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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 :
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Articles 65 Documents
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Search results for , issue "Vol 37, No 2: February 2025" : 65 Documents clear
Machine learning-based emotions recognition model using peripheral signals Kumar, Tarun; Kumar, Rajendra; Chandra Singh, Ram
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp976-984

Abstract

This work proposes a system for emotion recognition using four peripheral signals electromyography, galvanic skin response, blood volume pulse, and respiration. Peripheral signals cannot be modified, unlike other expression like voice and facial expression. The proposed method is applied to the DEAP datasets to verify the accuracy of emotion recognition. The proposed model focuses on accuracy and F1-score. DEAP dataset has more signals but only thirty-seven features from four peripheral signals were extracted for each trail and each video. On the DEAP datasets, the implementation found that the classification accuracy for arousal, valence, liking, and dominance was, respectively, 80%, 75%, 71%, and 78%. For two classes of problems, the corresponding F1-scores for arousal, valence, liking, and dominance are 0.50, 0.49, 0.47, and 0.47. The proposed model was implemented in MATLAB R2017a.
Challenges of implementing protection systems in smart grids: a review Anwari, Sabat; Fauziah, Dini; Lidyawati, Lita
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp715-729

Abstract

Based on the emergence of increasingly advanced technology, the conventional power grid can be upgraded to a smart grid by adding bidirectional communication, computer algorithms, and equipment that uses artificial intelligence (AI). A smart grid is a revolution in the current electricity network that can control the two-way generation and transmission process by utilizing an intelligent system so that the distribution of electric power can be handled optimally and in real time. The challenge of the smart grid is that there are distributed generators and microgrids that must be controlled in real time with rapidly changing loads. To meet these criteria, several points are proposed, i.e., finding an effective procedure to construct self-healing capability; developing a protection system based on AI; and proposing a systematic procedure to realize self-healing and protection systems with the help of a multi-agent system (MAS). Multi-agent systems are one of the AI approaches. Each agent can work independently and can also communicate with one another and with other devices on the network. Agents used as models can be classified into several categories, such as grid component agents, distributed resource agents, end-user agents, failure control agents, data analysis agents, and graphical visualization agents.
Novel five-patch compact microstrip Yagi-alike antenna for Ka-band applications Kumar Singh, Raj; Mamta, Kumari; Kumar Sinha, Navin; Kumar Choudhary, Vinay
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp878-887

Abstract

This paper discusses the process of designing and fabricating a novel compact microstrip patch Yagi-like antenna having five-patch radiating element at operating frequency 31 GHz with a bandwidth of 1 GHz. The developed design aims to optimize the antenna performance. The overall dimension of the antenna being 17× 14 × 0.8 mm3, based on RT Duroid 5880 substrate having dielectric loss tangent of 0.0009 and relative permittivity 2.2. The effectiveness of the performance of proposed design was evaluated using the electromagnetic solver Ansoft high-frequency structure simulator (HFSS) and validated by the laboratory measurements on the antenna prototype. The measured results are consistent with the simulation prediction. The designed antenna achieved directional radiation and the performances with voltage standing wave ratio (VSWR) < 1.32, return loss -17 dB and gain of 6 dBi. The measured results are compared with those existing in literature. The proposed antenna design has proven very effective in terms of the intended design and parameters which make it suitable for satellite application and wireless communication.
IT risks associated with information theft in the financial system - a systematic review Cabanillas-Allca, Frank; Chaquila-Muñoz, Sebastian; Iparraguirre-Villanueva, Orlando
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1339-1351

Abstract

This research paper systematically reviews the financial system’s computer security risks associated with information theft. The objective is to explore the security risks and their implications concerning information theft in the economic system. Three research questions were formulated to identify these risks, their nature, and potential consequences to achieve this objective. Fifty-five articles obtained from reliable databases linked to both study variables were analyzed using the PRISMA methodology. To ensure the validity and reliability of the information, various filters were applied, such as year, keywords, and elimination of duplicate articles. In addition, an exhaustive reading of the content of each article was carried out, organizing all the information through a systematization matrix. After a thorough review of the research articles, mostly written in English and representing 34.55% of the total in 2023, risks associated with the financial sector were identified, including malware, ransomware, phishing, distributed denial of service (DDoS), hybrid XSS, eavesdropping, and social engineering. Geographically, India leads with 14.55% of the articles, followed by South Korea and the United States, with 12.72% each, while the other countries have lower percentages. In conclusion, these risks coincide with previous research and the consequences they generate, highlighting the importance of this type of study for the basis of scientific research.
Natural smart home automation system using LSTM based on household behaviour Susantok, Mochamad; Ahmad Po’ad, Farhana; Joret, Ariffuddin; Hilwa Salsabillah, Maulina
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp758-770

Abstract

A smart home automation system (SHAS) utilizing data-driven learning is an advanced internet of things (IoT) application aimed to learn household behavior to prevent miniatur circuit breaker (MCB) trips due to overload. Unlike traditional deterministic methods, this study leverages a layered AI model, featuring real-time data collection, long short-term memory (LSTM) based learning, and an automatic control system. The LSTM classification model generates precise ON/OFF control signals sent to IoT smartplugs, optimizing appliance usage and reducing the risk of electrical overload. Data from smartplug sensors, including appliance status and environmental factors like power consumption, temperature, and humidity, were collected every minute over three months, yielding 80,818 data points. The system's performance was evaluated on three appliances: Air Conditioner, Television, and Water Pump Machine. Results showed high accuracy for Television at 98% and Water Pump Machine at 97.6%, with slightly lower accuracy for Air Conditioner at 81.9%. This demonstrates the system's effectiveness in real-world applications. The scalability and adaptability of the Natural SHAS model to different appliances and environments mark a significant advancement in smart home automation, offering a practical solution for preventing electrical overload and improving household energy management.
Analysis of LLC resonant converter performance with PIDD2 controller for electric vehicle application K., Sathya; Guruswamy, K. P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp749-757

Abstract

The key uses of the latest developments is electric vehicles (EV’s). As a result, several researchers were drawn to EV’s control to propose appropriate controllers and predicted that control engineers face a challenge when it comes to regulating the LLC resonant converter output voltage. In this regard, the study proposes a PID Type modified controller for regulation of voltage across output in LLC resonant converter. The design and control procedure of this modified proportional integral derivative double derivative (PIDD2) is explained along with EDF modeling in LLC resonant converter. This work proposes to use two controllers to drive the voltage output of a resonant converter LLC to constantly track the desired value. Proportional integral derivative controller (PID) is the first, while the PIDD2 method is the foundation of the second. Every controller has undergone simulation testing and the results are compared based on how the evaluated controllers respond dynamically in accordance with settling time, rising time and overshoot.
A novel secure and energy aware LOADng routing protocol for IoT: an application to smart agriculture Sana, Touhami; Mohamed, Belghachi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1005-1013

Abstract

In the burgeoning domain of the internet of things (IoT), efficient and secure communication protocols are crucial for the seamless operation of diverse applications. This paper proposes a novel routing protocol, termed secure and energy aware LOADng (SEA-LOADng), tailored for IoT deployments in the context of smart agriculture. The protocol is designed to address the unique challenges posed by agricultural environments, including limited energy resources and the need for robust security measures. The proposed protocol leverages LOADng, a lightweight and efficient routing protocol suitable for low-power and lossy networks characteristic of IoT deployments. Through innovative energy-aware mechanisms, it optimizes the power usage of IoT devices, thus prolonging their operational lifespan and reducing maintenance overhead. Moreover, stringent security measures are integrated into the protocol to safeguard sensitive data transmitted within the IoT network. To assess the efficacy of the proposed protocol, comprehensive simulations are carried out using realistic smart agriculture scenarios. The results demonstrate significant improvements in energy efficiency compared to LOADng protocol, while maintaining robust security against hello flood attack.
Educational impact and ethical considerations in using Chatbots in Academia Ibrahim, Dina M.; Al-harbi, Njood K.; Al-Shargabi, Amal A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1150-1167

Abstract

Chatbots are getting better every day due to the advancements in their capabilities in today’s technological age. This study aims to assess the efficacy of ChatGPT-4 and Gemini in producing scientific articles. Two types of prompts are given: direct questions and complete scenarios. Subsequently, we evaluate the educational and ethical aspects of the produced material by employing statistical analysis. We verify the credibility of references, detect any instances of plagiarism, and ensure the precision of the articles generated by the chatbot. In addition, we utilize topic modeling to assess the extent to which the content of the articles corresponds to the specified topic. According to the findings, Gemini outperformed ChatGPT-4, specifically in scenario questions, where it achieved an accuracy rate of 85%, while ChatGPT-4 only achieved 35% accuracy.
Detect and envision of pandemic disease exposure using CNN Rupa, Ch.; Rama Prasad, Kanakam Siva; Lakshmi Rajeswari, Aremanda; Sambasiva Rao, Elika; Sirajuddin, Mohammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp948-958

Abstract

COVID-19 has emerged as a pandemic, affecting millions globally with its high transmission rate, especially in colder climates. The virus's multiple mutations have made it progressively harder to detect and manage. Despite widespread awareness of preventive measures such as masks and sanitizers, early detection remains critical. Traditional methods like blood tests are time-consuming, and existing studies utilizing fuzzy K-means clustering, principal component analysis (PCA), stochastic discriminant analysis (SDA), decision trees (DT), and support vector machines (SVM) have faced limitations, including small datasets, insufficient accuracy, inadequate medical data, weak methodologies, and failure to consider primary symptoms. This work proposes a deep learning (DL) convolutional neural network (CNN) architecture utilizing CT scan images of the lungs for the rapid and accurate identification of COVID-19 infections. The approach leverages the Visual Geometry Group 16 (VGG16) model to extract significant features, such as size and color differences, from computed tomography (CT) scan images, facilitating a swift and precise diagnosis. The VGG16 model, implemented using the Keras library on top of TensorFlow, processes the preprocessed images through neural network layers to classify the images as COVID-19 positive or negative. The proposed model demonstrates a high accuracy rate of 94.12%, indicating that this method is both efficient and reliable for detecting COVID-19, offering a significant improvement over conventional diagnostic techniques and existing studies.
Two RC model and parameter estimation of lithium-ion battery Joshi, Girisha; Narayana Valluru, Lakshmi; Prakash Khade, Amol
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp730-739

Abstract

Electric vehicles are the trend of this decade. Frequent high-power requirements of electric vehicles, make the batteries discharge at higher C rate. Discharging at a higher C rate will lead to higher heat production leading to destruction and explosion of the battery. To optimize the charging and discharging C rates considering both safety and performance factors, battery management system (BMS) is used as an eternal component of power source. To estimate the state of charge (SOC), which is an essential component of BMS, accurate battery modelling is required. Two RC model is one of the most used lithium-ion battery model, due to its simplicity and accuracy. The equivalent circuit parameters, resistances and capacitance do change with SOC and temperatures. This paper focuses on estimation of equivalent circuit parameters for a wide range of temperatures and SOCs ranging from -20 degree celsius to +25 degree celsius and 100 to 0 respectively. We have developed two RC model for Panasonic 18650PF and estimated the parameters of the model using hybrid pulse power characterization (HPPC) data. MATLAB based parameter estimator is used in determining the equivalent circuit parameters.

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