Indonesian Journal of Electrical Engineering and Computer Science
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Advancing intelligent, sustainable, and secure engineering systems for future technologies
Get PDFThis editorial introduces Volume 41, Number 1, January 2026, of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), highlighting pivotal research trajectories expected to influence future progress in electrical engineering and computer science. Instead of covering all aspects of the field, this issue is structured around three strategic macroclusters: intelligent and sustainable engineering systems, AI-driven healthcare and human-centered technologies, and secure, comprehensible, and interconnected intelligent infrastructure. These themes show how artificial intelligence, sustainability, and security are coming together more and more in modern engineering applications. The editorial talks about how important intelligent energy systems, advanced control and hardware solutions, data-driven healthcare innovations, and reliable digital infrastructures are for solving global technological problems. This issue's contributions demonstrate IJEECS's dedication to publishing significant, cross-disciplinary research that bridges theory and practice. This issue of the journal makes it clear that it is a progressive platform that wants to promote smart, long-lasting, and safe technologies for the engineering systems of the future
Towards adapting the consensus proof of authentication algorithm for IoT
Get PDFThe Internet of Things (IoT) represents an increasingly sophisticated paradigm which interconnects heterogeneous devices, enabling continuous data exchange and automation. However, IoT systems face significant challenges related to scalability, limited device resources, and data security. Blockchain technology provides an effective foundation for addressing such challenges thanks to its decentralized structure and consensus algorithms. This work focuses on improving the blockchain consensus protocol or consensus algorithm referred to as proof of authentication (PoAh) for adaptation to IoT networks using smart contract. It also presents a comparison of various existing consensus algorithms and explores different blockchain open-source platforms and their adaptation to IoT. Although experimental validation remains part of future work, the conceptual design and theoretical analysis presented here lay the groundwork for the future implementation and evaluation of the improved PoAh within real IoT use cases
Cyber physical systems maintenance with explainable unsupervised machine learning
Get PDFAs 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
Comparative analysis of linear regression, random forest, and LightGBM for hepatitis disease prediction
Get PDFIn 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%
Lung cancer segmentation and classification using hybrid CNN-LSTM model
Get PDFA collection of genetic disorders and various types of abnormalities in the metabolism lead to cancer, a fatal disease. Lung and colon cancer are found to be main causes of death and infirmity in people. When choosing the best course of treatment, the diagnosis of these tumors is usually the most important consideration. This study's main objectives are to classify lung cancer and its severity, as well as to recognize malignant lung nodules. The suggested approach additionally classifies the stages of lung cancer in order to recognize lung nodules. The convolutional neural network (CNN) is used to detect lung nodules, identifying a nodule which is accurately segmented and classified. The suggested method is separated into dual parts: primarily, it classifies normal and abnormal behavior, and the subsequent one classifies the different stages of lung cancer. Texture and intensity-based features are extracted during the classification stage. When compared to other methods such as nested long short-term memory (LSTM)+ CNN, the hybrid CNN LSTM obtains superior outcomes in terms of accuracy (99.35%), specificity (99.30%), sensitivity (99.32%), and F1-score (99.29%)
Weighted fine-tuned BERT-based sparse RNN for fake news detection
Get PDFFake news refers to misinformation or false reports shared in the form of images, articles, or videos that are disguised as real news to try to manipulate people’s opinions. However, detection systems fail to capture diverse features of fake news due to variability in linguistic styles, contexts, and sources, which lead to inaccurate identification. For this purpose, a weighted fine-tuned-bidirectional encoder representation for transformer based sparse recurrent neural network (WFT-BERT-SRNN) is proposed for fake news detection using deep learning (DL). Initially, data is acquired from Buzzfeed PolitiFact, Fakeddit, and Weibo datasets to evaluate WFT BERT-SRNN. Pre-processing is established using stopword removal, tokenization, and stemming to eliminate unwanted phrases or words. Then, WFT-BERT is employed to extract features. Finally, SRNN is employed to detect and classify fake news as real or fake. Existing techniques like deep neural networks for Fake news detection (DeepFake), BERT with joint learning, and multi-EDU structure for Fake news detection (EDU4FD), Image caption-based technique, and fine-grained multimodal fusion network (FMFN) are compared with WFT-BERT-SRNN. The WFT-BERT-SRNN achieves a better accuracy of 0.9847, 0.9724, 0.9624, and 0.9725 for Buzzfeed, Politifact, Fakeddit, and Weibo datasets compared to existing techniques like DeepFake, BERT-joint framework, EDU4FD, Image caption-based technique, and FMFN
