International Journal of Informatics and Communication Technology (IJ-ICT)
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.
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<![CDATA[Explainable artificial intelligence for traffic signal detection using LIME algorithm ]]>
<![CDATA[Santhiya, P.]]>;
<![CDATA[Jebadurai, Immanuel Johnraja]]>;
<![CDATA[Leelipushpam Paulraj, Getzi Jeba]]>;
<![CDATA[Kirubakaran S, Stewart]]>;
<![CDATA[Keren L., Rubee]]>;
<![CDATA[Veemaraj, Ebenezer]]>;
<![CDATA[Sharance J. S., Randlin Paul]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp527-536
<![CDATA[As technology progresses, so does everything around us, such as televisions, mobile phones, and robots, which grow wiser. Of these technologies, artificial intelligence (AI) is used to aid the computer in making decisions comparable to humans, and this intelligence is supplied to the machine as a model. As AI deals with the concept of Black-Box, the model’s decisions were poorly comprehended by the end users. Explainable AI (XAI) is where humans can understand the judgments and decisions made by the AI. Earlier, the predictions made by the AI were not as easy as we know the data now, and there was some confusion regarding the predictions made by the AI. The intention for the use of XAI is to improve the user interface of products and services by helping them trust the decisions made by AI. The machine learning (ML) model White-box shows us the result that can be understood by the people in that domain, wherein the end users cannot understand the decisions. To further enhance traffic signal detection using XAI, the concept called local interpretable model- agnostic explanation (LIME) algorithm has been taken into consideration and the performance is improved in this paper.]]>
<![CDATA[Automated multi-document summarization using extractive-abstractive approaches ]]>
<![CDATA[Nasari, Maulin]]>;
<![CDATA[Girsang, Abba Suganda]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp400-409
<![CDATA[This study presents a multi-document text summarizing system that employs a hybrid approach, including both extractive and abstractive methods. The goal of document summarizing is to create a coherent and comprehensive summary that captures the essential information contained in the document. The difficulty in multi-document text summarization lies in the lengthy nature of the input material and the potential for redundant information. This study utilises a combination of methods to address this issue. This study uses the TextRank algorithm as an extractor for each document to condense the input sequence. This extractor is designed to retrieve crucial sentences from each document, which are then aggregated and utilised as input for the abstractor. This study uses bidirectional and auto-regressive transformers (BART) as an abstractor. This abstractor serves to condense the primary sentences in each document into a more cohesive summary. The evaluation of this text summarizing system was conducted using the ROUGE measure. The research yields ROUGE R1 and R2 scores of 41.95 and 14.81, respectively.]]>
<![CDATA[Fault detection in single-hop and multi-hop wireless sensor networks using a deep learning algorithm ]]>
<![CDATA[Padmasree, Ramineni]]>;
<![CDATA[Chaithanya, Aravalli Sainath]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp453-461
<![CDATA[The wireless sensor network (WSN) has received significant recognition for its positive impact on environmental monitoring, yet its reliability remains prone to faults. Common factors contributing to faults include connectivity loss from malfunctioning node interfaces, disruptions caused by obstacles, and increased packet loss due to noise or congestion. This research employs a variety of machine learning and deep learning techniques to identify and address these faults, aiming to enhance the overall lifespan and scalability of the WSN. Classification models such as support vector machine (SVM), gradient boosting clasifer (GBC), K-nearest neighbours (KNN), random forest, and decision tree were employed in model training, with the decision tree emerging as the most accurate at 90.23%. Additionally, a deep learning approach, the recurrent neural network (RNN), effectively identified faults in sensor nodes, achieving an accuracy of 93.19%.]]>
<![CDATA[Utilization of the use of technological devices in delivering communication information in the learning process ]]>
<![CDATA[Irsyad, Irsyad]]>;
<![CDATA[Anisah, Anisah]]>;
<![CDATA[Ramadhan, Iwan]]>;
<![CDATA[Lumbantoruan, Jitu Halomoan]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp547-555
