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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 19 Documents
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Search results for , issue "Vol 13, No 1: March 2025" : 19 Documents clear
Detection and Estimation of Schizophrenia Severity from Acoustic Features with Inclusion of K-means as Voice Activity Detection Function Alimi, Sheriff; Kuyoro, Afolashade Oluwakemi; Eze, Monday Okpoto; Akande, Oyebola
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5506

Abstract

Schizophrenia symptom severity estimation provides quantitative information that is useful at both the detection and treatment stages of the mental disorder, as the information helps in decision-making and improves the management of the illness. Very limited studies have been recorded for estimating the symptom severity as a regression task with machine learning, especially from speech recordings, which is the aim of this study coupled with detection. Acoustic features, which comprise frequency-domain and time-domain features, were extracted from 60 schizophrenia subjects and 59 healthy controls enrolled in this research. The acoustic features were used to train GridSearchCV-optimized XGBoost as a classifier. Three Multi-Layer Perceptron (MLP) networks, hyper-parameter-tuned by Bayesian Optimizer, were trained to predict the sub-type symptom severity from acoustic extracted features from the schizophrenia groups. The XGBoost classification model that discriminates between schizophrenia and healthy groups achieved a classification accuracy of 98.6%. The three MLP regression models yielded Mean Absolute Errors of 1.975, 2.856, and 1.555, as well as correlation coefficients of 0.888, 0.806, and 0.786 for predicting positive, negative, and cognitive symptom scores, respectively. Solution architecture for the deployment of the models for practical use was suggested
EfficientNet Model for Multiclass Classification of The Correctness of Wearing Face Mask Khadijah, Khadijah; Kusumaningrum, Retno; Rismiyati, Rismiyati; Sabilly, Nur
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5197

Abstract

A face mask is essential for protecting individuals from the entry of infectious or hazardous materials through the nose or mouth in specific situations. To optimize its protective function, it must be worn correctly. This research aims to develop a multiclass classification model, rather than a binary one, to assess the correctness of wearing face mask. The proposed model is designed to achieve high accuracy while maintaining efficiency, with a low number of model parameters. To this end, a deep convolutional neural network (CNN), specifically EfficientNet, is utilized. Experiments are conducted on the public MaskedFace-Net image dataset, which consists of four categories (correctly masked, uncovered chin, uncovered nose, and uncovered nose and mouth), using 3,000 randomly selected images from each category. The experiments test several EfficientNet models (B0-B3) and network hyperparameters (learning rate and dropout). The best accuracy of 0.99 is achieved by EfficientNet-B0 with a learning rate of 0.01 and a dropout rate of 0.2. The EfficientNet-B0 model outperforms other benchmark CNN models, including MobileNet-V3 and Inception-V3, despite having a slightly higher number of parameters than MobileNet-V3. This result demonstrates that the EfficientNet model is both accurate and efficient for multiclass classification of the correctness of wearing face mask.
IVFD: An Intelligent Video Forgery Detection Framework Leveraging InceptionV3 and GRU for Enhanced Forensics Bhargavi, Kumbham; Pasha, M Jahir; Kotoju, Rajitha; Vani, M. Sree
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.6030

Abstract

Cloud computing-like services that are great at paying for and managing multimedia are fundamental technological innovations that have made it easier for individuals and organizations to adopt multimedia content. Thanks to social media, different people with different perspectives can voice their opinions and present data through photos and videos. However, video tampering is a significant issue because illegal modification of video content can easily mislead audiences and make it difficult for them to relate to reality. This is, therefore, a serious problem, as the consequences of video forgery are dire. Several image processing-based solutions have emerged to address video forgery. Artificial intelligence has recently allowed deep learning models to be trained extensively; hence, deep learning has been frequently used for video tampering detection. However, further work is still required to refine such models or develop hybrid models to improve the existing models' capabilities in identifying video forgeries and assisting digital forensics. We introduce a framework based on deep learning to automate the detection and localization of video forgeries. We offer a hybrid deep learning model that fuses Inception V3 with a Gated Recurrent Unit (GRU) as part of our framework. We also propose a new algorithm, Intelligent Video Forgery Detection (IVFD), to detect the forgeries and their invariants based on this hybrid model. Through empirical studies applied on a standard dataset, called the Deepfake Challenge dataset, we get an accuracy of 97.21%, which makes our hybrid deep learning model outperform many existing models. Since video content is prevalent in almost all applications in today's era, our design system should be laid on top of these applications, which can facilitate detecting the tampering of the videos and thereby contribute towards digital forensics.
Transfer Learning for Detecting Alzheimer’s Disease in Brain Using Magnetic Resonance Images Islam, Md. Monirul; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5069