Survey on prediction, classification and tracking of neurodegenerative diseases
Get PDFNeurodegenerative diseases (NDD) such as Alzheimer's, Parkinson's, and Huntington's disease are complex conditions that progressively impair neurological function. In recent years, machine learning (ML) techniques have shown considerable promise in the prediction, tracking, and understanding of these diseases, offering potential for earlier diagnosis and better patient outcomes. However, despite the advances, significant challenges remain in accurately predicting and classifying NDD due to their heterogeneous nature and the complexity of underlying biological processes. This survey aims to explore the current developments in the prediction and classification of neurodegenerative diseases using ML. The primary objective is to analyze various methods and techniques employed in the early diagnosis of NDD, focusing on ML algorithms, neuroimaging techniques, and biomarker analysis. The survey systematically reviews and categorizes existing studies, highlighting their methodologies, strengths, and limitations. Through an extensive literature review, the survey identifies key challenges such as the need for large, high-quality datasets, the integration of multi-modal data, and the interpretability of ML models. Findings suggest that while ML holds significant potential for advancing NDD research, addressing these challenges is crucial for its successful application. The survey concludes with a discussion on future research directions, emphasizing the importance of interdisciplinary approaches and the development of robust, transparent, and generalizable ML models for the early detection and diagnosis of neurodegenerative diseases
Optimization of a hybrid forward chaining and certainty factor model for malaria diagnosis based on clinical and laboratory data
Get PDFMalaria remains a serious public health problem in Indonesia, particularly in Papua Province, which accounts for 89% of national malaria cases. The similarity of malaria symptoms with other infectious diseases and limited laboratory facilities often lead to delays and inaccuracies in diagnosis. The study proposes an optimized hybrid model that combines forward chaining and certainty factor (CF) by integrating clinical and laboratory data to improve the accuracy of malaria diagnosis. The research design includes acquiring knowledge from medical experts, developing a rule-based system using forward chaining, and applying CFs to overcome uncertainty in symptom interpretation. The system is implemented using Python with support from libraries such as NumPy and PyKnow. The test results showed that the integration of laboratory data significantly improved diagnostic performance, with accuracy increasing from 81% malaria-positive using clinical data alone to 98% malaria-positive after combining with laboratory data. Expert testing to validate the accuracy of clinical and laboratory data results compared to expert validation results in an accuracy score of 98%. These findings show that the optimization of the hybrid forward chaining model and CF for malaria diagnosis based on clinical and laboratory data as a recommendation tool for early diagnosis of malaria in endemic areas
Hybrid energy storage system for fast and efficient electric vehicle charging
Get PDFThe rapid adoption of electric vehicles (EVs) necessitates efficient and fast charging solutions to meet growing energy demands. This study introduces a hybrid energy storage system (HESS) designed to enhance EV charging performance. By integrating batteries and supercapacitors, the HESS leverages their complementary characteristics, optimizing energy storage and delivery. The primary problem addressed is the inefficiency and prolonged charging times of conventional EV charging infrastructure. A dynamic control strategy manages power flow between batteries and supercapacitors, significantly reducing charging times and improving system efficiency. This approach reduces battery size and optimizes power quality, utilizing a device with three 18650 lithium-ion batteries and four high-capacity supercapacitors. Simulations using MATLAB/Simulink and Proteus software demonstrate a charging time of 57 minutes for the storage system and 4.74 hours for a full EV battery charge, outperforming traditional methods. This project contributes to the design and implementation of a HESS for EVs, facilitating both efficient and fast charging capabilities
Convolutional neural network DenseNet in classifying dyslexic handwriting images
Get PDFDyslexia 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