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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 65 Documents
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Search results for , issue "Vol 35, No 3: September 2024" : 65 Documents clear
Enhancing emotion detection with synergistic combination of word embeddings and convolutional neural networks Jadon, Anil Kumar; Kumar, Suresh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1933-1941

Abstract

Recognizing emotions in textual data is crucial in a wide range of natural language processing (NLP) applications, from consumer sentiment research to mental health evaluation. The word embedding techniques play a pivotal role in text processing. In this paper, the performance of several well-known word embedding methods is evaluated in the context of emotion recognition. The classification of emotions is further enhanced using a convolutional neural network (CNN) model because of its propensity to capture local patterns and its recent triumphs in text-related tasks. The integration of CNN with word embedding techniques introduced an additional layer to the landscape of emotion detection from text. The synergy between word embedding techniques and CNN harnesses the strengths of both approaches. CNNs extract local patterns and features from sequential data, making them well-suited for capturing relevant information within the embeddings. The results obtained with various embeddings highlight the significance of choosing synergistic combinations for optimum performance. The combination of CNNs and word embeddings proved a versatile and effective approach.
Analyzing electroencephalogram signals with machine learning to comprehend online learning media Venu, Vasantha Sandhya; Moorthy, Chellapilla V. K. N. S. N.; Patil, Preeti S.; Kale, Navnath D.; Andhare, Chetan Vikram; Tripathi, Mukesh Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1876-1885

Abstract

In E-learning, evaluating students' comprehension of lecture video content is significant. The surge in online platform usage due to the pandemic has been remarkable, but the pressing issue is that learning outcomes still need to match the growth. Addressing this, a scientific system that gauges the comprehensibility of lecture videos becomes crucial for the effective design of future courses. This research paper is based on a cognitive approach utilizing EEG signals to determine student's level of comprehension. The study involves the design, evaluation, and comparison of multiple machines learning models, aiming to contribute to developing an efficient learning system. Fifteen distinct machine learning (ML) classifiers were implemented, among them AdaBoost (ADA), gradient boosting (GBC), extreme gradient boosting (XGboost), extra trees (ET), random forest (RF), light gradient boosting machine (light gum), and decision tree (DT) algorithms standouts. The DT exhibited exceptional performance across metrics such as area under the curve (AUC), accuracy, recall, F1 score, Kappa, precision, and matthews correlation coefficient (MCC). It achieved nearly 1.0 in these metrics while taking a short training time of only 1.7 seconds. This reveals its potential as an efficient classifier for electroencephalography (EEG) datasets and highlights its viability for practical implementation.
Mathematical model enhancing flash memory reliability through DFT-driven error correction coding Sukanya, Poornima Huchegowda; Chowdaiah, Nagaraju
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1468-1479

Abstract

Flash memory, ubiquitous in diverse electronic devices, confronts persistent challenges stemming from inherent errors that jeopardize data integrity. This research situates itself at the intersection of these challenges and advancements, proposing an inventive error correction coding framework that harnesses the unique capabilities of analysis with a hybrid error control coding (HECC) approach. In the proposed work, a mathematical model aimed at enhancing the flash memory by identifying the error pattern within the pages using the discrete fourier transform (DFT). By incorporating distinctive DFT mathematical properties, the proposed technique intends to improve flash memory error correction beyond traditional methods. The flash storage defect detection and rectification results with hybrid error correction coding achieved bit error rate (BER) of 4.3e-6, latency 14.1, mean 15.1 and standard deviation 1.0. Error correction efficiency 98% and storage overhead 10%. With this approach results are significantly improving the error correction efficiency, reduce storage overhead and enhanced adaptability to diverse error patterns.
Optimizing assembly processes with augmented reality: a case study on TurtleBots Wu, Mingyu; Koh, Ye Sheng; Yeong, Che Fai; Goh, Kai Woon; Dares, Marvin; Lee Ming, Eileen Su; Holderbaum, William; Sunar, Mohd Shahrizal
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1547-1555

