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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 601 Documents
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Multi-criteria optimization of emergency unit allocation using COPRAS and SMART: a case study in Palembang Evi Yuliza; Fitri Maya Puspita; Indrawati Indrawati; Sisca Octarina; Frisca Frasilia
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1419-1430

Abstract

Increasing living standards and instant eating patterns have improved people's demands for quality health services. Hospitals as health service facilities are actually real-time networks that expected to be able to provide effective and efficient services. This research uses the complex proportional assessment (COPRAS) and simple multi-attribute rating technique (SMART) methods to determine the hospital with the most optimal emergency unit (EU) services in each subdistrict based on predetermined criteria. The research results show that the COPRAS method is produces performance index values ranging from 0.0195 to 0.1317, while the SMART method yields scores between 0.054 and 0.122, both demonstrating consistent ranking outcomes. The three hospitals, with the most optimal EU performance are Dr. Mohammad Hoesin, RSU Pertamina, and RSJ Ernaldi Bahar, with Dr. Mohammad Hoesin achieving the highest utility value (0.1317). The novelty of this study lies in the integration of real-time spatial and operational data from Google Maps and RS Online into a hybrid set covering problem (SCP) framework, combining the strengths of COPRAS and SMART.
Social media interaction of halal fashion brand in Indonesia: a netnographic study of image management Azhar Alam; Fatmawati Fatmawati; Muhamad Al Bagir; Raisa Aribatul Hamidah
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1395-1407

Abstract

Research on halal fashion has largely focused on consumer purchase decisions, with limited attention to how halal fashion brands interact with consumers and manage their brand image on social media platforms such as Instagram. This study addresses this gap by examining brand interaction patterns and image management strategies among leading halal fashion brands in Indonesia. Using a netnographic approach, it analyzed 1,321 Instagram posts from six halal fashion brands over six months (July–December 2022), applying content and image‑management codes to classify post types (photos and videos) and representation strategies (personalized, contextual, and celebrity use). The findings show a slightly higher proportion of photo posts (51%, 674 posts) than video posts (49%, 647 posts), with hijab fashion brands more active than Muslim and sports fashion brands in producing content. Across all brands, image management relied predominantly on personal context and non‑celebrity representation, while professional context and celebrity‑based posts were used less frequently. These results suggest that halal fashion brands strategically emphasize relatable, personalized, and non‑celebrity content to build brand image and engagement on Instagram, offering practical guidance for brand managers in designing effective social media strategies and contributing novel empirical evidence on brand interaction and image management in the halal fashion sector.
Advanced encryption standard with asymmetric key exchange for text encryption Ravindra K Reddy; Vijayalakshmi P
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1263-1271

Abstract

When creating communication systems, securing data is crucial and improved randomization in creating secret keys contributes to more secure systems. Unfortunately, the symmetric ciphers used for data encryption, such as the advanced encryption standard (AES), may be subject to attacks that exploit timing measurements to deduce the secret key used for encryption, resulting in a significant lack of research on the security of hybrid implementations (usually defined as incorporating AES and asymmetric ciphers) for AES encryption. We introduce a new hybrid encryption method that combines the AES encryption standard with elliptic curve cryptography (ECC). In this hybrid form of encryption, ECC is utilized to facilitate secure encryption and transmission of the AES key and cycle through 16 rounds of AES to encrypt the majority of the data. We provide a performance comparison between the new algorithm and the AES-128 encryption standard. Our findings indicate that the new hybrid encryption method produced an average encryption time of 0.0002 seconds for 10MB files, significantly faster than the AES-128 encryption standard, which takes an average of 0.0016 seconds. The new hybrid method exhibits significant resistance to cryptanalytical attempts, experiencing an average avalanche effect of 49.84%, while maintaining the AES nonlinearity value at 112. As such, we conclusively state that the new hybrid encryption method utilizing ECC and AES will provide effective security for user data against timing side-channel attacks while remaining an efficient method for performing encryption.
Enhanced anomaly detection in IoT networks via feature fusion and learning-based echo state networks P. Palpandi; B. Sakthivel; M. Ponnrajakumari; M. Indirani; S. Govindaraju; S. Deivasigamani
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1154-1166

