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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,199 Documents
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Ontology-based semantic link prediction for enhancing academic collaboration through knowledge management Thu Thuy, Pham Thi; Thi Thuy, Thinh
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1040-1048

Abstract

This paper introduces a novel ontology-based semantic link prediction framework that unifies structural, temporal, and semantic signals from heterogeneous scholarly sources to enhance academic collaboration forecasting. By integrating AMiner, DBLP, and Mendeley datasets into a unified SKOS- and Dublin Core-aligned ontology, the framework enables semantic enrichment, cross-source reasoning, and contextualized link prediction. Unlike previous studies that focus solely on structural features or basic content similarity, our approach leverages ontology-based semantic feature engineering and graph-based learning for robust and interpretable predictions. Experimental results show that random forest and graph neural networks significantly outperform traditional models, achieving high accuracy and ranking precision. This work contributes to knowledge management by enabling expert recommendation, trend identification, and semantic integration for strategic academic planning.
Mobility-aware adaptive tag selection strategy in ambient backscatter systems Abera Mulatu, Mengistu; Dlamini, Thembelihle; Nkosingiphile Nyembe, Wiseman; Police Ncube, Zenzo; Mulatu Beyene, Asrat
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp924-934

Abstract

Ambient backscatter communication (AmBC) has emerged as a promising solution to enable ultra-low-power connectivity in large-scale internet of things (IoTs) and future 6G mobile networks. In this paper, we consider a mobility-aware AmBC system, where a mobile user equipped with a reader in teracts with multiple passive tags deployed in the coverage area of a base station (BS). To achieve high decoding reliability, an adaptive tag selection scheme is proposed based on received signal strength (RSS) and interference constraints. Here, we derive a closed-form expression of the outage probabilities of both the mobile user and tags taking into account the Rayleigh double-fading nature of backscatter links. Performance evaluation carried out through simulations vali dates the theoretical analysis based on various selected system parameters. The results obtained show that the proposed adaptive scheme significantly improves system reliability compared to fixed tag selection strategies, thus emphasizing the importance of mobility-aware and context-driven adaptation in mobile IoT scenarios such as smart transportation and aerial data collection.
An innovative deep learning based approach for anomaly detection in intelligent video surveillance Pallewar, Megha G.; Pawar, Vijaya R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1105-1116

Abstract

Nowadays, anomaly detection has gained vital importance as security is a major concern everywhere. This work focuses on developing an intelligent video surveillance system capable of detecting anomalous activities in videos, utilizing the UCF Crime dataset as the primary source. The proposed model employed a multistage method uniting the convolutional neural networks (CNN) and long short-term memory (LSTM) networks. In the proposed approach, video frames serve as input to the CNN, which processes them to extract key features. These features are then passed to an LSTM network to capture temporal dependencies and identify anomalous events over time. This CNN-LSTM architecture successfully detects twelve distinct types of anomalous activities: abuse, arrest, arson, assault, burglary, explosion, fight, road accident, robbery, stealing, shoplifting, and vandalism. The dataset is divided into portions for training, testing, and validation, along with cross-validation to ensure model generalization. The system achieves an accuracy of 98.6%, reflecting a significant improvement of 4-5% over existing systems. This demonstrates the robustness of the proposed method in detecting anomalous behavior in video data.
A microservice-oriented machine learning framework for cold chain management in perishable fish logistics Jamaludin, Maun; Ginanjar, Arief; Herdiani, Leni; Ramadhan, Toto; Alif Naufal, Muhammad; Ismet Rohimat, R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1070-1081

Abstract

This study proposes a microservice-oriented machine learning framework to enhance intelligence and scalability in perishable fish cold chain logistics. Unlike conventional monitoring-centric systems, the framework integrates edge–cloud computing with multimodal machine learning models, including random forest for anomaly detection, long short-term memory (LSTM) for spoilage risk prediction, and convolutional neural network (CNN) for visual fish quality classification. The research adopts a design science approach combining literature analysis, field observations at cold storage facilities in Indramayu, Indonesia, and simulation-based validation. Experimental results demonstrate the feasibility of distributed analytics, modular deployment, and real-time inference within heterogeneous logistics environments. The proposed framework provides a deployable architectural reference for intelligent fisheries cold chain management and supports future large-scale, multi-stakeholder implementation.
Classification of DoS/distributed DoS threats in software defined networks using advanced deep belief network-long short term memory architecture Maraiah, Manjula; Venkatesh, Venkatesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1000-1016

