cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
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 66 Documents
 2   3   4   5   6 
Search results for , issue "Vol 37, No 1: January 2025" : 66 Documents clear
Innovative virtual reality solutions for technical training in heavy construction equipment repair and maintenance Istiono, Wirawan; Wira Pratama, Andhika Nugraha
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.pp627-635

Abstract

The construction industry is significantly impacted by heavy construction equipment, including bulldozers, excavators, and vehicles. This equipment speeds up building, moves supplies, and builds infrastructure. Using heavy construction equipment correctly can boost productivity and shorten project timelines. Due to their complexity and scale, this equipment must be maintained and repaired. Poor maintenance and repair of heavy construction equipment can reduce performance, damage, and even cause accidents. Due to these problems, this study focuses on the design and development of a simulation training application to enhance the technical skills of workers in maintaining and repairing heavy construction equipment using virtual reality (VR) technology, the development of this application will be carried out using Unreal Engine 5 and thereafter tested and implemented at PT Menara Indonesia or M-Knows Consulting, Indonesia. At the end of this study, the design and development of a VR training simulation application for heavy equipment repair has been successfully completed. After testing the VR application and conducting user acceptance tests, it was concluded that the created VR application greatly assists M-Knows Consulting in training workers to perform maintenance and repair on heavy equipment, with a user acceptance rate of 84%.
Predicting autism spectrum disorder through sentiment analysis with attention mechanisms: a deep learning approach Mareeswaran, Murali Anand; Selvarajan, Kanchana
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.pp325-334

Abstract

Autism spectrum disorder (ASD) is considered a spectrum disorder. The availability of technology to identify the characteristics of ASD will have major implications for clinicians. In this article, we present a new autism diagnosis method based on attention mechanisms for behavior modeling-based feature embedding along with aspect-based analysis for a better classification of ASD. The hybrid model comprises a convolutional neural network (CNN) architecture that integrates two bidirectional long short-term memory (BiLSTM) blocks, together with additional propagation techniques, for the purpose of classification the origins of Autism Tweet dataset; the proposed work takes Autism Tweet dataset and preprocesses them to employ n-gram to extract features of which the features of the ASD behavior are fed to generate the significant behavior for classification. The model takes into account both behavior-guided features across every aspect of the Class/ASD to provide higher accuracy using Adam optimizer. The experimental values inferred that the n-BiLSTM technique reaches maximum accuracy with 98%.
Characterization of A2G UAV communication channels under rician fading conditions Guno, Yomi; Adiono, Trio; Suryana, Joko; Triputra, Fadjar Rahino; Hidayat, Asyaraf; Octaviany, Siti Vivi
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.pp143-153

Abstract

The variation in the k-factor value significantly influences the performance of unmanned aerial vehicle (UAV) air-to-ground point-to-point line of sight (A2G PTP LOS) communications over a Rician channel at 1,800 MHz using quadrature phase shift keying (QPSK) modulation and orthogonal frequency division multiplexing (OFDM) techniques. The research emphasizes the impact of the k-factor, which quantifies the dominance of the line-of-sight component over multipath scattering. The variation in the k-factor significantly influences UAV A2G PTP LOS communication performance for the empirical model (EM), as it involves precise measurements of the received power level in dBm from UAV to ground control station (GCS) across varying distances and altitudes. We introduce a method to compute the k-factor by assessing the ratio of the line-of-sight signal power to the multipath signal power, thereby enhancing channel modeling accuracy. Empirical analysis shows a strong correlation between bit error rate (BER) and signal-to-noise ratio (SNR) with differing k-factor values; a higher k-factor of 16.3 markedly improves performance, virtually eliminating errors at a 10 dB SNR, while a lower k-factor of 2.39 still shows significant errors at a 30 dB SNR. These results highlight the necessity of optimizing the k-factor in UAV A2G PTP LOS systems to ensure stable and reliable communication under diverse operational conditions.
A conceptual approach of optimization in federated learning Mar’i, Farhanna; Supianto, Ahmad Afif
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.pp288-299

Abstract

Federated learning (FL) is an emerging approach to distributed learning from decentralized data, designed with privacy concerns in mind. FL has been successfully applied in several fields, such as the internet of things (IoT), human activity recognition (HAR), and natural language processing (NLP), showing remarkable results. However, the development of FL in real-world applications still faces several challenges. Recent optimizations of FL have been made to address these issues and enhance the FL settings. In this paper, we categorize the optimization of FL into five main challenges: Communication Efficiency, Heterogeneity, Privacy and Security, Scalability, and Convergence Rate. We provide an overview of various optimization frameworks for FL proposed in previous research, illustrated with concrete examples and applications based on these five optimization goals. Additionally, we propose two optional integrated conceptual frameworks (CFs) for optimizing FL by combining several optimization methods to achieve the best implementation of FL that addresses the five challenges.
LMD-based fault detection scheme for TCSC compensated wind integrated transmission lines Market, Saritha; Swaminathan, Seenivasan; Gurram, Ravindranath
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.pp26-34

Abstract

In this paper, a fast fault detection scheme is presented to detect the faults in thyristor-controlled series capacitor (TCSC) compensated transmission line connected with the large wind farms to export the electrical power to grid. The proposed logic utilizes the current information at the relay location and processes through the local mean decomposition technique to extract the magnitude features of the current. Cumulative sum of these features are computed for each phase currents to detect the faults in the transmission lines and further to classify the faulty phase in the system. The residual component of the current is used to detect the ground involvement in the faulty phase. The proposed method is tested during variety of faults by changing the nature of the fault using the fault parameters. Furthermore, the impact of the TCSC is also investigated along with the dynamic changes of the WF and their influence on the protection scheme. All the simulations are performed in MALTAB-Simulink software.
Seasonal meat stock demand used comparison of performance smoothing-average forecasting Tundo, Tundo; Saifullah, Shoffan; Dharmawan, Tio; Junaidi, Junaidi; Devia, Elmi
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.pp425-433

