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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 49 Documents
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Search results for , issue "Vol 40, No 3: December 2025" : 49 Documents clear
SCADE: a deep learning ensemble for semantic flow analysis in smart contract vulnerability detection Srirama, Muralidhara; Banavikal Ajay, Usha
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1417-1429

Abstract

A vulnerability in smart contracts refers to weaknesses in the code that can be exploited by attackers, leading to security breaches and unintended behavior. With the growing use of smart contracts in decentralized blockchain systems, particularly in internet of things (IoT) environments, ensuring their security has become increasingly critical. Traditional vulnerability detection techniques, such as formal verification and symbolic execution, face significant limitations, including high rates of false positives and negatives, scalability issues, and difficulty in detecting complex vulnerabilities. To address these challenges, this paper proposes semantic contract flow analysis and deep learning ensemble (SCADE) for smart contract vulnerability detection. SCADE leverages semantic flow analysis combined with an ensemble of deep learning models, including convolutional neural networks (CNN), bidirectional sequence encoder (BSE), layered probabilistic neural network (LPNN), and adaptive context learning network (ACLN), to detect vulnerabilities effectively. The methodology breaks down the smart contract code into structured components through a contract structure mapper, followed by extracting semantic paths and converting them into sequential vector representations. These representations are then processed through a deep learning ensemble to identify potential vulnerabilities such as reentrancy, timestamp dependency, code injection, and hardcoded gas amounts.
Effective medium ratio obeying metamaterial absorber for 5G sub-7 GHz and sub-8 GHz applications Jakir Hossain, Mohammad; Uddin, Md. Alim; Islam, Md. Mesbahul; Ghosh, Keya; Jahan, Nusrat; Akter Asma, Mukta
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1368-1376

Abstract

Metamaterials possess the capability to enhance fifth-generation (5G) communication technology. This article proposes an innovative construction of a miniature metamaterial absorber (MMA) with a dramatically improved effective medium ratio (EMR) characterized by utilizing a multi-square split-ring resonator (MSSRR) MMA unit cell specifically designed for operation in the 5G sub-7 GHz and Sub-8 GHz frequency bands. The unit cell of the MMA is designed using a commercially available FR-4 material with εr=4.3, which is cost-effective. The proposed MMA achieves a remarkably high EMR of 9.83, indicating superior compactness and design efficiency. The MMA of interest operates with absorbance peaks of 70.632%, 96.936%, and 79.930% within the frequencies of 3.554 GHz, 4.940 GHz, and 8.335 GHz, respectively. Along with the absorption analysis, our examination also includes E-field, H-field, surface current, and power flow. The expected MMA has proven potential for application in some frequency bands related to 5G, released absorption signal, and specific absorption rate (SAR) assistance.
Partitioning hazy images using interactive active contour models Ahmad Khairul Anuar, Firhan Azri; Jone, Jenevy; Aiesya Raja Azhar, Raja Farhatul; Kadir Jumaat, Abdul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1317-1324

Abstract

Image partitioning, also known as image segmentation, is a process that involves dividing an image into distinct and meaningful segments. Recently, an interactive active contour model (ACM) namely the Gaussian regularization selective segmentation (GRSS) was designed to handle images with intensity inhomogeneity effectively. However, the GRSS model shows limited performance when applied to hazy images, which often results in incomplete detection and inaccurate extraction of the target object. This study reformulates the GRSS model by integrating the simple dark channel prior (SimpleDCP) dehazing technique, producing a modified model referred to as GRSS with SimpleDCP (GRSSD). The model is derived and implemented in MATLAB software. Experimental results show that the GRSSD model achieves improved segmentation accuracy (ACU) compared with the GRSS model. On average, the ACU improved by 1.8%, while the error metric (EM) decreased from 0.053 to 0.036, representing a reduction of about 32%. The Dice and Jaccard indices improved by approximately 2.6% and 4.9%, respectively. Although the computation time increased, the enhancement in segmentation ACU demonstrates the benefit of incorporating a dehazing process into the variational formulation. The proposed GRSSD model can be extended to color and three-dimensional image segmentation in future work.
IoT-based intelligent crop rotation and recommendation system V. Nuada, Dave Emmanuel; M. Velonta, Jerald; G. Tuazon, Christian Neri; D. Mallari, Jimuel Nyle; P. Pinpin, Arzel; Dela Cruz, Grosby A.; O. Mallari, Marvin
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1539-1548

Abstract

Traditional farming practices often rely on manual monitoring and crop selection, leading to inefficient use of resources and limited crop diversification. This study addresses these issues through the development of an IoT-based intelligent crop rotation and recommendation system that automates crop monitoring, irrigation, and crop selection processes. The system integrates DHT11 and NPK sensors to measure temperature, humidity, soil moisture, and nutrient levels (N, P, K), with real-time data displayed on a web-based application interface. An automated irrigation and fertilizer subsystem with SMS notifications enhances user control and remote accessibility. A crop recommendation module using the Euclidean Distance algorithm analyzes soil-nutrient data to identify the most suitable crops for the next planting cycle. System evaluation based on the ISO/IEC 25010 software quality model indicated high functionality, usability, reliability, portability, and maintainability, with an overall weighted mean of 3.958 (Agree) and a cronbach’s alpha of 0.9585, signifying excellent reliability. The system demonstrates the potential of internet of things (IoT)- based technologies in promoting crop diversification, optimizing farm productivity, and advancing sustainable agricultural practices.
Abstractive and extractive based YouTube transcript summarization: a hybrid approach Sadashiv, Naidila; Krishna Maiya, Aneesha; Shivareddy, Geetha; Reddy, Akash
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1439-1452

Abstract

The rapid advancement in the field of communication and ubiquitous access to computing has led to the proliferation of large amounts of video content on YouTube and other social media platforms. However, getting precise information from the video in concise textual manner remains a challenge. Different extractive and abstractive text summarization methods are prevalent in the literature. In this paper, classical extractive text summarization methods Luhn’s algorithm, TextRank algorithm and Keyword- based summarization are combined to develop a combined extractive (CE) method. To enhance its performance, bidirectional and auto-regressive transformers (BART) is investigated and integrated as a hybrid model. Further, we explore how Kmeans clustering algorithm can be used for text summarization in general and with the proposed hybrid approach for improvement in text summarization. Using CNN/DailyMail dataset, assessment of text summarization methods based on ROUGE scores and time taken for summary generation is carried out. Based on the ROUGE score, we observe that the proposed hybrid method - 0.2644 is better than traditional extractive summarization methods. The combination of hybrid method with K-means further improved the score to 0.3227. The time taken by them for summary generation are 138.09 and 142.16 seconds respectively. This work experimented with different classical and transformer-based text summarization techniques to explore the complementary aspects and the results obtained are comparable with that of existing models with less time for text summarization.
Augmented reality in the context of universal design for hearing impaired student Luangrungruang, Tidarat; Phatai, Gawalee
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1650-1658

Abstract

Advancing equal rights and prohibiting discrimination based on disability are essential to achieving social equity. Education serves as a vital mechanism in this effort, particularly through inclusive practices that support diverse learners. Sakon Nakhon Rajabhat University advances these values by admitting students with disabilities, including those with hearing impairments, and by fostering accessible learning environments. This study presents the development of an augmented reality (AR) application, designed according to universal design (UD) principles, to enhance learning for students with hearing impairments. The AR technology integrates real and virtual elements to create an engaging and interactive educational experience. Evaluation results indicate a high level of effectiveness, with the assessment dimension receiving the highest mean score (? = 4.87, ?? = 0.35), and overall effectiveness rated similarly (? = 4.78, ?? = 0.42). User satisfaction was also rated at a very high level across all aspects (? = 4.67, ?? = 0.54). These findings highlight the potential of AR technology, when guided by inclusive design principles, to improve learning outcomes for students with hearing impairments.
Fuzzy logic control of a hybrid PV/battery/diesel generator system integrated in an electrical network: case study of City of Douala Bading Epanda, Alain; Nyobe Yome, Jean Maurice; Thierry Sosso, Olivier; Ele, Pierre
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1720-1734

