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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 66 Documents
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Search results for , issue "Vol 34, No 2: May 2024" : 66 Documents clear
Design and implementation of duty cycle-based futuristic clustering technique in WSN Trupti Shripad Tagare; Rajashree Narendra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp951-959

Abstract

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.
Potential microgrid model based on hybrid photovoltaic/wind turbine/generator in the coastal area of North Sumatra Habib Satria; Rahmad Syah; Dadan Ramdan; Muhammad Khahfi Zuhanda; Jaka Windarta; Syafii Syafii; Almoataz Youssef Abdelaziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp768-776

Abstract

The high potential for renewable energy in the North Sumatra region, especially the coast of the Belawan area, needs to be exploited properly. The design that will be carried out is to explore the potential in coastal areas by simulating microgrid systems and hybrid system-based electricity installations. The method that will be used is to find the accuracy of strategic location points by considering the panel surface temperature which will later influence the power output of the power plant. Then find the ideal installation location as a reliable system when irregular climate conditions occur, of course this phenomenon will have a significant effect on energy balance and energy conversion, especially in coastal areas. The potential for installation construction will be carried out with a hybrid system using power sources from photovoltaics, wind turbines and diesel generators assisted by HOMER Pro software. The results of testing with simulations and information data that have been recorded in the software can later be used as a benchmark in planning electrical installations and also for identifying microgrid protection challenges. Then the measurement results that have been obtained for the installation of a hybrid-based microgrid system on Photovoltaic (PV) are DC output power of 618.80 W with measurements of sunny weather conditions, then the potential wind speed on the wind turbine reaches 5 m/s and the potential use of a diesel generator reaches 40% with power output capacity 1 kW.
Empowering health data protection: machine learning-enabled diabetes classification in a secure cloud-based IoT framework Dalia Ebrahim Hamid; Hanan M. Amer; Hossam El-Din Salah Moustafa; Hanaa Salem Marie
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1110-1121

Abstract

Smart medical devices and the internet of things (IoT) have enhanced healthcare systems by allowing remote monitoring of patient's health. Because of the unexpected increase in the number of diabetes patients, it is critical to regularly evaluate patients' health conditions before any significant illness occurs. As a result of transmitting a large volume of sensitive medical data, dealing with IoT data security issues remains a difficult challenge. This paper presents a secure remote diabetes monitoring (SR-DM) model that uses hybrid encryption, combining the advanced encryption standard and elliptic curve cryptography (AES-ECC), to ensure the patients' sensitive data is protected in IoT platforms based on the cloud. The health statuses of patients are determined in this model by predicting critical situations using machine learning (ML) algorithms for analyzing medical data sensed by smart health IoT devices. The results reveal that the AES-ECC approach has a significant influence on cloud-based IoT systems and the random forest (RF) classification method outperforms with a high accuracy of 91.4%. As a consequence of the outcomes obtained, the proposed model effectively establishes a secure and efficient system for remote health monitoring.
An efficient test suit reduction methodology for regression testing Shailendra Gupta; Jitendra Choudhary
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1336-1343

Abstract

This paper's goal is to provide a more effective algorithm for reducing the amount of test cases in a test suit during the regression testing phase. This algorithm divide the entire test suit into equivalence classes in first step and then apply boundary value coverage to select test case out of repeated test cases which has same importance in test suit. This algorithm is based on the concept that before selecting best test cases out of repeated test case in test suit to prepare reduced test suit we can divide all test cases in number of equivalence classes so number of test case under consideration reduced by great extend. This paper proposed a method of experimentation involving test cases from different software application areas; minimization algorithms and the maths and algorithms of minimization algorithms in details. Test case techniques are equivalence portioning and boundary value analysis. Along with this concept, I also discuss a case study to verify and check new algorithm for its efficiency, for that I apply my algorithm on one of the program or group of program. This complete proposed methodology shall be applied to different software applications belonging to soft computing, Engineering software, Financial Software, Cloud Applications, Business Applications, AI and Machine Learning, Data Analytics are being identified. This selection has been made keeping in mind the trends, industrial importance, economic values and research challenges. Minimization in test cases would lead to lesser testing effort and desirable test completion.
Proactive ransomware prevention in pervasive IoMT via hybrid machine learning Usman Tariq; Bilal Tariq
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp970-982

Abstract

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem.
Fresnel lenses and auto tracking to increase solar panel output power Sindak Hutauruk; Libianko Sianturi; Irvan Togatorop
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1389-1398