<![CDATA[The development of increasingly sophisticated technology today brings education to participate in using the features available in today’s media such as the transition from face-to-face learning communication in schools to face-to-face assisted by technology such as laptops, tablets, cellphones and other multimedia. Technology currently provides innovation in the learning process both from home and from school. However, there are still many teachers and students who have not utilized technology such as laptops, tablets, cellphones and other multimedia in the learning process. The purpose of the study was to analyze the benefits and relationships of the four main variables of communication assessment elements with digital devices. The research method used was quantitative with a sample of 148 teachers randomly selected from schools that use technology in the learning process. Data collection techniques with instruments. The instruments used were four indicator instruments, namely technology from laptops, tablets, cellphones and other multimedia. Data analysis techniques with descriptive statistics using SPSS version 26.0 calculated the mean, standard deviation, and correlation test. The results of the study found that the four indicators had high reliability and the four indicators had significant utilization, were mutually positive and had a high relationship with each other. The conclusion is that the four technological devices are good for use in digital communication during the learning process and laptops and tablets are more recommended in this study.]]>
<![CDATA[Machine learning-driven design and performance analysis of microstrip antennas for sub-6 GHz/mm Wave 5G networks ]]>
<![CDATA[Prasanna Kumar, Piske Laxmi]]>;
<![CDATA[Padmasree, Ramineni]]>;
<![CDATA[Kiran, Korra]]>;
<![CDATA[Sudheer, Banothu]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp462-469
<![CDATA[In the realm of modern communication systems, antennas are crucial components, with the microstrip patch antenna being particularly notable for its low profile and seamless integration. Despite its widespread use, designing this antenna involves complex simulations to optimize parameters, requiring significant expertise and consuming considerable time and energy. To streamline this process, machine learning (ML) algorithms are being utilized. This paper introduces an innovative approach that employs ML techniques to design a rectangular microstrip patch antenna operating within the sub-6 GHz frequency range (1-6 GHz) and the millimeter frequency range (28-40 GHz). The antenna design maintains consistent patch dimensions positioned strategically at the center, with a thorough examination of patch length and width to enhance performance. Datasets are meticulously prepared, covering output parameters such as beam area, directivity, gain, and radiation efficiency across the specified frequency ranges. By employing various ML algorithms, this study conducts a comprehensive analysis to identify the most effective algorithm for accurately predicting antenna characteristics. The K-nearest neighbor (KNN) algorithm achieved high accuracy across all parameters: gain at 94.23% under sub-6 GHz and 95.93% under millimeter frequency range, directivity at 99.02% and 98.59%, radiation efficiency at 93.94% and 94.28%, and beam area at 99.07% and 98.59% respectively. These results optimize microstrip antenna designs and enhance understanding of the relationship between design parameters and performance outcomes with ML.]]>
<![CDATA[Optimized support vector machine for sentiment analysis of game reviews ]]>
<![CDATA[Supriyatna, Bryan Leonardo]]>;
<![CDATA[Putri, Farica Perdana]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp344-353
<![CDATA[The rapid development of games has made game categories diverse, so there are many opinions about games that have been released. Sentiment analysis on game reviews is needed to attract potential players. Sentiment analysis is carried out using the support vector machine (SVM) and particle swarm optimization (PSO) algorithms. SVM training was conducted with a linear kernel, the ‘C’ value parameter was 10 resulting in an accuracy value of 97.28%. The SVM algorithm optimized using the PSO method produces an accuracy of 97.61% using the parameters c1 is 0.2, c2 is 0.5 and w is 0.6. Based on these results, sentiment analysis using PSO-based SVM optimization has been successfully carried out with an increase in accuracy of 0.33%. This game review has a sentiment value from neutral to positive so this game can be recommended to other players.]]>
<![CDATA[Enhancement of liner materials based on nanomaterials to promote sustainability in noise intercourse ]]>
<![CDATA[Kumar, Malagonda Siva]]>;
<![CDATA[Mohanraj, Jayavelu]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp476-483
<![CDATA[Daily usage of devices has had a major influence on lives and existence, which would be unimaginable without them. Due to this, recent gadget dependability concerns need particular attention. PCs, hand mobile phones, and other computerized household gadgets need integrated circuits (ICs). Individual components must work together to accomplish their tasks and make the circuit operate. Hot carrier effect, oxide breakdown, and other system-level problems result from accommodating several devices in a planar IC. Vertical linking active components in one IC to another IC is a common method of three-dimensional IC integration (3D-IC). The main issue with 3D-IC adoption is electrical interference to neighboring through silicon via (TSV) and active transistors, which substantially reduces system performance. The electrical TSV (ETSV) model, which employs solely electrical signal carrying TSV, and the thermal TSV (TTSV) model, which incorporates thermal TSV during simulation, are used in this research to reduce electrical interference. The electrical signal transporting TSV to the substrate and other TSV was investigated for interference. With other models, this study also shows higher frequency regimes up to 1 THz. We found that the suggested methodology improves 3D-IC development by more than 30% by reducing electrical interference from signal-carrying TSV to other TSV.]]>