Abstract

The fast development of internet of things (IoT) networks has led to an increased probability of cyberattacks. Intrusion detection systems (IDS) are needed for identifying unauthorised access and malicious activities in such dynamic environments. However, existing machine learning (ML) models failed to handle the complexity and variability of modern cyber threats. In this work, a hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity. The model combines three types of features: (i) supervised feature extraction using linear discriminant analysis (LDA) to extract the most discriminative features, (ii) unsupervised feature learning through autoencoders to capture latent representations of the input data, and (iii) statistical features such as mean, variance, skewness, and kurtosis to learn input characteristics. The fused feature matrix is fed into a learning based echo state network (LBESN) for final detection. The parameters of the LBESN model are tuned using black eagle optimizer (BEO). Experimental results on standard intrusion detection datasets such as UNSW-NB15, KDD99, and InSDN show that the proposed model achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.
A recent hybrid of IoT with adaptive extended Kalman filter fuzzy logic for children’s health dietary Noorrezam Yusop; Massila Kamalrudin; Mohd Nazrien Zaraini; Siti Fairuz Nurr Sardikan
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1217-1225

Abstract

The increasing prevalence of childhood obesity highlights the critical need for intelligent dietary monitoring systems that are tailored to individual nutritional requirements. This work describes the creation and testing of an internet of things (IoT) hybrid using an adaptive extended Kalman filter and fuzzy logic (AEKFFL-IoT) model aimed at providing personalised food calorie prediction for children. The system uses IoT devices to collect real-time sensor data, such as height, weight, and BMI, and then employs extended Kalman filter (EKF) algorithms to denoise signals and anticipate trends. Fuzzy logic inference is then utilised to adaptively calculate caloric requirements based on biometric data. Experimental results reveal that the proposed AEKFFL model has a training root mean square error (RMSE) of 21.43 kcal and a testing RMSE of 22.35 kcal, exceeding existing rule-based, wearable, and ANN-driven models in terms of accuracy and generalisation. Furthermore, the system achieves high classification accuracy (94.5%) for BMI categorisation and fuzzy rule application. Comparative examination confirms the model’s adaptability, real-time integration, and mobile deployment capability. This study presents a scalable and intelligent approach for child-centered dietary monitoring, paving the path for personalised digital health interventions.
Metaverse based immersive learning prototype for satellite communication using silvercoms model Komputerio Akbar; Meyliana Meyliana; Harco Leslie Hendric Spits Warnars; Ilvico Sonata
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1408-1418

Abstract

Advancements in immersive technologies and the metaverse are transforming engineering education. Satellite communication, as a complex and abstract domain, requires innovative approaches to enhance conceptual understanding and learner engagement beyond traditional methods. This study proposes the Silvercoms model, an immersive learning framework tailored for satellite communication systems education, integrating metaverse technology with pedagogical design. The model combines 3D interactive simulations, collaborative virtual environments, and systems-level content delivery, structured using the 6E instructional model and supported by the motivated strategies for learning questionnaire (MSLQ) to address both cognitive and motivational aspects of learning. The system is developed using a systems engineering approach and implemented with Unity3D in a virtual reality (VR) based metaverse environment. The prototype includes modules such as satellite orbit simulation, satellite history, and interactive satellite systems, enabling experiential and concept-driven learning. This study contributes by integrating immersive technology with structured pedagogical frameworks, offering a novel approach to improving learning effectiveness in satellite communication systems education.
Insight invest: sentiment-aware stock prediction using LSTM and conversational interface Ankit Pande; Aakhyan Jeyush; Abhishek K. Lakhote; Saket A. Rathi; Manoj B. Chandak
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1115-1122

Abstract

The volatile nature of financial markets requires sophisticated tools that integrate advanced analytics with accessible interfaces to facilitate informed investment decisions. This research introduces Insight Invest, an intelligent investment assistant that combines sentiment analysis with time-series forecasting to deliver comprehensive stock market insights. The platform introduces the emotional quotient (EQ), a novel metric derived from the sentiment analysis of financial news, to quantify market sentiment and align it with historical stock price data. Leveraging long short-term memory (LSTM) models, the system provides precise predictions of future stock trends. Automated data collection and processing are achieved through a Flask-based backend, while an OpenAI-powered chatbot delivers intuitive interpretations of predictions and EQ values. The user-centric design, implemented using Next.js, ensures a seamless and responsive experience. By integrating state-of-the-art machine learning techniques with intuitive interfaces, Insight Invest bridges the gap between complex predictive analytics and practical usability, offering a robust framework for informed investment strategies.
Design and analysis of low-k dielectric TSV liners for noise mitigation in high-frequency 3D ICs Pathakunta Guru Prathap Reddy; Sravan Abhilash Kothapalli
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1188-1196

