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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 64 Documents
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Search results for , issue "Vol 36, No 1: October 2024" : 64 Documents clear
Improved dung beetle optimization algorithm and finite element analysis for spindle optimization Haohao, Ma; As’arry, Azizan; Xuping, Wu; Shah Ismail, Mohd Idris; Ramli, Hafiz Rashidi; Saad, Mohd Sazli; Delgoshaei, Aidin
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp559-569

Abstract

This research introduces an integrated optimization methodology for spindle design, combining the improved dung beetle optimization (IDBO) algorithm with finite element analysis (FEA). The IDBO algorithm, enhanced in population initialization and convergence factors, minimizes total deformation and mass, addressing a multi-objective optimization model. The obtained optimal parameters guide the construction of a finite element model, considering additional factors like stiffness and maximum stress. The ensuing FEA produces a foundation for constructing a response surface, further optimized to refine the initial design. Through the combination of the IDBO algorithm and FEA method, the mass of the spindle is reduced from 46.582 kg obtained by the IDBO algorithm solution to 28.479 kg, a total reduction of 38.86%, while meeting design requirements such as maximum total deformation. Modal analysis up to the sixth order validates the design correctness reveals dynamic spindle behavior and guarantees the design requirements. The study demonstrates the reliability and effectiveness of the proposed IDBO algorithm in conjunction with FEA, providing a versatile framework for engineering optimization.
Development of an algorithm for integrated UAV groups using visible light communication technology Alibekkyzy, Karygash; Keribayeva, Talshyn; Koshekov, Kayrat; Baidildina, Aizhan; Bugubayeva, Alina; Azamatova, Zhanerke
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp41-52

Abstract

Our research group dedicated its idea in developing and analyzing an algorithm for transforming integrated unmanned aerial vehicle (UAV) groups (IUGs) using visible light communication (VLC) technology. This innovative approach is designed to enhance UAV network coordination, addressing the complex challenges of communication within these networks. The primary issue addressed is the pressing need for advanced communication mechanisms within UAV networks to ensure efficient. This is a robust data transfer and complex coordination between UAVs. The existing systems lack the required adaptability and efficiency, leading to operational inefficiencies and reduced effectiveness in UAV applications. The main results of the study are concluded in the design and implementation of the conversion algorithm. Which provides efficient and reliable data transmission and sophisticated coordination between UAVs. Through careful mathematical modeling of UAV group dynamics and extensive MATLAB simulations, the study demonstrates the algorithm's ability to effectively control UAV formations. This method gives adaptability to different operational requirements and supports collision-free maneuvers. The algorithm's innovative design and the comprehensive approach adopted in the study, including the use of VLC technology and the integration of advanced restructuring methods, enable the effective resolution of the identified communication challenges within UAV networks.
Identification of faults in rotating machines using high precision FBG vibration sensor: a case study on PM schemes Nayak, Dipak Ranjan; Ghuge, Nilam N.; Mohapatra, Ambarish G.; Sharma, Pramod; Nayak, Narayan; Satapathy, Satyapriya; Khanna, Ashish
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp535-547

Abstract

Predictive maintenance (PM) is a data-driven approach to performing proactive maintenance by analyzing the condition of the equipment in any industrial setting. The high-precision sensors are widely adapted to meticulously analyze critical maintenance conditions using such a data-driven approach. In a similar context, a fiber brag grating (FBG) sensor is a passive and high-precision sensor that is widely used in industries where conventional sensors are not preferred. Broadly, this article presents four sub elements of the proposed integrated system such as the design of the sensor element, signal processing scheme (SPS), machine learning (ML) model for predicting anomalies, and decision support system (DSS) to suggest maintenance actions. Also, this article highlights an experimental case study on vibration monitoring and analysis of real-time signals for making proactive maintenance decisions. An FBG vibration sensor of center wavelength 1,550 nm is designed and utilized to acquire real-time vibration signatures of a rotating machine under test. A piezoelectric vibration sensor is used with the FBG sensor to compare the vibration response obtained during the test. Pre-processing of raw signals is performed using a moving average filter (MAV) followed by a low pass filter to nullify the effect of noise. To obtain proactive maintenance decisions, a DSS model is prepared by considering the processed vibration signatures. Various maintenance conditions are tested during the experimental analysis and detailed results analysis are presented.
Spiking neural network with blockchain for tampered image detection using forensic steganography images Basavanyappa, Gurumurthy Shikaripura; Danti, Ajit
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp477-485

Abstract

Accurate tools are required to acknowledge misleading images in order to maintain image legitimacy, and these tools must allow for legal operations on images. Additionally, after posting their images to the Internet, image owners lose rights over the images because there are no measures in place to safeguard them from misuse. One of the most well-liked techniques for addressing copyright disputes is the use of steganography technologies. The embedded steganography images can, sadly, be easily altered or deleted. To address this problem, this work presents the spiking neural network (SNN) with blockchain for tampered image detection utilizing forensic steganography images. Forensic steganography images that have been altered can be found with this SNN. Using steganography images from the database, SNN is trained in this model. The blockchain stores the owners’ access policies. The Python platform is used to implement the proposed strategy. F-measure, specificity, accuracy, precision, recall false positive rate (FPR), and false negative rate (FNR) are used to gauge how well the proposed approach performs. When compared to state-of-the-art approaches, the proposed approach obtained an impressive rise of 98.65%, in classification accuracy.
Performance evaluation of rank attack impact on routing protocol in low-power and lossy networks Al-Qaisi, Laila; Hassan, Suhaidi; Zakaria, Nur Haryani
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp242-251

Abstract

The internet of things (IoT) is a network of connected devices, enabling the exchange and collection of data from various environments. The routing protocol for low power and lossy networks (RPL) is a protocol for routing IPv6 over low-power wireless personal area networks, commonly used in IoT applications. However, RPL has several security and privacy issues that make it vulnerable to various attacks, including rank attacks (RA), which can lead to denial-of-service (DoS) scenarios. This research aims to address the impact of RA on RPL networks by conducting simulations using the Contiki/Cooja simulator with two topology types, random and grid, along with three RA scenarios and a normal network scenario. The study compares the performance of RPL network OF0 and MRHOF in terms of throughput, packet delivery ratio (PDR), hop count (HC) and delay. The results demonstrate that RA significantly degrades network performance and reduces network lifetime, thus draining its limited resources. Some possible solutions are also suggested to mitigate these attacks by focusing on core components of the network like objective function (OF) and node behavior. Future work will focus on studying security mechanisms for RPL against RA.
Optimizing dialog policy with large action spaces using deep reinforcement learning Thakkar, Manisha; Pise, Nitin
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp428-440

Abstract

Dialogue policy is responsible to select the next appropriate action from the current dialogue state to accomplish the user goal efficiently. Present commercial task-oriented dialogue systems are mostly rule-based; thus, they are not easily scalable to adapt multiple domains. To design an adaptive dialogue policy, user feedback is an essential parameter. Recently, deep reinforcement learning algorithms have been popularly applied to such problems. However, managing large state-action space is time consuming and computationally expensive. Additionally, it requires good quality and a reliable user simulator to train the dialogue policy which takes additional design efforts. In this paper, we propose a novel approach to improve the performance of dialogue policy by accelerating the training process by using imitation learning for deep reinforcement learning. We utilized proximal policy optimization (PPO) algorithm to model dialogue policy using a large-scale multi-domain tourist dataset MultiWOZ2.1. We observed a remarkable performance of dialogue policy with 91.8% task success rate, and an approximate 50% decrease in the average number of turns required to complete tasks without using user simulator in the early phase of training cycles. This approach is expected to help researchers to design computationally efficient and scalable dialogue agents by avoiding training from scratch.
User self-efficacy enhances business intelligence tools for organizational agility Al-Dwairi, Radwan Moh’d; Al-Khataybeh, Maali; Najadat, Dania; Rawashdeh, Adnan
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp592-602