Abstract

Alzheimer’s Disease (AD) is one of the most concerning diseases because the patients show very few symptoms at the earlier stages. Dementia is very common in patients who have suffered brain damage or those who have suffered from psychotic trauma. Patients who have a lot of age suffer the most from it. Magnetic resonance imaging (MRI) is widely used to clinically treat patients with Alzheimer’s. Currently, there is no known remedy for the disease. We can only identify and try to give the proper medications to give some relief to patients. In this study, we have collected MRI data from patients with 4 different stages of Alzheimer’s. The purpose of this paper is to build a model to securely detect these stages for the betterment of medical science. We implemented a transfer learning method with state-of-the-art models such as ResNet50, DenseNet121, and VGG19. We proposed our method with these models which have pre-trained weights of “ImageNet”. The layers that we added are our novelty. We were able to achieve 97.70% accuracy on our best pre-trained model with an F1 score of 97% and a precision of 97% on our test data.
Improving Channel Gain of 6G Communications Systems Supported by Intelligent Reflective Surface Alsahlanee, Abbas Thajeel Rhaif; Al-Safi, Jehan Kadhim Shareef
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.6169

Abstract

The 6G wireless communication networks may use intelligent reflecting surfaces (IRS). It can enhance energy efficiency (EE). The IRS can enhance wireless communication by selectively reflecting incident signals in favorable directions. A potential method to improve the efficacy of wireless channels is to use a software-controlled metasurface that reflects signals when the direct transmission line from the source to the destination is insufficient. The IRS may redesign the environment to facilitate radio signal transmission. The decrease in channel gain in 6G communications networks using multiple reflective elements of the IRS is one of the challenges. This study seeks to propose a solution to enhance the channel gain and performance of the IRS in 6G communication systems. The research aimed to improve channel gain in assisted-IRS 6G communication systems by artificial intelligence algorithm (DS-PSO: dynamic and static particle swarm optimization). This study's technique enhances the effectiveness of aided-IRS communication methods. The simulation results of the optimized IRS model proposed in this paper show a significant improvement in channel gain compared to the results of previous studies.
Simulation-Based Evaluation of Dense Convolutional Neural Networks for Skin Cancer Detection Behara, Kavita; Bhero, Ernest; Agee, John Terhile
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5825

Abstract

Skin cancer, particularly melanoma, poses significant challenges to public health, with early detection being critical for effective treatment. Traditional diagnostic methods often fall short, particularly in resource-limited settings. In response, artificial intelligence (AI) techniques, especially deep learning models, have emerged as promising tools for automated skin cancer detection. This study evaluates the performance of Dense Convolutional Neural Networks (DCNNs) in classifying and detecting skin lesions, leveraging simulation-based approaches to assess the effectiveness of various AI models. Utilizing datasets such as HAM10000 and ISIC2017, which contain a wide variety of skin types and lesion stages, the models were trained and tested using key performance metrics such as accuracy, precision, recall, and F1-score. The results shows that DCNNs outperformed traditional machine learning techniques like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees (DT), demonstrating superior accuracy, generalization ability, and efficiency in handling large, imbalanced datasets. The simulation-based approach provided insights into the ability of DCNN models to manage dataset inconsistencies and class imbalances, showcasing their potential as robust tools for skin cancer detection. These findings highlight the ability of AI in advancing dermatological diagnostics, offering more timely and accurate detection, and potentially improving patient outcomes
Enhanced Field-Oriented Control for Synchronous Reluctance Motor Using Fuzzy Logic Madbouly, Sayed O.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.6070

Abstract

This paper presents a fuzzy logic-based Field Oriented Control (FOC) strategy for synchronous reluctance motors (SynRMs). The proposed algorithm addresses the inherent nonlinearities and parameter sensitivities of SynRMs by integrating fuzzy logic control (FLC) into the FOC framework, enhancing system robustness and adaptability. The SynRM model is derived in the rotor reference frame, with two control loops implemented: one for speed control and the other for flux control. Two FLCs are utilized in the speed control loop, while one FLC is adopted in the flux control loop. Fuzzy sets, membership functions, and rule bases enable dynamic parameter tuning. The entire system is simulated in MATLAB/Simulink. The system's dynamic performance is rigorously evaluated in two scenarios: with decoupling control components between the speed and flux control loops, and without these components under various loading conditions. Comprehensive simulations demonstrate that the proposed control algorithm, without decoupling control components, exhibits superior dynamic performance in terms of rise time, overshoot, and settling time. Furthermore, eliminating the decoupling components reduces the system's dependency on machine parameters while having a minor effect on undershoot.
Developing a Prototype for Enhancing Data Security in LoRaBased Theft Detection Systems Using ASCON-128 Encryption Amelia, Fetty; Wulandari Hartejo, Bella
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.6021

