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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 40 Documents
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Search results for , issue "Vol 41, No 1: January 2026" : 40 Documents clear
Towards adapting the consensus proof of authentication algorithm for IoT Aghroud, Mohamed; El Gountery, Yassin; Oualla, Mohamed; El Bermi, Lahcen
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.pp439-452

Abstract

The 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.
Survey on prediction, classification and tracking of neurodegenerative diseases Dhavalgi, Veena; Ranganatha, H R
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.pp367-374

Abstract

Neurodegenerative 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.
Remaining useful life estimation of turbofan engine: a sliding time window approach using deep learning Alqushaibi, Alawi; Hasan, Mohd Hilmi; Abdulkadir, Said Jadid; Taib, Shakirah Mohd; Al-Selwi, Safwan Mahmood; Sumiea, Ebrahim Hamid; Ragab, Mohammed Gamal
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.pp283-299

Abstract

System degradation is a common and unavoidable process that frequently oc curs in aerospace sector. Thus, prognostics is employed to avoid unforeseen breakdowns in intricate industrial systems. In prognostics, the system health status, and its remaining useful life (RUL) are evaluated using numerous sen sors. Numerous researchers have utilized deep-learning techniques to estimate RUL based on sensor data. Most of the studies proposed solving this problem with a single deep neural network (DNN) model. This paper developed a novel turbofan engine RUL predictor based on several DNN models. The method includes a time window technique for sample preparation, enhancing DNN’s ability to extract features and learn the pattern of turbofan engine degradation. Furthermore, the effectiveness of the proposed approach was confirmed using well-known model evaluation metrics. The experimental results demonstrated that among four different DNNs, the long short-term memory (LSTM)-based predictor achieved the better scores on an independent testing dataset with a root mean-square error of 15.30, mean absolute error score of 2.03, and R-squared score of 0.4354, which outperformed the previously reported results of turbofan RULestimation methods.
Cyber hygiene awareness among Malaysian youth Fikry, Amily; Abdul, Azreen Joanna; Kamaruzaman, Khairul Nazlin; Asnawati, Asnawati
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.pp210-219

Abstract

The study examined cyber hygiene awareness among Malaysian youth by analyzing the roles played by individual knowledge, awareness, attitudes, gender differences, and educational level. An online survey was conducted with 414 respondents in Peninsular Malaysia. The results showed no significant differences in cyber hygiene awareness based on gender and educational level. This suggests equal access to cybersecurity information and training across genders and education levels in Malaysia. This study also found significant relationships between individual characteristics (knowledge, rationality, and attitude) and cyber hygiene awareness. These findings indicate that individuals who are more knowledgeable, have positive attitudes, and make rational decisions tend to have higher cyber hygiene awareness. The results highlight the importance of fostering rationality and consistency in approaches to cybersecurity practices. The study contributes to the thoughtfully reflective decision-making (TRDM) theory, providing insights for developing targeted cybersecurity training programs and policies. Future research could explore additional factors influencing cyber hygiene awareness and examine how these findings translate to actual cybersecurity behaviors in professional settings.
Optimization of a hybrid forward chaining and certainty factor model for malaria diagnosis based on clinical and laboratory data Hasan, Patmawati; Kiswanto, Rahmat H.; Lestari, Susi
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.pp419-429

Abstract

Malaria 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.
Deep-fuzzy personalisation framework for robot-assisted learning for children with autism Gyening, Rose-Mary Owusuaa Mensah; Hayfron-Acquah, James Ben; Asante, Michael; Takyi, Kate; Appiahene, Peter
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.pp320-330

Abstract

Research exploring the efficacy of robots in autism therapy has predominantly relied on the Wizard-of-Oz method, where robots execute predetermined behaviours. However, this approach is constrained by its heavy reliance on human intervention. To address this limitation, we introduce a novel deep-fuzzy personalization framework for social robots to enhance adaptability in interactions with autistic children. This framework incorporates a deep learning model called singleshot emotion detector (SED) with a mean average precision of 93% and a fuzzy-based engagement prediction engine, utilizing factors such as scores, IQ levels, and task complexity to estimate the engagement of autistic children during robot interactions. Implemented on the humanoid robot RoCA, our study assesses the impact of this personalization approach on learning outcomes in interactions with Ghanaian autistic children. Statistical analysis, specifically Mann Whitney tests (U=3.0, P=0.012), demonstrates the significant improvement in learning gains associated with RoCA's adoption of the deep fuzzy approach.
Enhancing cybersecurity in 5G networks systems through optical wireless communications Alodat, Iyas Abdullah; Al-Khateeb, Shadi
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.pp250-257