Abstract

The primary objective of this paper is to investigate the interplay between individual self-efficacy (SE) and the adoption of business intelligence (BI) tools, and their combined effects on organizational agility and performance. This research offers a novel perspective by examining the relationship between individual SE and BI tools together, which was neglected in the previous research, shedding light on how these factors collectively influence organizational performance and agility. The importance of this study addresses the crucial need for understanding the role of individual capabilities in leveraging BI tools, especially in the context of rapidly changing environments. The study employs a quantitative approach to examine the proposed model. A survey was conducted with 174 respondents from private and public organizations in Jordan. The findings reveal significant and positive impacts of individual experiences, vicarious experiences (VE), and psychological feedback (PS) on SE. Moreover, the study demonstrates that SE significantly and positively influences the utilization of BI tools, consequently affecting organizational agility and performance. The significance of the study findings lies in its ability to bridge the gap between individual capabilities and the effective utilization of BI tools to equip businesses with invaluable insights for enhancing their decision-making processes.
How does natural language processing identify the issues? Assiroj, Priati; Alam, Sirojul; Spits Warnars, Harco Leslie Hendric
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp357-366

Abstract

Product innovation and service improvement have become essential or crucial for organisations, including public service organisations. The Indonesia Immigration Directorate released the m-passport application to enhance its quality of service. The m-passport application is considered good as it has been downloaded over a million times. Like immigration officers, this application seems to be at the forefront, reflecting an increasingly better service. However, there was still a need for significant improvement in the application. Improvements can be made to the application by considering user feedback or reviews. Reviews provided by users, approximately 12K, will serve as input for improving or enhancing the application. This was made possible as users interacti directly with the application. The most common issues are one-time password or OTP verification code with a probability value of 0.044, errors when logging in with a probability value of 0.283, and slow response applications with a probability value of 0.125.
A novel framework for MOOC recommendation using sentiment analysis Uthamaraj, Sujatha; Ranganathan, Gunasundari
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp603-613

Abstract

Massive open online courses (MOOC) are the largest initiative in eLearning, with the support of universities across the world. To increase course satisfaction in MOOCs, learners’ must relate to the courses that best suit their needs and interests. The goal of recommendation systems is to suggest items to users based on their preferences and past behaviour. A course recommender system makes recommendations based on the similarity of courses and past interactions with the MOOC platform. With a huge volume of online courses on multiple learning platforms, it has been difficult for learners to identify the course of their interest. To address these challenges, a novel framework for hybrid MOOC course recommendations is proposed to recommend courses from multiple learning platforms. It uses web scraping techniques to collect course data from various MOOC providers, such as Coursera, Udemy, and edX platforms. With the real time dataset, a deep learning chatbot captures the personalized learning requirements of learners and recommends using a user-user collaborative approach with the valence aware dictionary and sentiment reasoner (VADER) for sentiment analysis. It enhances the accuracy of recommendations with an root-mean-square error (RMSE) value of 0.541.
Enhancing radar signal processing through LVQ-Kalman fusion: a tsunami prediction perspective Shobha, Shobha; Narasimhaiah, Nalini
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp282-289

Abstract

In radar signal processing, the pursuit of precise prediction algorithms motivates the exploration of innovative methodologies. This study introduces a pioneering fusion of learning vector quantization (LVQ)- Kalman, merging LVQ with the advanced Kalman filter. The primary aim is to enhance adaptability and robustness, vital in weather monitoring and military surveillance. LVQ, known for its efficacy in pattern recognition and prediction, adjusts prototype vectors iteratively based on input data, ideal for radar signal intricacies. Various LVQ types are incorporated, tailored meticulously for specific radar applications. The Kalman filter, originally for aerospace, excels in tracking and predicting dynamic systems, seamlessly integrated to address uncertainties in radar data. By combining LVQ’s pattern recognition with the Kalman filter’s adaptability, the fusion aims to create a versatile system navigating radar data intricacies. Applications range from airborne target tracking to weather analysis and military surveillance. The integrated approach offers adaptability and robustness, vital for real-world implementations, particularly in tsunami detection. Future research may explore deep learning to further enhance adaptability. This fusion technique presents significant potential for advancing radar signal processing, promising accurate and adaptive systems, especially in critical applications like tsunami detection.

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