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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 9,138 Documents
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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.
Enhancing malware detection through self-union feature selection using gray wolf optimizer Abualhaj, Mosleh M.; Shambour, Qusai Y.; Abu-Shareha, Ahmad Adel; Al-Khatib, Sumaya N.; Amer, Amal
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp197-205

Abstract

This research explores the impact of malware on the digital world and presents an innovative system to detect and classify malware instances. The suggested system combines a random forest (RF) classifier and gray wolf optimizer (GWO) to identify and detect malware effectively. Therefore, the suggested system is called RFGWO-Mal. The RFGWO-Mal system employs the GWO for feature selection in binary and multiclass classification scenarios. Then, the RFGWO-Mal system uses a novel self-union feature selection approach, combining features from different subsets of binary and multiclass classification extracted using the GWO optimizer. The RF classifier is then applied for classifying malware and benign data. The comprehensive Obfuscated-MalMem2022 dataset was utilized to evaluate the suggested RFGWO-Mal system, which has been implanted using Python. The suggested RFGWO-Mal system achieves significantly improved results using the novel self-union feature selection approach. Specifically, the RFGWO-Mal system achieves an outstanding accuracy of 99.95% in binary classification and maintains a high accuracy of 86.57% with multiclass classification. The findings underscore the achievement of a self-union feature selection approach in enhancing the performance of malware detection systems, providing a valuable contribution to cybersecurity.
Design of stress detector with fuzzy logic method (GSR and heart rate parameters) Fajrin, Hanifah Rahmi; Sasmeri, Sasmeri; Prilia, Levina Riski; Untara, Bambang
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp56-68

Abstract

Stress is a condition of tension that affects emotions, thought processes, and the physical or psychological state of humans due to pressure from within or from outside a person, which can interfere with activities that can cause various diseases. Therefore, a tool is made to detect stress levels so that a person can monitor their condition and prevent stress from getting more severe and detrimental to the health of the body and mind. The stress level detection tool is designed using a galvanic skin response (GSR) sensor to detect skin response through a person's sweat glands and a heart rate sensor to detect heart rate. Furthermore, the reading results will be processed by microcontroller and then the stress level decision will be made using the fuzzy logic method and will be classified into Relax, Anxiety, Calm, and Stress. Based on the test results, the GSR parameter has the highest accuracy of 99.78%, and the heart rate parameter has the highest accuracy of 99.63%.
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.
Attention based English to Indo-Aryan and Dravidian language translation using sparsely factored NMT Dwivedi, Ritesh Kumar; Nand, Parma; Pal, Om
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp250-256

Abstract

Neural machine translation (NMT) is a sophisticated technique that employs a large, singular neural network to learn and execute automatic translation tasks. Unlike statistical machine translation systems, NMT handles the entire translation process in an end-to-end manner, removing the need for additional components. This approach has shown significant promise in translation quality and has become the prevalent method. In this study, we apply sparsely factored NMT to English and several Indo-Aryan (Hindi, Bengali) and Dravidian (Tamil, Malayalam) language pairs. Specifically, we develop the machine translation system using an attention-based mechanism. A significant problem with traditional transformers is the huge memory requirement. Therefore, a sparsely factored NMT (SFNMT) is used to reduce the memory requirement but also improves the training time, thereby, reducing the computing time. In this paper, take inspiration from Vaswani transformer and modify it to get the best results. The system’s performance was evaluated using the BLEU metric. The proposed model indtrl achieves a BLUE score of 32.13 (en→hi), 29.31 (en→be), 31.21 (en→ta), 21.12 (en→ml) and 32.67 (en→hi), 29.38 (en→be), 31.75 (en→ta), 21.17 (en→ml) without backtranslation and with backtranslation. To evaluate the performance of the system, we compared the results with those of existing systems. The developed system demonstrated a marginally higher BLEU score than both AnglaMT and Google translate.
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.
Internet of things enabled landfill pollution gas monitoring Junus, Mochammad; Putradi, David Fydo; Soelistianto, Farida Arinie; Anshori, Mohammad Abdullah; Ardiansyah, Rizky
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp48-55

Abstract

Due to the increasing concern on how to manage wastes and ensure environmental safety across the globe, a new tool that assists in the monitoring of methane, humidity, and temperature in the landfill using internet of things (IoT) has been created. This system uses ESP32 microcontroller and MQ-4 and DHT-22 sensors to measure environmental conditions at three different spots in a landfill. The samples of data are collected at three times a day, that is, in the morning at 7:00 am, at midday at 12:00 pm and in the evening at 5:00 pm and the data is transmitted to an online sheet where the public can access it in real time hence increasing transparency in the management of wastes. The tool shows a very good precision and effectiveness and the parameters are 94. 6% data integrity over three months testing period. The first findings show that the mean methane concentration is the highest at midday, which is related to the temperature and underlines the role of temperature in the methane emission process. The presented IoT based monitoring system also enhances the accuracy and efficiency in the monitoring of landfill gas and at the same time reduces the intervention of human effort and increases the capability to make prompt adjustments to changes in the environment. Used as an instrument for obtaining accurate and easily understandable data, it is hoped that this tool will in some way help to enhance global environmental health and safety standards, and help pave a way for methane storage for renewable energy purposes.
Optimizing carrier transport properties in the intrinsic layer of a-Si single and double junction solar cells through numerical design Prayogi, Soni; Hamdani, Dadan; Darminto, Darminto
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp69-77

Abstract

This research aims to improve the performance of a-Si: H solar cells, particularly in terms of carrier transport properties, through a numerical design approach utilizing AFORS-HET simulation software. By performing a series of rigorous computer simulations, we explore the potential regulation of the intrinsic layer thickness, carrier mobility, loading factor, and density of states (DoS) distribution in the solar cell's intrinsic layer. Recombination losses are reduced, and light absorption efficiency is significantly increased when the intrinsic layer thickness is adjusted, as shown by simulation findings. Moreover, reduction of transit times and enhancement of the total efficiency of the solar cells depend on increased carrier mobility. Parameters can be adjusted to attain optimal performance under various operating situations by adjusting the DoS and load factors. Furthermore, the simulations provide insightful information about the interactions between the junctions in solar cells with double junctions. Our results of this research provide an important contribution to efforts to develop more efficient and sustainable a-Si: H solar cells and emphasize the importance of numerical design approaches in photovoltaic technology.
Optimizing dynamic response and stability of pressure-controlled swash plate type axial piston pump Verma, Vivek; Kumar, Sachin; Anand, Apurva
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.pp771-780

Abstract

The main objective of the paper is to explore the role of lead-lag compensators in improving the performance of control systems for variable delivery hydraulic axial piston pumps (VAPP). These compensators offer a range of benefits, including stability enhancement, transient response optimization, frequency response modification, disturbance rejection, and robustness improvement. A mathematical model of the hydro-mechanical system is developed, and the transfer function for the dynamic system is established. The simulation of the model with lead-lag compensator significantly enhanced the phase margin to 55.70 and gain margin to 12.3 dB ensuring robust control for pressure-controlled VAPP, whereas the uncompensated system is marginally stable. The compensated system exhibits better transient and steady-state response. The optimized lead-lag compensated system achieves a maximum percentage overshoot of 12.1% and a settling time of 1.95 sec. This is a substantial improvement compared to the uncompensated system with a maximum % overshoot of 20.5% and a settling time of 2.39 sec. The improved response tends to induce greater damping (ζ) in the compensated system from 0.015 to 0.108 and increases leakage coefficient (K) from 3.38×10-12 m3/Pa.s to 24.34×10-12 m3/Pa.s. Optimized lag-lead compensator ensures stability, responsiveness adapting effectively to dynamic operating conditions of VAPP for aerospace application.
Hydrophobicity signal analysis for robust SARS-CoV-2 classification Jamhuri, Mohammad; Irawan, Mohammad Isa; Mukhlash, Imam; Tri Puspaningsih, Ni Nyoman
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.pp1294-1305

Abstract

Rapid and accurate classification of viral pathogens is critical for effective public health interventions. This study introduces a novel approach using convolutional neural networks (CNN) to classify SARS-CoV-2 and non-SARS-CoV-2 viruses via hydrophobicity signal derived from DNA sequences. Conventional machine learning methods grapple with the variability of viral genetic material, requiring fixed-length sequences and extensive preprocessing. The proposed method transforms genetic sequences into image-based representations, enabling CNNs to handle complexity and variability without these constraints. The dataset includes 8,143 DNA sequences from seven coronaviruses, translated into amino acid sequences and evaluated for hydrophobicity. Experimental results demonstrate that the CNN model achieves superior performance, with an accuracy of over 99.84% in the classification task. The model also performs well with extended sequence lengths, showcasing robustness and adaptability. Compared to previous studies, this method offers higher accuracy and computational efficiency, providing a reliable solution for rapid virus detection with potential applications in bioinformatics and clinical settings.

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