Abstract

Moore’s Law has driven the development of very large-scale integration (VLSI) technology, allowing continuous transistor scaling to increase speed, density, and performance. However, as two-dimensional (2D) integrated circuits (ICs) near their physical and performance boundaries, and 2.5D ICs still face interconnect delay and power issues, three-dimensional (3D) integration has become a practical solution. In 3D ICs, multiple active layers are vertically stacked and connected via through-silicon vias (TSVs), providing short, high-bandwidth interconnects between layers. Electrical TSVs are essential for signal transmission, but also cause noise coupling between adjacent TSVs, where an aggressive TSV can induce interference in a nearby TSV. This coupling can impair signal integrity, increasing delay and power consumption. To mitigate this, low-dielectric-constant (low-k) materials are used to reduce capacitive coupling. In this study, materials such as benzocyclobutene (BCB), Perylene-N, and Teflon AF 1600 are compared with conventional SiO₂. Generally, TSVs are two structures — single-liner and stacked-liner — which are analysed at 10 GHz and 1 THz frequencies. At 10 GHz, the single-liner structure incorporating SiO₂ exhibits a noise reduction of about 6.56 dB, whereas the stacked-liner configuration using Teflon AF 1600 provides a noticeably greater reduction of 8.40 dB. As the operating frequency increases to 1 THz, the advantage of the low-k dielectric becomes more evident, yielding 9.63 dB noise reduction for the single-liner and 12.04 dB for the stacked-liner structure. These results indicate that low-k materials effectively suppress capacitive coupling and mitigate high-frequency interference in 3D ICs. The stacked-liner design contributes additional isolation by creating a secondary dielectric barrier, which further minimizes electric field interaction between neighboring interconnects. Thus, the integration of low-k dielectrics with optimized liner architectures significantly enhances signal integrity and overall electromagnetic performance in advanced high-frequency 3D IC systems.
Classification of encryption attacks and strategies for mitigation Anas Maaifi; Khalid Zine-Dine
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1238-1253

Abstract

Cryptography is essential for securing digital communications, yet it remains vulnerable to various malicious attacks. These attacks can be classified based on the type of cryptography they target symmetric or asymmetric. This paper presents a comprehensive classification of encryption attacks, examining the specific vulnerabilities associated with each cryptographic approach. By analyzing these attack vectors, the study shows the importance of understanding weaknesses in cryptographic systems. Furthermore, it proposes several mitigation strategies to strengthen defenses and enhance protection of sensitive information in the digital domain.
Wavelet-based spectrum sensing with improved thresholding for enhanced detection in cognitive radio networks Nur Hanis Abdul Rani; Mas Haslinda Mohamad; Nurusolihah Zamri; Nor Khairiah Ibrahim
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1123-1134

Abstract

Cognitive radio (CR) technology is an adaptive, intelligent radio and network technology that can automatically detect available channels in a wireless spectrum. Spectrum sensing is the most important component in CR due to its ability to sense and recognize parameters related to the radio channel characteristics. However, there are some spectrums that are not used known as spectrum holes. It is challenging to accurately identify these spectrum holes, especially when employing traditional energy detection techniques, which suffer from incorrect threshold selection at low signal-to noise ratio (SNR) levels. This work suggests a wavelet-based spectrum sensing technique in conjunction with an enhanced thresholding method to improve detection accuracy and decrease noise to overcome this constraint. MATLAB simulations are used for evaluating three threshold functions: hard, soft, and improved. The results indicate that the improved threshold achieves superior denoising performance and a higher detection probability compared to the traditional energy detection method. In this study, the energy detection technique was also implemented for comparison with the wavelet-based approach. The findings reveal that wavelet-based sensing consistently provides a higher detection probability (????), demonstrating its effectiveness and reliability for cognitive radio (CR) application.

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