information system (CAIS) can integrate data, increase productivity, and minimize infrastructure costs because there is no need to provide costs for physical infrastructure. In this research, CAIS was designed to produce financial reports that focus on small medium businesses in Indonesia. The method used is a qualitative method by conducting observations through literature study for data collection and the rational unified process (RUP) which is limited to the elaboration stage to produce a prototype design. So, the result of this paper is a system design that can be used as a guide to continue with system development. This system aims to simplify transaction records so that they can be more efficient and effective in producing financial reports. The use of CAIS is also expected to increase profits and maximize the use of internet and technology in small medium businesses.
Automated rice leaf disease detection using artificial intelligence deep learning M. P., Suhaila; S., Hemalatha
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp405-415

Abstract

As one of the top five rice-producing countries, India relies heavily on rice for both economic management and food needs. To ensure healthy rice plant growth, early detection of diseases and timely treatment are essential. Since manual disease detection is time-consuming and labor-intensive, an automated approach is more practical. This work presents a deep neural network (DNN)-based artificial intelligence (AI) method for recognizing rice leaf diseases. The method detects three common diseases: leaf smut, bacterial leaf blight, and brown spot, as well as healthy images. The approach uses an AI-based attention network and semantic batch normalized DeepNet (AN-SBNDN) combined with a channel attention mechanism to improve disease detection accuracy. Experiments with rice leaf datasets and comparison with conventional networks like residual attention network (Res ATTEN) and dynamic speeded up robust features (DSURF) validate the effectiveness of the method. Key performance metrics include average accuracy, time, precision, and recall, achieved at 21%, 44%, 26%, and 31%, respectively.
A hybrid framework for enhanced intrusion detection in cloud environments leveraging autoencoder Alagarsamy, Abinaya; Elumalai, Thenmozhi; Ramesh, S. P.; Karuppiah, Tamilarasi; Kaliyaperumal, Prabu; Perumal, Rajakumar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp555-564

Abstract

In today’s world, the significance of network security and cloud environments has grown. The rising demand for data transmission, along with the versatility of cloud-based solutions and widespread availability of global resources, are key drivers of this growth. In response to rapidly evolving threats and malicious attacks, developing a robust intrusion detection system (IDS) is essential. This study addresses the imbalanced data and utilizes an unsupervised learning approach to protect network data. The suggested hybrid framework employs the CIC-IDS2017 dataset, integrating methods for handling imbalanced data with unsupervised learning to enhance security. Following preprocessing, principal component analysis (PCA) reduces the dimensionality from eighty features to twenty-three features. The extracted features are input into density-based spatial clustering of applications with noise (DBSCAN), a clustering algorithm. particle swarm optimization (PSO) optimizes DBSCAN, grouping similar traffic and enhancing classification. To address the imbalances in the learning process, the autoencoder (AE) algorithm demonstrates unsupervised learning. The data from the cluster is input into the AE, a deep learning algorithm, which classifies traffic as normal or an attack. The proposed approach (PCA+DBSCAN+AE) attains remarkable intrusion detection accuracy exceeding 98%, and outperforms five contemporary methodologies.
Cyber-physical resilience system for anomaly detection in industrial environments Mishra, Debani Prasad; Lenka, Rakesh Kumar; Yagyna Duthsharma, Rampa Sri Sai; Kumar, Pavan; Bhardwaj, Lakshay; Salkuti, Surender Reddy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp497-505

Abstract

This work explores the topic of cybersecurity in the context of electric vehicles (EVs). It ensures the resilience of cyber-physical systems against anomalies, which is paramount for maintaining operational efficiency and safety. This paper presents a cyber-physical resilience system (CPRS) customized for anomaly detection. Maintaining operational efficiency and safety in today’s networked industrial contexts requires that cyber-physical systems be resilient to abnormalities. With an emphasis on EVs, this research introduces a unique CPRS designed for anomaly detection in industrial settings. By utilizing the combination of digital and physical elements, the CPRS uses sophisticated monitoring and reaction systems to identify and address irregularities instantly. The process includes creating algorithms for anomaly detection and putting in place a framework that is responsive enough to change with the dangers that it faces. The efficiency of the CPRS in detecting unusual behaviors in EVs is demonstrated by experimental findings, which also improve the overall resilience of the system. Moreover, the research’s ramifications go beyond EVs to include a variety of industrial settings, providing valuable information for the development and execution of resilient cyber-physical systems. This paper highlights the significance of proactive resilience measures in protecting critical infrastructure and advances anomaly detection approaches.
Enhanced n-party Diffie Hellman key exchange algorithm using the divide and conquer algorithm Ashioba, Nwanze Chukwudi; Ejeh, Patrick Ogholorunwalomi; Maduabuchuku, Azaka
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp438-445

Abstract

Cryptographic algorithms guarantee data and information security via a communication system against unauthorized users or intruders. Numerous encryption techniques have been employed to safeguard this data and information from hackers. By supplying a distinct shared secret key, the n-party Diffie Hellman key exchange approach has been used to protect data from hackers. Using a quadratic time complexity, the n-party Diffie-Hellman method is slow when multiple users use the cryptographic key interchange system. To solve this issue, the researchers created an effective shared hidden key for the n-party Diffie Hellman key exchange of a cryptographic system using the divide-and-conquer strategy. The current research recommends the use of the divide and conquer algorithm, which breaks down the main problem into smaller subproblems until it reaches the base solution, which is then merged to generate the solution of the main problem. The comparative analysis indicates that the developed system generates a shared secret key faster than the current n-party Diffie Hellman system.
Exploring user feedback on sharia FinTech apps: a Netnographic study in Indonesia Alam, Azhar; Raihan, Fadhiil Arkanur; Al Bagir, Muhamad; Kurniawan, Adityo Wiwit; Yusuf, Jibrail Bin
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp663-672

Abstract

The rapid growth of Sharia FinTech applications in Indonesia has raised questions about user perceptions and experiences. This study employs a Netnographic approach to explore user feedback on Sharia FinTech apps through reviews posted on the Google Play store. The research analyzed 129 reviews from five Sharia FinTech applications between July and December 2023. The study reveals that 55.10% of users expressed overall satisfaction with the apps, appreciating their ease of use and Sharia compliance. However, significant challenges were identified, with 37.50% of negative reviews related to payment delays and interest issues. Other concerns included system errors, account creation difficulties, and poor customer service. These findings highlight the complex dynamics of user experiences with Sharia FinTech applications, demonstrating a generally positive reception but also pointing to critical areas for improvement. The study contributes to the understanding of Sharia FinTech adoption in Indonesia and provides valuable insights for application developers and Islamic microfinance institutions to enhance their services and address user concerns.
Consumer behavior switching from human agents to chatbots in the health service industry Yulianto, Dwi Fajar; Pratiwi, Titik; Irsan, Fatih Akbarul; Mustikasari, Faranita
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp355-365

Abstract

Artificial intelligence (AI) technology is used in organizations to replace human services with technology, altering customer service experiences. Only a limited number of studies have explored how consumers change their behavior from human-assisted to technology-assisted services when using AI in frontline and specialty healthcare services. This study examined the elements that impact consumers’ transition from human agents to AI-based conversational agents using the push-pull mooring framework. Data from 147 healthcare users was evaluated using structural equation modeling. The data indicates that push effects, specifically adaptability, have a negative impact on switching behavior, while pull effects, such as responsiveness and accessibility, have a positive impact on the switching behavior of customers.

Page 40 of 47 | Total Record : 462

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