<![CDATA[Utilizing RoBERTa and XLM-RoBERTa pre-trained model for structured sentiment analysis ]]>
<![CDATA[Putri Masaling, Nikita Ananda]]>;
<![CDATA[Suhartono, Derwin]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp410-421
<![CDATA[The surge in internet usage has amplified the trend of expressing sentiments across various platforms, particularly in e-commerce. Traditional sentiment analysis methods, such as aspect-based sentiment analysis (ABSA) and targeted sentiment analysis, fall short in identifying the relationships between opinion tuples. Moreover, conventional machine learning approaches often yield inadequate results. To address these limitations, this study introduces an approach that leverages the attention values of pre-trained RoBERTa and XLM-RoBERTa models for structured sentiment analysis. This method aims to predict all opinion tuples and their relationships collectively, providing a more comprehensive sentiment analysis. The proposed model demonstrates significant improvements over existing techniques, with the XLM-RoBERTa model achieving a notable sentiment graph F1 (SF1) score of 64.6% on the OpeNEREN dataset. Additionally, the RoBERTa model showed satisfactory performance on the multi-perspective question answer (MPQA) and DSUnis datasets, with SF1 scores of 25.3% and 29.9%, respectively, surpassing baseline models. These results underscore the potential of this proposed approach in enhancing sentiment analysis across diverse datasets, making it highly applicable for both academic research and practical applications in various industries.]]>
<![CDATA[Ensemble stacking classifier model for prediction of diabetes ]]>
<![CDATA[Bhandarkar, Mrunalini]]>;
<![CDATA[Bendre, Varsha S.]]>;
<![CDATA[Bellary, Yash Venkatesh]]>;
<![CDATA[Bhole, Anuj Kiran]]>;
<![CDATA[Bhadange, Abhishek Abasaheb]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp499-508
<![CDATA[Diabetes, being a chronic condition, possesses the capacity to instigate a global healthcare catastrophe. This condition can be managed and potentially cured with prompt diagnosis and treatment. Integrating machine learning technology with medical science enables precise prognosis of an individual’s susceptibility to diabetes. The proposed work presents the ensemble stacking classifier model. This efficient and effective diabetes prediction model predicts a patient’s diabetes risk by combining the output of multiple machine-learning techniques into a single model. The performance parameters of four distinct machine learning classification algorithms K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and decision tree (DT) are compared in this study with those of the proposed stacked classifier model. The suggested model is developed using ensemble methods, where the previously discussed algorithms are integrated to create the base classifier layer of the stack classifier. The meta-classifier is implemented in the form of the logistic regression (LR) algorithm. Upon evaluating the performance of both the developed model and its algorithms, it is proved that the proposed model attains a testing accuracy of 88.5%, surpassing the accuracy of all baseline classification algorithms. As a result, this work determines that the ensemble stacking classifier model exhibits higher prediction accuracy than the base classifier algorithms. This finding underscores the model’s potential as a viable instrument for predicting diabetes in individuals.]]>
<![CDATA[Entanglement classification – a comparative study of ??(?) and ??(?) developed operator model ]]>
<![CDATA[Zhahir, Amirul Asyraf]]>;
<![CDATA[Mohd, Siti Munirah]]>;
<![CDATA[M. Shuhud, Mohd Ilias]]>;
<![CDATA[Idrus, Bahari]]>;
<![CDATA[Zainuddin, Hishamuddin]]>;
<![CDATA[Mohamad Jan, Nurhidaya]]>;
<![CDATA[Wahiddin, Mohamed Ridza]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i3.pp556-562
<![CDATA[Entanglement classification is a core aspect of quantum information theory. It ensures successful quantum information processing. This article presents a comparative study of entanglement classification using developed operator models for the special unitary group and special linear group. This study was built upon prior work in entanglement classification in a pure three-qubit quantum system environment, where the operator models for each mathematical group were independently developed. Through extensive analysis, both synthesized models are functionally effective and yield the desired results. However, the comparative analysis reveals that the operator model exhibits certain limitations, particularly in its early phase of development compared to. This study provides significant enlightenments into the practical abilities of the developed operator models in entanglement classification and underlines the theoretical distinction between and paving the path for future research in quantum information theory, specifically entanglement classification.]]>