Abstract

Seasonal patterns significantly influence the demand for beef stock, especially in rural areas that rely on natural feed. Accurate forecasting is essential for managing this demand due to beef's status as a government-regulated nutritional commodity. Food production, consumption, and income levels affect the demand for beef stocks. This research aims to identify the most precise forecasting method for predicting future beef stock needs. We evaluated multiple techniques, including single exponential smoothing (SES), double exponential smoothing (DES), single moving average (SMA), and double moving average (DMA), using the mean absolute percentage error (MAPE) metric, focusing specifically on beef supplies in Pemalang. The results indicated that the DMA method achieved the highest accuracy with a MAPE value of 5.993% at the 4th -order parameter. Additionally, increasing the data volume improved forecasting accuracy, demonstrating the effectiveness of the DMA method for beef stock prediction.
A framework for dynamic monitoring of distributed systems featuring adaptive security Periyasamy, Sudhakar; Kaliyaperumal, Prabu; Alagarsamy, Abinaya; Elumalai, Thenmozhi; Karuppiah, Tamilarasi
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.pp660-669

Abstract

Distributed systems play a crucial role in today’s information-based society, enabling seamless communication among governmental, industrial, social, and non-governmental institutions. As information becomes increasingly complex, the software industry is highly concerned about the heterogeneity and dynamicity of distributed systems. It is common for various types of information and services to be disseminated on different sites, especially in web 2.0. Since ‘information’ has become a prime tool for organizations to achieve their vision and mission, a high level of quality of service (QoS) is mandatory to disseminate and access information and services over remote sites, despite an unsecure communication system. These systems are expected to have security mechanisms in place, render services within an acceptable response time, dynamically adapt to environmental requirements, and secure key information. This research article proposes a framework for evaluating and determining a threshold up to which distributed systems can collect data to adapt to the environment. The study also proposes a dynamic security metric to determine the level of security disturbance caused by the monitoring system for adaptation and the measures to be implemented. Additionally, the paper details the role of the monitoring system in safeguarding the adaptive distributed system and proposes an adaptive monitoring system that can modify its functionality as per the environment.
Backstepping approach for the control of the double-fed asynchronous generator in a wind power system Chahboun, Mbarek; Abouyaakoub, Mohcine; Ali, Ali Ait; El Mrabet, Aziz; Hihi, Hicham; Ouabi, Hassan; El Bid, Youssef
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.pp78-89

Abstract

This paper aims to model and control the dual-fed asynchronous generator (DFIG). The modeling and vector control were simulated using MATLAB, followed by the application of the Backstepping control strategy. A comparative study between two DFIG control strategies, fuzzy logic control (FLC) and Back-stepping control, was conducted. The results for the Backstepping approach are discussed and compared with FLC, highlighting that the Backstepping technique addresses robustness issues regarding variations in operating conditions and internal parameters. Both control strategies are applied to a wind turbine system, and the simulation results and robustness tests are analyzed.
EMSPLA for accurate feature molecular extraction from protein-ligand interactions Kulkarni V, Srinidhi; Dhandapani, Ganesh; Ramesh, Kureeckal V.
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.pp580-589

Abstract

Protein-ligand interactions are fundamental in various biological and medical fields, influencing drug discovery and therapeutic development. In recent years, deep learning (DL) has revolutionized the study of these interactions, but significant challenges remain in accurately representing molecular structures for DL models. Traditional featurization techniques often depend on handcrafted features, requiring expert knowledge and potentially missing crucial molecular aspects. This work addresses these challenges by developing and evaluating a novel protein-ligand feature extraction system using an enhanced molecular similarity protein-ligand aligner (EMSPLA). The primary objective is to leverage EMSPLA for similarity matching in protein-ligand interactions, improving predictive model accuracy. The methodology combines convolutional neural networks (CNN) for local feature extraction with an attention module to capture long-distance dependencies, enhancing binding site predictions. Using the PDBbind v.2020 dataset, the EMSPLA model demonstrated superior performance with a root mean square error (RMSE) of 0.67, surpassing current state-of-the-art models. These findings highlight the system’s potential for efficient deployment and scalability, positioning it as a powerful tool in computational biology and drug discovery, ultimately advancing our understanding of protein-ligand interactions.
Implementing zero-knowledge proof authentication on Hyperledger fabric to enhance patient privacy and access control Joshi, Praveena Bolly; Natesan, Arivazhagan
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.pp498-506

Abstract

In recent years, the healthcare sector has encountered significant challenges in authenticating identities for online medical services. A predominant reliance on centralized identity management systems (IDMs) has presented obstacles to the seamless exchange of patient identities among various healthcare institutions, often resulting in data isolation within individual silos. Of paramount concern are the potential privacy breaches associated with centralized IDMs, which may compromise patient confidentiality. In response to these challenges, we propose a novel approach to securely sharing patient details across multiple hospitals utilizing the zero-knowledge access protocol (MediCrypt-ZKAP) within the Hyperledger Fabric blockchain framework. By adopting MediCrypt-ZKAP, hospitals can effectively verify the identities of requesting entities without disclosing sensitive patient information, thereby ensuring the highest levels of confidentiality and privacy protection. The proposed system represents a proactive step towards addressing the critical need for secure and interoperable patient data exchange within the healthcare sector. Through the integration of MediCrypt-ZKAP into existing blockchain infrastructure, our solution aims to enhance data security and privacy while promoting seamless collaboration among healthcare institutions.

Page 4 of 7 | Total Record : 66

 2   3   4   5   6 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 41, No 2: February 2026 Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue