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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 9,138 Documents
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Cyber physical systems maintenance with explainable unsupervised machine learning Jasti, V. Durga Prasad; Ashok, Koudegai; Gude, Ramarao; Kandukuri, Prabhakar; Bejjam, Surendra Nadh Benarji; B., Anusha
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp300-308

Abstract

As cyber-physical systems (CPS) continue to play a pivotal role in modern technological landscapes, the need for robust and transparent machine learning (ML) models becomes imperative. This research paper explores the integration of explainable artificial intelligence (XAI) principles into unsupervised machine learning (UML) techniques for enhancing the interpretability and understanding of complex relationships within CPS. The key focus areas include the application of self-organizing maps (SOMs) as a representative unsupervised learning algorithm and the incorporation of interpretable ML methodologies. The study delves into the challenges posed by the inherently intricate nature of CPS data, characterized by the fusion of physical processes and digital components. Traditional black-box approaches in unsupervised learning often hinder the comprehension of model-generated insights, making them less suitable for critical CPS applications. In response, this research introduces a novel framework that leverages SOMs, a powerful unsupervised technique, while concurrently ensuring interpretability through XAI techniques. The paper provides a comprehensive overview of existing XAI methods and their adaptation to unsupervised learning paradigms. Special emphasis is placed on developing transparent representations of learned patterns within the CPS domain. The proposed approach aims to enhance model interpretability through the generation of human-understandable visualizations and explanations, bridging the gap between advanced ML models and domain experts.
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.
Convolutional neural network DenseNet in classifying dyslexic handwriting images Pondayu, Chelsea Zaomi; Widodo, Widodo; Nugraheni, Murien
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp220-232

Abstract

Dyslexia is a specific learning disability (SLD) associated with word-level reading difficulties and often manifests in childhood handwriting through irregular spacing and inconsistent letter sizing, due to shared phonological and orthographic processing. Early identification is critical; however, traditional diagnostic procedures are time-consuming and unsuitable for large-scale screening. This study aimed to develop a handwriting analysis at the paragraph-level using a DenseNet121 convolutional neural network (CNN) model as a low-cost dyslexia screening tool for resource-constrained educational settings. One hundred English handwriting images were preprocessed and standardized into two hundred samples, with 70% of the dataset evaluated using 4-fold cross-validation and the remaining 30% used for testing. The model achieved 90% test accuracy and 92.86% training accuracy, significantly outperforming a random forest baseline that reached 83.57% train accuracy and 63.33% test accuracy, with statistical significance confirmed by McNemar’s test. The main contribution of this study is the demonstration that a lightweight, single-architecture DenseNet121 using paragraph-level analysis can achieve competitive performance compared to prior studies that relied on more complex hybrid models and character-level analysis, while requiring substantially lower computational resources and simplified pipeline. These findings indicate that DenseNet121 provides a robust and low-cost solution for preliminary dyslexia screening in resource-limited educational environments.
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.
YOLOv8m enhancement using α-scaled gradient-normalized sigmoid activation for intelligent vehicle classification V. Serrano, Renz Raniel; B. Delmo, Jen Aldwayne; M. Rosales, Cristina Amor
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp153-167

Abstract

Vehicle classification plays a vital part in the development of intelligent transportation systems (ITS) and modern traffic management, where the ability to detect and identify vehicles accurately in real time is essential for maintaining road efficiency and safety. This paper presents an enhancement to the YOLOv8m model by refining its activation function to achieve higher accuracy and faster response in diverse traffic and environmental situations. In this study, two alternative activation functions—Mish and Swish—were integrated into the YOLOv8m structure and tested against the model’s default sigmoid linear unit (SiLU). Training and evaluation were carried out using a comprehensive dataset of vehicles captured under different lighting and weather conditions. The experimental findings show that the modified activation design leads to better model convergence, improved generalization, and a noticeable boost in detection performance, recording up to 5.4% higher accuracy and 6.6% better mAP scores than the standard YOLOv8m. Overall, the results confirm that fine-tuning activation behavior can make deep learning models more adaptive and reliable for vehicle classification tasks in real-world intelligent transportation environments.
Comparative analysis of linear regression, random forest, and LightGBM for hepatitis disease prediction Tuhuteru, Hennie; Nivaan, Goldy Valendria; Rijoly, Marvelous Marvel; Tuhuteru, Joselina
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp430-438

Abstract

In bioinformatics research, computational pattern-analysis techniques are frequently employed to assist in disease prediction and diagnostic modeling, including applications for hepatitis prognosis. Hepatitis is a type of serious disease with various types that have the potential to threaten the life of the sufferer without showing significant symptoms and signs, so many sufferers do not realize that they are affected by the disease. Various methods are used to predict diseases in the hope of providing the best results from the learning model used. The objective of this study is to implement linear regression, random forest, and light gradient boosting machine (LightGBM) to estimate mortality risk among hepatitis patients. In addition, a performance comparison of the results of hepatitis disease prediction using the three algorithms was also carried out to find out which model gave the most accurate and optimal results. The results of this study show that the application of learning models from the linear regression, random forest and Light-GBM algorithms has been successfully carried out to predict the survival status of patients with hepatitis. The findings reveal that random forest achieved the highest predictive performance with an accuracy of 84%, followed by LightGBM at 77% and linear regression at 32%.
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.
Invisible watermarking as an additional forensic feature of e-meterai Rimbawa, H.A Danang; Alam, Sirojul; Saputro, Joko W.; Mantoro, Teddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp344-356

Abstract

The e-meterai is an official digital product of the Indonesian government issued by the Directorate General of Taxation (DGT). Its usage has become increasingly widespread as conventional documentation transitions to digital formats, serving the same function as its printed counterpart. This product features a quick-response code embedded with unique Indonesian codes and offers overt, covert, and forensic features. This study aims to experiment with adding a forensic feature in the form of an invisible watermark. We employed two watermark embedding techniques, discrete Fourier transform (DFT) and scale-invariant feature transform (SIFT), to determine which is more suitable for this application. After embedding the watermark, we also simulate various attacks including gaussian noise, salt and pepper noise, averaging filter, rotation, translation, and speckle noise. For each attack, we calculated with normalized-cross correlation (NCC) values, obtaining 0.863 and 0.976 for the gaussian noise attack, 0.929 and 0.984 for the salt and pepper attack, 0.975 and 0.984 for the averaging filter attack, 0.173 and 0.097 for rotation attacks, 0.172 and 0.032 for translation attack, and 0.972 and 0.996 for speckle noise attack, using DFT and SIFT techniques, respectively.
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.

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