Abstract

With the evolution of telecommunication core and access networks, the next generation networks leverages software defined networks (SDN) to provide flexi bility, scalability and centralized control. Denial of service (DoS)/distributed DoS (DDoS) attacks have been a major threat to next generation networks especially to the centralized architecture of SDNs. The ever-changing and dynamic nature of the DoS/DDoS attacks makes it challenging to detect and resolve them. The existing models to handle DoS/DDoS attacks often suffer from false positive rates and adaptability. In order to solve these problems, this study aims to create and apply sophisticated deep learning framework namely adversarial DBN-LSTM to accurately detect and classify various DoS/DDoS attack types. The proposed adversarial DBN-LSTM model is based on the generative adversarial networks. The proposed model uses generator to generate the adversarial attack and discrim inator to detect the attacks. The adversarial DBN-LSTM model is evaluated using a dataset specifically generated in a Mininet-based SDN controller environment to ensure relevance and practical applicability. The performance of the adver sarial DBN-LSTM is compared with other prevalent models. The adversarial DBN-LSTMmodelachieves accuracy about 99.4%. The proposed work achieves a breakthrough in identifying and preventing DoS/DDoS threats in relation to SDNenvironment.
Design and implementation of novel encryption architecture using mix column with novel adder Appisetty, Radha; Kumar, Munuswamy Siva
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1134-1140

Abstract

Digital information is extremely simple to process these days, but it can be accessed by unauthorized people. Cryptography is one of the most effective and widely used methods for data security, to protect this information. The cryptography techniques are becoming popular and widely adopted due to the security threats during data transmission. An essential part of a cryptographic system, cryptography algorithms are developed and implemented to increase data security. The developers of these cryptographic algorithms took into consideration additional parameters, including speed, resource consumption, reliability, usage type, and flexibility, even if their primary goals are confidentiality, integrity, and authenticity. It’s important to understand that each component affects the way that a cryptographic technique is designed. Hence, this analysis presents the design and implementation of a novel encryption architecture using mix column with a novel adder. The novel encryption algorithm is designed for an encryption architecture (EA) with mix column using novel adder. This novel encryption algorithm will attain better security and performance.
Arich and balanced phonetics corpus for modern standard Arabic ASR systems Boutazart, Youssef; Laaïdi, Naouar; Ezzine, Abderrahim; Satori, Hassan; Taj Bennani, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1049-1059

Abstract

This research delves into the creation of an innovative Modern Standard Ara bic corpus, aiming for a comprehensive balance and richness while adhering to Zipf’s law. Building a phonetically diverse Arabic sentence collection yields significant advantages in terms of efficiency, cost-effectiveness, and storage ca pacity compared to conventional corpora. The corpus undergoes meticulous seg mentation into graphemes, which are then manually converted into phonemes, resulting in a total of 19769 phonemic units. Among these phonemes, conso nants like ’Laam- l’ account for 10%, while ’Fatha- A’ vowels constitute 20%. Evaluation of this corpus using an automatic speech recognition (ASR) system reveals a sentence error rate (SER) of 30% and a word error rate (WER) of 15%. Furthermore, statistical analysis unveils that diacritic marks encompass 47.59% of the corpus, with graphemes comprising the remaining 52.41%. These dia critized marks provide valuable insights into the precise phonetic transcription of the corpus. Additionally, the study provides detailed breakdowns of consonants based on their place and manner of articulation, enhancing our understanding of phonetic structures.
Overvoltage assessment of wind energy integration in low voltage distributed grids Merahi, Farid; Abd Essalam, Badoud
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp859-872

Abstract

Large-scale integration of renewable energy (RE) resources into the electrical grid has increased significantly over the last decade, affecting the network at various nodes even at considerable distances from the common connection point. This paper presents an overvoltage assessment caused by the integration of two wind generators (WGs) into a low voltage distribution grid, which is structured into three zones. Two scenarios are studied, the first one considers the low voltage grid without WGs, representing its natural operating condition. In the second scenario, two WGs are connected in zone 3, inducing voltage rises at different nodes within the same zone, by reaching 7.9%, and affecting nodes located in other zones (Zone 1 and Zone 2). The simulation is performed using MATLAB/Simulink (R2025a), and the results obtained are compared to the standards test feeder IEEE 33-bus network, showing the overvoltage caused by WGs integration at nodes close to the connection point while improving voltage quality at distant nodes.
A hybrid approach for measuring semantic similarity in lexically identical but ambiguous sentences El Janati, Btissam; Enaanai, Adil; Ghanimi, Fadoua
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp954-965

Abstract

This study addresses the critical challenge of semantic similarity and lexical disambiguation in natural language processing, focusing on sentences with structural and lexical ambiguities. We introduce an innovative hybrid approach that synergistically combines symbolic and neural methods to better align with human judgment. Our methodology dynamically integrates fuzzy Jaccard’s lexical precision with SBERT embeddings’ contextual sensitivity, enabling adaptive semantic ambiguity resolution. Experimental evaluation on 33 ambiguous sentences demonstrates that our approach significantly outperforms conventional artificial intelligence (AI) systems, achieving an 11.7% reduction in mean absolute error compared to reference models, with statistical analysis confirming robust results (d = -0.80, p < 0.001). This represents a 65% improvement in human evaluation alignment over existing methods. Our research contributes to advancing the field by showing that architectural intelligence can surpass mere parameter scaling, offering an effective solution for applications requiring both precision and interpretability, with promising directions for multilingual extension and explainable AI integration.

Page 920 of 920 | Total Record : 9199

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