Abstract

Asset protection is crucial for organizations to prevent theft. This study presents a LoRa-based theft detection prototype enhanced with ASCON-128 encryption for secure data transmission. The system consists of a transmitter attached to assets and a receiver in a monitoring room, featuring a web-based digital map for real-time tracking. ASCON-128, a NIST-standard lightweight encryption algorithm, ensures data confidentiality and integrity against ManIn-The-Middle (MITM) attacks. The system was evaluated based on transmission speed, power consumption, and security performance. Results indicate that ASCON-128 integration reduces data transmission speed by 42.7% in Line-of-Sight (LOS) and 45.35% in Non-Line-of-Sight (NLOS) conditions. Power consumption increased by 2.7% in standby mode and 12.85% under simulated attack scenarios. Despite these trade-offs, encryption provides significant security benefits with acceptable resource overhead, making it a viable solution for LoRa-based asset tracking and theft detection.
Enhancing LEACH Protocol with Multi-Criteria Decision Making for Prolonged Network Lifetime in WSNs Altaha, Mohammed A.; Al Ali, Ghazwan Abdulnabi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.6054

Abstract

Wireless Sensor Networks (WSNs) have become a crucial solution for monitoring across diverse environments and consist of tiny sensor nodes that autonomously gather data on the environment. Energy depletion is a looming challenge, as sensor nodes rely heavily on their batteries, and once exhausted, the entire network can collapse prematurely. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is a cornerstone in energy-efficient routing protocols for WSNs. However, the Cluster Head (CH) selection process in the traditional LEACH protocol relies on a probabilistic model for CH selection, where each sensor has an equal chance of becoming a CH based on a fixed threshold. To address these issues, this paper proposes an enhanced version of the LEACH protocol by employing a Multi-Criteria Decision-Making (LEACHMCDM) process for CH selection. Instead of relying on random probabilities, the proposed protocol incorporates three key factors: Residual Energy (RE), Distance to the Base Station (DBS), and Node Degree (ND). Nodes with higher RE, shorter DBS, and an optimal ND are more likely to be selected as CHs. Compared to the traditional LEACH, the proposed method significantly improves the network’s lifetime by evenly distributing energy consumption and reducing the risk of premature node failure. Simulation results demonstrate the enhanced protocol’s ability to sustain more operational rounds and achieve higher energy efficiency.
Optimizing Data Survivability in Unattended Wireless Sensor Networks: A Machine Learning Approach to Cluster Head Selection and Hybrid Homomorphic Encryption Sivaraman, Haritha K; L, Rangaiah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v13i1.5998

Abstract

The research relies on machine learning-based Cluster Head (CH) selection and optimised Attribute-Based Encryption (ABE) with Homomorphic Encryption to improve data survivability in Unattended Wireless Sensor Networks (UWSNs). Integrating blockchain technology would enable tamper-proof data storage and provenance. The suggested method uses machine learning techniques like Deep Q-Networks (DQNs) or other models for intelligent and adaptive CH selection in UWSNs. Dynamically selecting CHs takes into account energy efficiency, network coverage, communication dependability, and node characteristics. The second part protects data using optimised Attribute-Based Encryption (ABE) and Homomorphic Encryption. ABE offers fine-grained attribute-based access control to restrict data access to authorised entities. Secure processing of encrypted data using homomorphic encryption protects privacy and integrity. These encryption algorithms are optimised to balance security and computational performance for efficient data processing and transmission while guaranteeing data privacy and integrity. Blockchain technology is suggested for tamper-proof data storage and provenance. To optimise the suggested solution's performance, the study uses the Seagull Optimisation Algorithm (SOA) and the Whale Optimisation Algorithm (WOA). These algorithms fine-tune system parameters, optimise CH selection, and boost UWSN performance. This holistic strategy uses machine learning-based CH selection, optimised ABE with Homomorphic Encryption, and blockchain technology for tamperproof data storage and provenance to improve UWSN data survival. Optimisation algorithms boost the solution's efficacy and efficiency, protecting UWSN data, latency, and energy usage.

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