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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 25 Documents
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Search results for , issue "Vol 12, No 4: December 2024" : 25 Documents clear
Simple and Efficient Key Management Method for Hierarchical Wireless Sensor Networks Altaha, Mohammed A.; Lafta, Wisam Mahmood; Al Ali, Ghazwan Abdulnabi; Alkadhmawee, Ahmed Adil
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5782

Abstract

Security is an important consideration for Wireless Sensor Networks (WSNs), and key management plays a pivotal role in facilitating safe communication and data transfer. Key management must be designed with the constraints of these networks in mind, which include limited computation capabilities, memory, and energy. Achieving secure and efficient communication in large-scale WSNs is a significant challenge. In this paper, we propose a simple key management method for securing hierarchical WSNs, which employs only a few hash functions and XOR operations to derive shared keys. Its simplicity makes optimal use of resources and offers an efficient approach to establishing keys for sensor nodes. Simulation results demonstrate that the proposed scheme reduces energy consumption by 15% and decreases the key establishment time by 20% compared to existing methods such as LKMS, while maintaining strong security with low computational and communication costs, which are crucial considerations for WSNs.
Enhancing Confidence In Brain Tumor Classification Models With Grad-CAM And Grad-CAM++ Vo, Hoang-Tu Vo; Thien, Nhon Nguyen; Mui, Kheo Chau; Tien, Phuc Pham
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5977

Abstract

Brain tumors are a terrible and dangerous health problem, often posing a significant threat to individuals due to their high probability of death. Detecting these tumors at an early stage is crucial, as it not only increases the chances of successful treatment but also plays a pivotal role in reducing total healthcare costs. Early detection allows medical professionals to take action quickly, enabling a more targeted and effective treatment approach. Numerous studies are currently employing Machine Learning (ML) and Deep Learning (DL) to classify brain tumors, promising improved accuracy and efficiency in tumor identification for potential breakthroughs in medical diagnosis. However, a significant challenge lies in these models being "black box" as their complex inner workings are not easily understood by humans. Explainable Artificial Intelligence (XAI) refers to the capability of an artificial intelligence (AI) system to provide understandable and interpretable explanations for its decisions or predictions. In this study, we propose a classification model based on various network architectures, namely DenseNet201, DenseNet169, Xception, MobileNetV2 and ResNet50. We then used Grad-CAM and Grad-CAM++ to interpret the model's results, evaluating its ability to distinguish important features in Magnetic resonance imaging (MRI) images of brain tumors during the decision-making process. The integration of Grad-CAM and Grad-CAM++ enhances the interpretability of the brain tumor classification model, providing valuable evidence of its effectiveness by focusing on crucial features in MRI images of brain tumors during decision-making. Research results contribute to the development of systems that support early diagnosis of tumors. This contribution is pivotal as it not only enhances the model's transparency but also validates its effectiveness in accurately identifying brain tumors.
A Diet Recommendation System using TF-IDF and Extra Trees Algorithm Boudaa, Boudjemaa; Hammadi, Aissa; Akermi, Kada
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5809

Abstract

Across the globe, there is a growing emphasis on health and lifestyle choices. However, refraining from unhealthy foods and staying active are not enough; maintaining a well-balanced diet is also essential. Recently, recommendation systems have focused on promoting healthy eating habits, tailoring suggestions for balanced diets based on some parameters like age, gender, height, weight, age, BMI and BMR. Pairing a nutritious diet with regular physical activity can aid in reaching and sustaining a healthy weight, reducing the likelihood of chronic ailments such as heart disease, and enhancing overall well-being. The present paper introduces a novel approach for constructing dietary recommendations with optimized calorie intake, using content-based filtering with the TF-IDF statistical method and machine learning with the Extra Trees algorithm. This approach can generate a dynamic diet based on the calories a person burns and other parameters including the current Body Mass Index (BMI) and BMR (Basal Metabolic Rate). The proposed approach has been tested on a new realworld diet dataset, showcasing its effectiveness in providing diverse and accurate diet recommendations compared to another content-based filtering method and other machine learning algorithms.
Collaborative Online International Learning to Address Mental Health Across Cultures with an Islamic Perspective Gunawan, Teddy Surya; Kartiwi, Mira; Lubis, Asmuliadi; Arifin, Muhamad
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.6041