Abstract

In this paper we will discuss with the recent global deployment of 5G networks, it has become imperative to ensure secure and reliable communications in addi tion to basic responsibility. Given that standard radio frequency (RF) communi cations have security flaws such as eavesdropping, signal jamming, and cyber attacks, wireless optical communications (WOC) offers a viable alternative. Us ing technologies such as visible light communications (VLC) and the free space optics (FSO) technologies, 5G networks can enhance the speed and efficiency of data transmission, while simultaneously enhancing cyber security. In addition to discussing the advantages of wireless on-chip communication technology com pared to RF solutions and the challenges that need to be addressed, this paper examines how WOC technology can enhance cyber security in 5G networks.
Predictive control strategy for a novel 15-level inverter with reduced power components El Ansari, Taoufiq; El Gadari, Ayoub; Ounejjar, Youssef
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.pp33-44

Abstract

This paper proposes a novel fifteen-level H-PTC inverter topology controlled by model predictive control (MPC), which reduces the number of components. The design employs only two DC sources, nine switches, including one bidirectional switch, and a single capacitor. The system’s performance is validated through MATLAB/Simulink simulations under various scenarios, such as steady-state operation, load variations, nonlinear loads, and sudden supply voltage disturbances. Compared to existing topologies, the proposed inverter demonstrates hardware simplicity, high output quality, and enhanced dynamic robustness. Notably, it features very low total standing voltage (TSV) and a minimized cost function value of 2.05. For a load characterized by R = 20 Ω and L = 20 mH, the total harmonic distortion (THD) of the load current is 0.88%, confirming excellent power quality without the need for output filters. The MPC controller ensures a fast dynamic response and strong adaptability, making this topology ideal for modern energy conversion applications.
Incipient anomalous detection in a brain using the IBIGP algorithm Nait Chalal, Mohamed Hichem; Yagoubi, Benabdellah; Henni, Sidahmed
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.pp119-127

Abstract

The detection of an incipient anomalous growth of tissue in a brain is often a difficult task. Various algorithms for brain anomalous detection have been suggested abundantly in the existing literature. In the last decade, many detection methods have been suggested to improve and facilitate abnormal tissue detection. However, the most attractive techniques to many researchers are maybe those that are magnetic resonance imagery (MRI)- based algorithms. A technique known as the inverse of the belonging individual Gaussian probability (IBIGP) is applied to MRI in this work in order to mitigate incipient anomalous tissue detection in a brain. This study demonstrates that the IBIGP technique, applied to the MRI image, is extremely effective in early detecting an anomalous change in the brain MRI image. Although this technique is still in its infancy, it has a great potential to enhance brain anomalous early detection.
Development of a machine learning model with optuna and ensemble learning to improve performance on multiple datasets Efendi, Akmar; Fitri, Iskandar; Nurcahyo, Gunadi Widi
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.pp375-386

Abstract

Machine learning, a subset of artificial intelligence (AI) is vital for its ability to learn from data and improve system performance. In Indonesia, advancements in ML have significant potential to boost competitiveness and foster sustainable development. However, issues like overfitting and suboptimal parameter settings can hinder model effectiveness. This study aims to improve the classification performance of ML models on various datasets. Advanced techniques like hyperparameter tuning with Optuna and ensemble learning with extreme gradient boosting (XGBoost) are integrated to enhance model performance. The study evaluates the performance of K nearest neighbors (KNN), support vector machine (SVM), and Gaussian naïve Bayes (GNB) algorithms across three datasets: academic records from the Islamic University of Riau (UIR), diabetes data from Kaggle, and Twitter data related to the 2024 elections. The findings reveal that the GNB algorithm outperforms KNN and SVM across all datasets, achieving the highest accuracy, precision, recall, and F1-score. Hyperparameter tuning with Optuna significantly improves model performance, demonstrating the value of systematic optimization. This study highlights the importance of advanced optimization techniques in developing high-performing ML models. The results suggest that robust algorithms like GNB, combined with hyperparameter tuning and ensemble learning, can significantly enhance classification performance.

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