Abstract

Augmented reality (AR) technology is revolutionizing traditional assembly processes, offering intuitive and interactive guidance that significantly enhances operational efficiency and accuracy. This study investigates the impact of AR on the assembly of Turtlebots, a complex task representative of industrial applications. Through a comparative analysis involving traditional paper manuals, modified paper manuals, and AR-based manuals, the benefits of AR integration are quantitatively assessed. Participants utilizing AR-based manuals completed the Turtlebot assembly 21.72% faster than those using traditional paper manuals, with a notable reduction in assembly time from an average of 03:00:40 to 02:21:26. Furthermore, the incidence of assembly errors significantly decreased, with AR manual users making an average of 2.25 errors compared to 5 by paper manual users. These findings underscore the potential of AR to expedite complex assembly tasks and enhance the accuracy of these processes. The study highlights the novel application of AR in improving both the speed and quality of assembly in an industrial context, demonstrating AR’s role as a pivotal technology for the future of manufacturing. 
Secured web application based on CapsuleNet and OWASP in the cloud Vallabhaneni, Rohith; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Vaddadi, Srinivas A.; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1924-1932

Abstract

The tremendous use of sensitive and consequential information in the advanced web application confronts the security issues. To defend the web application while it processing the information must requires the security system. The detection of attacks of web is made by the payload or HTTP request-based detection in association with the scholars. Some of the scholars provide secured attack model detection; however, it fails to achieve the optimal detection accuracy. In concern with these issues, we propose an innovative technique for the attack detection the web applications. The proposed attack detection is based on the novel deep CapsuleNet based technique and the process begins with pre-processing steps known as decoding, generalization, tokenization/standardization and vectorization. After the pre-processing steps the information are passed to deep CapsuleNet for extracting the features for attaining the temporal dependencies from the sequential data. The subtle patterns in the information also detected using the proposed work. Simulation is effectuated to demonstrate the effectiveness of the proposed work and compared with other existing works. Our proposed system provides better accuracy in detecting the attacks than the state-of-art works.
Optimize the position of distributed generations in distribution grid by using improved loss sensitivity factor Phan, Dinh Chung; Luu, Ngoc An
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1370-1378

Abstract

This research proposed a method to determine the optimal position of distributed generations in a distribution grid. The method is improved from the loss sensitivity factor method. An algorithm is developed to determine both the position and size of distributed generations. This algorithm is validated via IEEE 33 bus distribution grid in two cases of distributed generation size including unknown size and constant size. The results were analyzed and compared to other previous algorithms including loss sensitivity factor-based algorithm and other algorithms. Results indicated the optimal position of each distributed generation to minimize the power loss. Results also indicated that with the proposed algorithm, the loss reduction rate (LRR) is the highest in comparison to that with other previous algorithms.
A hybrid SATS algorithm based security constrained optimal power flow using FACTS devices Cherukupalli, Kumar; Chinda, Padmanabha Raju
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1388-1396

Abstract

In the realm of power systems, achieving optimal operation while ensuring security remains a paramount challenge. The security constrained optimal power flow (SCOPF) problem deals with optimizing power system operations while taking into account security limitations. Flexible alternating current transmission system (FACTS) is a system consisting of static equipment used for transmitting electrical energy in the form of AC. The static synchronous series compensator (SSSC) is a specific form of series FACTS device. The unified power flow controller (UPFC) is a FACTS device that is connected in parallel and series with a transmission line. In this research, hybrid simulated annealing and tabu search (hybrid SATS) algorithm is designed to solve SCOPF problems that involve use of FACTS devices. The combination of simulated annealing and tabu search is intended to improve algorithm's pace of convergence and the quality of its solutions. Hybrid SATS with FACTS devices are used to investigate line flow limit violations during single line failures and ensure power flows remain within their security limitations. The efficacy of proposed algorithm is demonstrated through case studies utilizing IEEE 30 bus system. These case studies demonstrate algorithm's capabilities to achieve optimal and secure power system functioning to demonstrate its effectiveness.
Predicting vulnerability for brain tumor: data-driven approach utilizing machine learning Effendi, Yutika Amelia; Sofiah, Amila; Hidayat, Niko Azhari; Ebrie, Awol Seid; Hamzah, Zainy
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1579-1589

