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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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Chaotic crow search enhanced CRNN: a next-gen approach for IoT botnet attack detection Antony, Veena; Thangarasu, Nainan
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1745-1754

Abstract

Internet of things (IoT) botnet attack detection is crucial for reducing and identifying hostile threats in networks. To create efficient threat detection systems, deep learning (DL) and machine learning (ML) are currently being used in many sectors, mostly in information security. The botnet attack categorization problem is difficult as data dimensionality increases. By combining convolutional and recurrent neural layers, our work effectively addressed the vanishing and expanding gradient difficulties, improving the ability to capture spatial and temporal connections. The problem of weight decaying and class imbalance affects the accuracy rate of the existing DL models. In convolutional neural network (CNN), fully connected layer optimizes the hyperparameters by utilizing its comprehension of the chaotic crow search method. The chaotic mapping maintains equilibrium between the global and local search spaces. The crow's strategy for hiding food is the main source of inspiration for optimizing the learning rate, weight, and bias components involved in the prediction process. When compared to other existing algorithms, the UNSW-NB15 dataset's results for IoT botnet attack detection in the presence of a high degree of class imbalance demonstrated the effectiveness of the proposed convolutional recurrent neural network (CRNN) boosted with chaotic crow searching algorithm, which produced the highest detection rate with the lowest false alarm rate.
RIBATS: RSSI-based adaptive tracking system with ASEKF for indoor WSN Ainul, Rafina Destiarti; Agung, Hendi Wicaksono
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp225-234

Abstract

Wireless indoor tracking systems face challenges due to environmental conditions and signal attenuation, affecting location accuracy, crucial in wireless sensor network (WSN) applications. Many tracking techniques rely on specific path loss models proposed by previous researches, but these models are susceptible to changes in environmental conditions, impacting estimation outcomes. In order to solve these problems, this paper propose adaptive tracking system using received signal strength indicator (RSSI) measurement parameter called as RIBATS. Adaptive in this system refers to the reliability of an algorithm for obtaining the accurate location without any path loss modelling at dynamic indoor environments. The enhancement of weighted centroid localization (eWCL) scheme calculates the location estimation only using RSSI data measurement without propagation characterisic determination. However, estimation result from eWCL still have high error at certain area. Hence, by defining a multiplier factor as adaptive scaled to the covariance matrix of EKF can eliminate distortion effects from eWCL called as adaptive scaled extended Kalman filter (ASEKF) algorithm. An effective variance estimation algorithm for adaptive indoor tracking system using eWCL and ASEKF combination achieve 0.82 meters mean square error (MSE) value with 55.67% error reduction. Then, without using multiplier scale factor at EKF algorithm only reduce previous eWCL at 3.78% with 1.78 meters MSE value.
Predictive modeling for equity trading using sentiment analysis Gondaliya, Chetan; Parikh, Abhishek
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp575-584

Abstract

Warren Buffett’s investment philosophy highlights the importance of generating wealth through available capital, but investors require more advanced tools for informed decision-making. Current research is focused on developing a modeling technique that leverages computer algorithms, including sentiment analysis. This method evaluates public sentiment about companies through social media, aiding investors in identifying promising stocks and safeguarding their wealth against unfavorable market conditions. In India, the banking, real estate, and pharmaceutical sectors are among the most robust and rapidly growing industries; however, deciding to invest in these sectors remains debatable. To address this, the proposed study aims to develop a hybrid prediction model that combines sentiment and technical analysis to uncover short-term trading opportunities. This model utilizes a two-layer ensemble stacking technique, training three distinct machine learning algorithms in the first layer and aggregating their outputs in the second layer. The proposed model significantly outperforms traditional methods in terms of accuracy, enabling investors to make more confident and profitable decisions in the Indian stock market.
Integrating gamification to increase users’ engagement to adhere COVID-19 interventions using extended TAM Afiq Kalana, Mohd Hazim; Junaini, Syahrul Nizam; Jali, Suriati Khartini; Rosmansyah, Yusep; Putri, Atina; Kamal, Ahmad Alif
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1936-1945