Abstract

This study examines the impact of a Collaborative Online International Learning (COIL) project on enhancing students’ cross-cultural understanding, collaborative skills, and problem-solving abilities in addressing mental health issues through technology, enriched by insights from an Islamic perspective. The project connected students from the International Islamic University Malaysia’s (IIUM) Operating Systems course and Shenandoah University’s Occupational Therapy in Mental Health Practice course, fostering a dynamic, interdisciplinary learning environment. Students worked in diverse teams, engaging in activities such as video introductions, infographic creation, and presentations on technological applications in mental health, facilitated by platforms like Zoom, Google Sites, and WhatsApp. Evaluations, including Programme Outcome (PO) analyses, revealed that over 80% of students achieved “Acceptable” or higher levels in applying engineering knowledge (PO1) and problem analysis (PO2), reflecting the success of the project in meeting its learning objectives. Student reflections captured on Flipgrid further underscored the project’s impact, with participants highlighting improved cultural sensitivity, adaptability to a global professional context, and collaborative problem-solving despite challenges such as time zone differences. The inclusion of Islamic perspectives provided a holistic lens, emphasizing spiritual and technological solutions to mental health issues through values such as patience (sabr), gratitude (shukr), and trust in Allah (tawakkul). This study underscores COIL’s potential as a transformative pedagogical approach for preparing students to navigate multicultural, technology-driven environments while fostering global mental health awareness. It offers actionable insights for educators and policymakers seeking to integrate cultural and religious perspectives into interdisciplinary education
Parallel Pipelined Hardware Acceleration of Fast Fourier Transforms on FPGA Sleeba, Simi Zerine; B.R., Karthika; Sudhan, Krishna; Unni, Lakshmi Priya; John, Lin
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5902

Abstract

The Fast Fourier Transform (FFT) is widely used in digital signal processing ap-plications and particularly for implementing convolution operation for real-time object detection using CNN. This paper proposes an efficient hardware architecture for Radix-2 FFT computation, implemented on an FPGA, employing multiple parallel and pipelined stages of butterfly units. The proposed architecture utilises Block RAM to store inputs and twiddle factor values to compute the transform. The hardware for the proposed architecture is synthesised on a Zync Ultrascale FPGA and its performance is evaluated using parameters such as critical path delay, throughput, device utilisation and power consumption.The performance of the proposed parallel pipelined architecture for 8 point FFT, measured in FFTOPS, is found to be 67% higher than the non-pipelined architecture. Performance comparison with the state-of-the-art parallel pipelined methods confirm the acceleration achieved by the proposed FFT architecture. A comprehensive comparison of the proposed hardware with the synthesised version of the FFT IP core bundled with the Vivado Design suite is also presented in the paper.
Stabilizing Quadruped Robot Movement Using Fuzzy Logic Control for Yaw Angle Adjustment in Walking and Troting Gait Izzah, Sukma Nurul; Dharmawan, Andi; Auzan, Muhammad; Sumbodo, Bakhtiar Alldino Ardi; Istiyanto, Jazi Eko
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5837

Abstract

Balance is a fundamental aspect of quadruped robots that determines their movement success. Imbalanced movement can affect the robot's orientation, leading to potential deviations from the intended direction due to changes in the attitude angle. An unstable attitude angle can result in loss of control, complicating effective navigation. This loss of control may prevent the robot from maintaining its stability, increasing the risk of falling. This study designs a control system for a quadruped robot using fuzzy control system to manage the yaw angle while the robot walks forward using both walking and trotting gaits. The fuzzy control system outputs are used to adjust the hip joint angles of the robot's four legs, modifying the stride length of each leg accordingly. The quadruped robot was tested with both walking and trotting gaits moving forward for 30 seconds. The quadruped robot successfully maintained balance and stability in the ?-axis (yaw) on a flat, obstacle-free surface using fuzzy control system. The fuzzy logic control effectively reduced positional distance fluctuations from the set point and enhanced the robot's ability to return to the set point after fluctuations, without producing excessive overshoot.
Empirical Evaluation of Energy-assisted Protocols for Wireless Sensor Networks Qureshi, Kalim Uddin; Almutairi, Hanan S R
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5814