Abstract

Brain tumors, whether benign or malignant, present a complex and multifaceted challenge in healthcare, affecting individuals across various age groups. Predicting the vulnerability of brain tumors using health risk factors and symptoms is crucial, yet there have been limited research studies, particularly those integrating artificial intelligence (AI) technology. This research explores machine learning models such as support vector machines (SVMs), multi-layer perceptrons (MLPs), and logistic regression (LR) for the early detection of brain tumors. Evaluation metrics, including accuracy, precision, recall, and F1-score, are employed to assess model performance. The results indicate that the SVM outperforms other models, providing a robust foundation for predictive accuracy. To enhance accessibility and usability, the research also integrates these models into a mobile application predictor. The application is beneficial for assisting individuals in early detection by identifying potential risk factors and symptoms that may lead to a brain tumor. In conclusion, the integration of machine learning through a mobile application represents a transformative approach to personalized healthcare. By empowering individuals with cutting-edge technology, this research strives to enhance early detection and decision-making regarding potential brain tumor risks and symptoms, ultimately contributing to improved patient outcomes and quality of life.
Design and implementation of an automated irrigation control for home plantations Pérez-Baca, Molly Scarlet; Sambrano-Luna, Karina Lizeth; Sánchez-Ramírez, Jhony Miguel; Cabana-Cáceres, Maritza; Castro-Vargas, Cristian
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1437-1446

Abstract

In today's society, keeping our gardens attractive is complex, especially if you need more time to care for them. This can cause the plant to wilt if it is not watered occasionally to keep the soil moist. In summer, this problem tends to get worse because the temperature tends to rise and reach high degrees. The objective is to design an automatic and manual irrigation system with a humidity detector through hardware programming and free software to solve this. The necessary components will be identified and selected, humidity thresholds will be established, and the adoption of technologies such as internet of things (IoT), Arduino, and humidity sensors will be promoted to solve the problem in automated irrigation systems. The technical specifications of the components are described, and the circuit design is presented. A programming algorithm will be developed to control the frequency and duration of irrigation, as well as the state of the water pump. Implementing the automated system will allow precise water supply control, contributing to the healthy growth of plants and crops in green areas.
Obstacle detection to minimize delay and Q-learning to improve routing efficiency in VANET Dev, Kishore Chandra; Barani, Selvaraj
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1507-1516

Abstract

Nowadays, several service providers in urban areas significantly consider vehicular ad hoc networks (VANET). VANETs can enhance road safety, prevent accidents, and grant passengers entertainment. Though in VANET, efficient routing has remained an open problem. VANET is dynamic; the frequent update in the situation originates through several aspects, such as traffic conditions and updates in the road topology, which demand a suitably adaptive routing. The existence of blocking obstacles degrades routing approaches and increases the failure of paths. These issues build an excessive amount of resource utilization and increase network delay. To solve these issues, obstacle detection to minimize delay and Q-learning to improve routing efficiency (ODQI) in VANET is proposed. This mechanism uses the spanning tree algorithm detects the obstacle. Clustering can be used to manage the topology in VANETs. The dingo algorithm selects the best cluster head (CH) based on vehicle bandwidth, speed, and link lifespan. Furthermore, the sender forwards the traffic information from the sender to the receiver by applying a Q-learning algorithm. This learning algorithm computes the award function to choose the forwarder, improving the routing efficiency. Simulation results demonstrate that the ODQI mechanism increases the CH lifetime and minimizes the network delay.

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