Abstract

The control of hybrid systems is a considerable challenge for the energy supply to consumers. For this purpose, this study implemented an intelligent control for a hybrid system connected to the electrical grid to meet the energy demand of a building in the city of Douala, Cameroon. In this work, an intelligent management system using fuzzy logic is proposed to overcome the challenges of this multi-source integration. The proposed method based on a fuzzy logic controller makes it possible to optimize the performance of the energy sources used with a coordination system. Thus, it makes it possible to adjust in real time the system control process based on climatic conditions and the characteristics of the storage devices in order to provide an adequate adaptive control strategy. Furthermore, this system effectively balances the energy supply from all sources. MATLAB/Simulink software and real building data are used to simulate the proposed intelligent management strategy. The results obtained indicate that energy is efficiently supplied to consumers with efficiency of 98% and reduction of fuel consumption of 45% based on the availability of the sources, thus demonstrating the benefits of the control strategy based on fuzzy logic for balanced system operation.
Performance evaluation of path planning algorithms for blind people Mosquera-Ortega, Paula; Díaz-Toro, Andrés; Villamizar-Carrillo, Anyela; Campaña-Bastidas, Sixto
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1638-1649

Abstract

Blind people face difficulties in identifying objects of interest and moving to them safely and efficiently in unfamiliar environments. Thanks to highperformance computers, high-quality sensors and artificial intelligence algorithms, it is possible to perform real-time tasks such as locating the user, generating occupancy grids that represent the environment and identifying objects of interest. From this information, paths can be generated that allow the user to reach a point of interest in an optimal way. This paper presents the performance evaluation of four path planning algorithms that were implemented in MATLAB and tested with synthetically generated occupancy grids, varying their size and occupancy percentage. The evaluation criteria include time to reach the goal, number of expanded cells and number of cells in the path. In addition, a single indicator that integrates all performance criteria is proposed to identify the best algorithm. The results show that the A* algorithm presents the best performance in static environments, under certain hardware requirements for data processing and restrictions on grid size for real-time applications. These findings expand the fields of application of path planning algorithms, quantify their performance under different conditions of the environment, and make them attractive for implementation in embedded systems.
Comparing machine learning and binary regression approach for motor insurance prediction Sefina Samosir, Ridha; Bazán Guzmán, Jorge Luis; Halim, Giselle
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1576-1585

Abstract

This study compares the performance of binary regression with the power cauchit (PC) link function and random forest in predicting motor insurance policyholder behavior using an imbalanced dataset. The dataset comprises 4,000 policyholders, with the response variable indicating whether a client purchased a full coverage plan (1) or not (0). Predictors include characteristics such as men, urban, private, age, and seniority. Binary regression was implemented using PyStan, while random forest was created with scikit-learn without additional hyperparameter tuning. Results demonstrate that random forest outperformed binary regression in a range of performance metrics, as well as specialized metrics suitable for imbalanced data. Findings point to the effectiveness of machine learning (ML) algorithms, exemplified by random forest, offer more robust performance in handling complex, imbalanced datasets compared to traditional statistical models. This highlights the potential of random forest to improve predictive accuracy in applications such as motor insurance policyholder behavior analysis.
Sentiment analysis in Arabic and dialects: a review utilizing a corpus-based approach Hussein Ali, Abbas; Barişçi, Necaattin
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1500-1516

Abstract

Arabic is one of the most morphologically complex languages, and its numerous dialects render identifying sentiment in digital communication a challenging task. In this study, we conduct a systematic literature review (SLR) to investigate the sentiment analysis (SA) techniques used on modern standard Arabic (MSA) and several Arabic dialects (AD) between 2020 and 2024. A corpus-based analysis of 71 articles indicated that machine learning (ML) and deep learning (DL) algorithms were the dominant methods used. Overall, the most frequently studied dialects are those from Saudi Arabia, Morocco, and to a lesser extent, Algeria, among various algorithms used for text classification, including support vector machines (SVM) and convolutional neural networks (CNN). These techniques emerged as some of the most effective strategies employed for sentiment classification. While new contemporary word embeddings, such as Word2Vec, are gaining traction in the field, traditional feature extraction methods, like term frequency-inverse document frequency (TF-IDF), continue to outperform them. The study highlights the importance of additional labeled datasets and tailored models in navigating the linguistically rich world of AD. Additionally, the results highlight the need for dialect-specific adaptations to improve SA outcomes, and further investigation is needed by leveraging advanced DL methodologies, as well as improved data resources, to address these issues.

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