Abstract

This solar panel, which is equipped with microcontroller-based auto tracking, is also equipped with a Fresnel lens to obtain more optimal power because the Fresnel lens has the property of bending or refracting light rays that pass through it so that it can focus sunlight. The Fresnel lens focuses sunlight radiation onto the solar panels. To determine the magnitude of the influence of Fresnel lenses on increasing the power produced by solar panels, two solar panels of the same size (11×11) cm were moved by auto tracking using the same axis. One solar panel is not fitted with a Fresnel lens, and the other is fitted with a Fresnel lens measuring 30×30 cm, where the distance between the Fresnel lens and the solar panel is 5 cm. Measurements of the output power of both solar panels were carried out simultaneously, namely from 08.00 to 18.00 WIB (West Indonesia time). The power output of both solar panels was measured in 30-minute intervals, resulting in 21 measurements. Solar panels using Fresnel lenses produce an output power that is 105.306% more significant than that of those without Fresnel lenses.
Effective virtual laboratory to build constructivist thinking in electrical measurement practicum Fivia Eliza; Oriza Candra; Doni Tri Putra Yanto; Radinal Fadli; Dwiprima Elvanny Myori; Syaiful Islami; Yayuk Hidayah; Levandra Balti
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp814-824

Abstract

Constructivism serves as a basic theory in education, and plays an important role, because this perspective views learning as a dynamic process in which students actively participate in constructing their knowledge. So, this research aims to produce a virtual laboratory that is valid and effective in developing constructivist thinking. This research and development use the Ploom approach which consists of initial investigation, design, construction/realization, testing, evaluation, revision, and implementation. Thirty-two electrical engineering students from Padang State University participated in this research. The instruments used are validity instruments and constructivism assessment tools. The research results show that the Proteus-based virtual laboratory developed is valid and effective in fostering constructivist thinking in electrical measurement practice. This study contributes to the advancement of virtual laboratories in educational settings, emphasizing the importance of constructivist pedagogy for meaningful and engaging learning experiences in the field of electrical engineering. The results of this research open opportunities for further research regarding the exploration of artificial intelligence to improve constructivism through virtual laboratories.
A critical evaluation of DC microgrid implementation in Indonesia: opportunities and challenges Levin Halim; Pinto Anugrah; Aditya Kurniawan; Khairuddin Karim
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp687-696

Abstract

This study thoroughly investigates the potential of direct current (DC) microgrids to enhance electricity access in rural and remote areas of Indonesia that continue to face significant obstacles despite ongoing national electrification efforts. Utilizing a mixed-methods approach, this research comprehensively evaluates socio-economic and technical factors that influence the adoption of DC microgrids. The results indicate that DC microgrids offer significant potential for enhancing energy access, reliability, and sustainability, particularly when combined with renewable energy sources. This aligns with Indonesia’s move towards renewable energy. Nevertheless, the analysis identifies significant obstacles, such as the substantial initial investment, the requirement for complete regulatory frameworks, and the technological complexities that need to be conquered. In conclusion, DC microgrids present a promising solution for rural electrification. However, the implementation requires a strategy that emphasizes strategic investments, policy innovation, and capacity-building initiatives. This research significantly contributes to the study of sustainable energy by evaluating the criticality of integrating policies and technology for implementing DC microgrids as a key factor in achieving sustainable energy access in Indonesia.
Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion Areen Arabiat; Muneera Altayeb
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1295-1303

Abstract

Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.
Rhinitis phototherapy prototype with timer based on light energy Erika Loniza; Mita Junita; Yessi Jusman; Siti Nurul Aqmariah Mohd Kanafiah; Kurnia Chairunnisa
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp861-869

Abstract

The set of timers in using phototherapy is major problem which has to be resolved to get a good performance of rhinitis phototherapy. This research aims to develop a prototype of phototherapy for allergic rhinitis, incorporating a timer based on light energy. The prototype utilizes a laser diode as a visible light source, specifically with a wavelength of 650 nm. The recommended safe and effective dose of light energy ranges from 1 to 10 Joules, which has been converted into minutes. Measurement tests indicate an average wavelength of 652.40 nm for the right laser, with a measurement uncertainty of ±0.11, and 653.23 nm for the left laser, with a measurement uncertainty of ±0.05. The laser diode source has an average voltage of 1.91 volts and an average current of 1.89 milliamperes, with a measurement uncertainty of ±0.00 and ±0.01, respectively. Additionally, the average discrepancy in the timer is 0.082 minutes for the 10-minute setting and 0.082 minutes for the 20-minute setting. These results confirm the effectiveness and suitability of the developed tool for practical use. The proposed method was useful for rhinitis therapy by using light energy.

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