Abstract

Gamification has shown potential for enhancing motivation and engagement, yet its effectiveness in promoting adherence to COVID-19 preventive measures remains underexplored. With decreasing public attentiveness, this study examines the role of gamification in encouraging compliance with health protocols through an extended technology acceptance model (TAM) and structural equation modeling (SEM). A gamified mobile application was developed, incorporating features such as points, badges, and progress tracking, and was designed to appeal to younger audiences. Data collected from 150 secondary school students in Sarawak, Malaysia, indicated that perceived usefulness, perceived ease of use, and attitude toward the app significantly influenced engagement with COVID-19 preventive measures. Among these factors, perceived usefulness demonstrated the strongest effect on engagement (β = 0.424, t = 4.812, p < 0.001). The findings highlight the potential of gamification to enhance compliance with COVID-19 protocols.
A simple machine learning technique for sensor network wireless denial-of-service detection Hameed, Shaik Abdul; Indurthi, Ravindra Kumar; Arumalla, Gopya Sri; Bachu, Venkatesh; Malluvalasa, Lakshmi S. N.; Peteti, Venkateswara Rao
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1690-1697

Abstract

Wireless sensor networks (WSNs) are integral to numerous applications but are vulnerable to denial-of-service (DoS) attacks, which can severely compromise their functionality. This research proposes a lightweight machine learning approach to detect DoS attacks in WSNs. Specifically, we investigate the efficacy of decision tree (DT) algorithms with the Gini feature selection method, alongside random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN) classifiers. Data collected from normal and DoS attack scenarios are preprocessed and used to train these models. Experimental results showcase the effectiveness of the proposed approach, with the DT algorithm exhibiting high accuracy exceeding 90%, surpassing other classifiers in computational efficiency and interpretability. This study contributes to enhancing the security and reliability of WSNs, offering insights into potential future optimizations and algorithmic explorations for robust DoS attack detection.
Virtual learning environment on satisfaction and academic performance of students in institutions of higher learning Olanloye, Odunayo Dauda; Idowu, Peter Adebayo; Adeniyi, Abidemi Emmanuel; Badmus, Afolake Afusat; Aroba, Oluwasegun Julius
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp258-271

Abstract

As a result of the COVID-19 outbreak in 2020, education institutions across the world had to come to a functional standstill since they had to protect their students from viral exposures thereby affecting academic activities. However, several institutions had to adopt online virtual learning environments (VLE) using basic information and communication technology tools to provide platforms for teaching and learning thereby mitigating the effects of the pandemic on the students. This study was focused on the identification of the various types of VLE tools that were adopted alongside the impact that these tools had on learning satisfaction and the academic performance of students of higher learning in Nigeria. This study adopted a purposive simple random selection of undergraduate students of the department of computer science who had adopted the use of VLE to learn during the period of the pandemic. The results of the study showed that the most popular VLE tools were Zoom, Google Classroom, WhatsApp, Telegram, Coursera, Google Forms and learning management systems (LMS) while the least popular VLE tools were Microsoft Teams, Moodle/Edmondo, and Google Meet. The results showed that the students agreed to their behavioral intention to use VLE, the impact of VLE on learning satisfaction, and the impact of VLE on academic performance alongside the existence of a positive correlation among the research variables.
BFT water color classification in tilapia aquaculture using computer vision Suwandi, Bondan; Anggraeni, Sakinah Puspa; Palokoto, Toto Bachtiar; Sulistya, Budi; Sujatmiko, Wisnu; Septiawan, Reza; Taufik, Nashrullah; Rufiyanto, Arief; Ardiansyah, Arif Rahmat
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp497-508