Abstract

Wireless sensor networks (WSNs) have emerged as a transformative technology with widespread applications in various fields, such as environmental monitoring, healthcare, and industrial automation. This investigation provides a comprehensive evaluation and comparison of an existing protocol, the Energy-Efficient Backbone-assisted protocol for Load Balancing (EBLBP), against two established protocols: Ad-hoc On-Demand Distance Vector (AODV) and Destination Sequenced Distance Vector (DSDV). Through extensive simulations, we analyzed the performance of these protocols across four critical metrics: scalability, efficiency, network lifetime, and energy consumption. Our findings reveal the inherent strengths and weaknesses of EBLBP, AODV, and DSDV, offering insights into their suitability for various WSN deployment scales and conditions. In the simulation environment, EBLBP achieved an impressive 66.67% reduction in overall energy consumption of 100 to 600 node positions, which underlined its positive impact on energy efficiency. The NS2 simulator was used for this investigation. The measured results validate the advantages of EBLBP in terms of energy optimization.
Implementation of Image Processing and CNN for Roasted-Coffee Level Classification Anto, Irfan Asfy Fakhry; Wibowo, Jony Winaryo; Salim, Taufik Ibnu; Munandar, Aris
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5531

Abstract

The roasting process of coffee beans plays a crucial role in the development of chemicals responsible for the rich color and complex flavors characteristic of well-roasted coffee. One approach to understanding this process involves assessing the roast level, which varies in color from light to dark, with intermediate levels in between. In this study, image processing was performed using Convolutional Neural Networks (CNNs), a widely used method for image classification. The objective was to utilize the LAB color model and the CNN framework to classify the roast levels of coffee beans based on images from files or video streams. The study also details the hardware and software tools employed. A user-friendly graphical interface was developed to ensure ease of use, requiring minimal training for efficient operation. The research successfully designed, developed, and implemented an application for classifying coffee bean roast levels using two methods: LAB color model image processing and the CNN model. Consequently, the system can recognize roast levels based on the outputs from both the LAB model and the CNN model. This research represents a preliminary effort and requires further development to support more extensive studies. Ultimately, it serves as a foundation for future exploration and the application of embedded system-based solutions for assessing coffee bean maturity levels in alignment with Agtron classification standards.
Analyzing Success Factors in Developing Mobile Applications for Farmers in Thailand Using Analytic Hierarchy Process Technique Kunlerd, Attapol; Nabumroong, Boonlueo; Jantarakomet, Kraisak; Ruttanaprasert, Ruttanachira; Burana, Kunlachat
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5508

Abstract

This study applies the Analytic Hierarchy Process (AHP) as a robust decision-making framework to address critical gaps in risk management and criteria prioritization within mobile application development for the agricultural sector, specifically focusing on Thai rice farmers. The research identifies essential factors influencing technology adoption through input collected from 100 rice farmers in Surin Province. Using AHP, these factors were systematically ranked, with "ease of use," "provision of up-to-date information," and "support" emerging as the most significant criteria. Based on these insights, three mobile application prototypes were developed, with Mobile App 1 achieving the highest AHP score of 0.633, demonstrating superior alignment with user requirements. Subsequent evaluations of user satisfaction reinforced these findings, with "ease of use" scoring the highest (4.60), followed by "perceived usefulness" (4.10). The findings underscore AHP’s efficacy in mitigating risks and aligning application features with user demands, thereby enhancing adoption effectiveness. This study contributes novel insights into leveraging AHP as a precision tool for guiding mobile application development in agriculture and provides a replicable framework for addressing user-centric challenges. Future research should investigate integrating AHP with emerging technologies to drive innovation and sustainable solutions in agricultural practices.
The Use of Green-Phosphor LuAG:Ce-Al2O3 for HighLuminosity Light Emitting Diode Packages Nguyen Thi, My Hanh; Le Thai, Nguyen
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v12i4.4797

Abstract

The LuAG:Ce-Al2O3 (LAGCA) green phosphor ceramic (GPC) is proposed for high-power white light emitting diodes (LEDs) in this paper. The luminescent properties of the GPC are examined with proper characterizing tools and under laser excitation. Then, LAGCA ceramic layer of 0.6 thickness is applied to fabricate the white LED. The results show LAGCA GPC is promising for high-power LED applications. The phosphor ceramic presents high thermostability and quantum efficacy and intense green emission peaking at nearly 550 nm. In the LED package, the amount of LAGCA in the composite layer is varied. The increasing dosage of LAGCA gives enhancement to the lumen output of the LED. However, the correlated color temperature stability and chromatic rendition declines. Thus, further improvements in LAGCA ceramic need to be carried out in the future works. Besdies, with the intense green emission, the LAGCA ceramics can be combined with red luminescent materials to increase the color performance of the LED lighting.

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