Abstract

Biofloc technology (BFT) is one of the most promising aquaculture cultivation methods in the modern aquaculture era because of its high efficiency level, especially in water and fodder use. Usually, the general condition of the biofloc can be known from the color of the water. By utilizing the vision sensor, BFT color identification can be done automatically, which helps cultivators find out their BFT system’s condition. In this research, a classification was made for the watercolor of the BFT Tilapia system based on the microbial community color index (MCCI) value and the initial cultivation conditions where algae and nitrifying bacteria had not developed significantly. The color classifications of the bioflocs are clear, green, browngreen, green-brown, and deep-brown. Clear color is the new classification to indicate BFT water conditions in the initial cultivation phase. Further, two computer vision algorithm methods are introduced to classify the color of BFT system water. The first method combines the B/W algorithm and MCCI calculations, while the second algorithm uses the Manhattan distance algorithm approach. From the experiments that have been carried out, both computer vision algorithms methods for classifying biofloc colors have shown promising results.
A novel deep learning based spatial delay feature aware encoder decoder module for enhanced CSI feedback in massive MIMO Jayashankar, Parinitha; Rangaswamy, Chigalakkappa; Shobha, Byrappa N.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1862-1869

Abstract

The algorithm presented in this study addresses the challenge of reconstructing downlink channel state information (CSI) in massive multiple input multiple output (MIMO) systems with a focus on enhancing efficiency and accuracy. It begins by acquiring both downlink and uplink CSI data alongside other critical parameters such as the number of iterations and convolutional filter specifications. The process initiates with the vectorization of downlink CSI data followed by compression through a fully connected layer, effectively reducing dimensionality to manage computational complexity. The iterative reconstruction phase then unfolds, where each iteration updates an intermediary variable using a refined formula that incorporates the compressed CSI representation and correction factors. This iterative refinement aims to progressively enhance the accuracy of the reconstructed CSI. A pivotal aspect of the algorithm involves an optimized Encoder-Decoder framework designed to handle spatial-delay features inherent in MIMO systems. This framework employs thresholding operations to eliminate insignificant features, ensuring that the reconstructed CSI accurately reflects the crucial aspects of the channel. Simultaneously, an information module utilizes uplink CSI data to adjust weights during reconstruction, thereby further refining the accuracy of the downlink CSI estimation.
Design of face recognition based effective automated smart attendance system Bangare, Jyoti L.; Chikmurge, Diptee; Kaliyaperumal, Karthikeyan; Meenakshi, Meenakshi; Bangare, Sunil L.; Kasat, Kishori; Rane, Kantilal Pitambar; Veluri, Ravi Kishore; Omarov, Batyrkhan; Jawarneh, Malik; Raghuvanshi, Abhishek
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2020-2030

Abstract

The issue of automatic attendance marking has been successfully resolved in recent years through the utilization of standard biometric approaches. Although this strategy is automated and forward-thinking, its use is hindered by time constraints. Acquiring a thumb impression requires the individual to form a line, which might lead to inconvenience. The innovative visual system utilizes a computer and camera to detect and record students’ attendance based on their facial features. This article presents a face recognition based automatic attendance system. This system includes- image acquisition, image enhancement using histogram equalization, image segmentation by fuzzy C means clustering technique, building classification model using K-nearest neighbour (KNN), support vector machine (SVM) and AdaBoost technique. For experimental work, 500 images of students of a class are selected at random. Accuracy of KNN algorithm in proposed framework is 98.75%. It is higher than the accuracy of SVM (96.25%) and AdaBoost (86.50%). KNN is also performing better on parameters likesensitivity, specificity, precision and F_measure.
Torque ripple minimization and performance enhancement of switched reluctance motor for electric vehicle application Mandake, Yogesh B.; Bankar, Deepak S.; Nehete, Amit L.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp70-78

Abstract

Switched reluctance motors (SRMs) are an attractive choice for electric vehicle (EV) applications but suffer from certain limitations, such as high torque ripple and acoustic noise. This paper presents ongoing research and development activity details to enhance the performance of SRMs for EV applications. The poor performance of a conventional SRM which is available in market with a rating of 8/6 poles, 48 V, 500 W, and 2,000 rpm is tested. A motor model of the same rating is developed using ANSYS Maxwell software. Motor performance parameters important for EV applications, such as efficiency, rated torque and torque ripple are compared with the conventional motor. One novel technique to reduce the torque ripple of SRM is discussed along with the results. Torque ripple of developed software model is reduced by 24.52% without a reduction in the efficiency and rated torque of the motor. The performance of the developed SRM software model is better compared to conventional SRMs available in the market. 2D and 3D models of SRM were presented using ANSYS Maxwell software.

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