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 9,138 Documents
 869   870   871   872   873 
Performance of rocket data communication system using wire rope isolator on sounding rocket RX Rahardiyanti, Kandi; Laksono, Shandi Prio; Hakim, Khaula Nurul; Nugroho, Yuniarto Wimbo; Adi, Andreas Prasetya; Salman, Salman; Kurdianto, Kurdianto
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp783-793

Abstract

The rocket experiment (RX) ballistic rocket requires a reliable data communication system capable of withstanding intense vibrations and shocks during flight. This study investigates the application of wire rope isolators (WRI) to damper mechanical disturbances and protect the rocket's communication system. Installation of WRI position and direction in this experiment with compression position. A series of vibration tests were conducted using 4 WRI installed in the rocket’s 30 kg data communication compartment, vibration test results frequency between 4 Hz and 1500 Hz with acceleration of 8.37 g to 20.37 g, higher "g" readings on the test object sensor compared to vibration machine readings are usually caused by phenomena such as resonance, differences in dynamic response, non-linear behavior, sensor placement location, and swing effects when the vibration machine oscillates. This is a natural mechanical response to external vibrations during testing. While the results of flight tests rocket RX has an acceleration of 8 g to 9.3 g. The results showed that the WRI dampers are effective in protecting the data communication system and ensuring the uninterrupted transmission of flight data to the ground control station (GCS).
Boosting stroke prediction with ensemble learning on imbalanced healthcare data Labaybi, Outmane; Taj, Mohamed Bennani; El Fahssi, Khalid; El Garouani, Said; Lamrini, Mohamed; El Far, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1137-1148

Abstract

Detecting strokes at the early day is crucial for preventing health issues and potentially saving lives. Predicting strokes accurately can be challenging, especially when working with unbalanced healthcare datasets. In this article, we suggest a thorough method combining machine learning (ML) algorithms and ensemble learning techniques to improve the accuracy of predicting strokes. Our approach includes using preprocessing methods for tackling imbalanced data, feature engineering for extracting key information, and utilizing different ML algorithms such as random forests (RF), decision trees (DT), and gradient boosting (GBoost) classifiers. Through the utilization of ensemble learning, we amalgamate the advantages of various models in order to generate stronger and more reliable predictions. By conducting thorough tests and assessments on a variety of datasets, we demonstrate the efficacy of our approach in addressing the imbalanced stroke datasets and greatly enhances prediction accuracy. We conducted comprehensive testing and validation to ensure the reliability and applicability of our method, improving the accuracy of stroke prediction and supporting healthcare planning and resource allocation strategies.
S-commerce: competition drives action through small medium enterprise top management Sutomo, Erwin; Abdul Rahman, Nur Shamsiah; Romli, Awanis
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1042-1050

Abstract

This study investigates the factors influencing the continued use of S-commerce in small and medium enterprises (SMEs), focusing on the roles of top management (TM) support, competitive pressure (CP), facilitating conditions, and service quality. Data were collected from 341 SME owners and analyzed using SEM. Data was analyzed with SmartPLS using a two-step approach. The findings indicate that TM support significantly impacts the continued use of S-commerce by influencing facilitating conditions and service quality while CP affects TM behavior and usage continuity. However, the findings reveal that operational factors, such as infrastructure and service quality, play a more critical role in sustaining S-commerce engagement than external pressures. Facilitating conditions, in particular, were found to have a strong influence on service quality and platform engagement, underscoring the importance of technical and organizational resources. The study extends prior research by highlighting the interplay between internal and external drivers in fostering the continuous use of S-commerce, offering practical insights for SMEs and future research directions.
A variant of particle swarm optimization in cloud computing environment for scheduling workflow applications Tripathi, Ashish; Singh, Rajnesh; Moudgil, Suveg; Gupta, Pragati; Sondhi, Nitin; Kumar, Tarun; Srivastava, Arun Pratap
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1392-1401

Abstract

Cloud computing offers on-demand access to shared resources, with user costs based on resource usage and execution time. To attract users, cloud providers need efficient schedulers that minimize these costs. Achieving cost minimization is challenging due to the need to consider both execution and data transfer costs. Existing scheduling techniques often fail to balance these costs effectively. This study proposes a variant of the particle swarm optimization algorithm (VPSO) for scheduling workflow applications in a cloud computing environment. The approach aims to reduce both execution and communication costs. We compared VPSO with several PSO variants, including Inertia-weighted PSO, gaussian disturbed particle swarm optimization (GDPSO), dynamic-PSO, and dynamic adaptive particle swarm optimization with self-supervised learning (DAPSO-SSL). Results indicate that VPSO generally offers significant cost reductions and efficient workload distribution across resources, although there are specific scenarios where other algorithms perform better. VPSO provides a robust and cost-effective solution for cloud workflow scheduling, enhancing task-resource mapping and reducing costs compared to existing methods. Future research will explore further enhancements and additional PSO variants to optimize cloud resource management.
Detection of COVID-19 based on cough sound and accompanying symptom using LightGBM algorithm Wiharto, Wiharto; Abdurrahman, Annas; Salamah, Umi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp940-949

Abstract

Coronavirus disease 19 (COVID-19) is an infectious disease whose diagnosis is carried out using antigen-antibody tests and reverse transcription polymerase chain reaction (RT-PCR). Apart from these two methods, several alternative early detection methods using machine learning have been developed. However, it still has limitations in accessibility, is invasive, and its implementation involves many parties, which could potentially even increase the risk of spreading COVID-19. Therefore, this research aims to develop an alternative early detection method that is non-invasive by utilizing the LightGBM algorithm to detect COVID-19 based on the results of feature extraction from cough sounds and accompanying symptoms that can be identified independently. This research uses cough sound samples and symptom data from the Coswara dataset, and cough sound’s features were extracted using the log mel-spectrogram, mel frequency cepstrum coefficient (MFCC), chroma, zero crossing rate (ZCR), and root mean square (RMS) methods. Next, the cough sound features are combined with symptom data to train the LightGBM. The model trained using cough sound features and patient symptoms obtained the best performance with 95.61% accuracy, 93.33% area under curve (AUC), 88.74% sensitivity, 97.91% specificity, 93.17% positive prediction value (PPV), and 96.33% negative prediction value (NPV). It can be concluded that the trained model has excellent classification capabilities based on the AUC values obtained.
Trends in machine learning for predicting personality disorder: a bibliometric analysis Sulistiani, Heni; Syarif, Admi; Warsito, Warsito; Berawi, Khairun Nisa
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1299-1307

Abstract

Over the last decade, research on artificial intelligence (AI) in the medical field has increased. However, unlike other disciplines, AI in personality disorders is still in the minority. For this reason, we conduct a map research using bibliometric and build a visualization map using VOSviewer in AI to predict personality disorders. We conducted a literature review using the systematic literature review (SLR) method, consisting of three stages: planning, implementation, and reporting. The evaluation involved 22 scientific articles on AI in predicting personality disorders indexed by Scopus Quartile Q1–Q4 from the Google Scholar database during the last five years, from 2018–2023. In the meantime, the results of bibliometric analysis have led to the discovery of information about the most productive publishers, the evolution of scientific articles, and the quantity of citations. In addition, VOSviewer’s visualization of the most frequently occurring terms in abstracts and titles has made it easier for researchers to find novel and infrequently studied subjects in AI on personality disorders.
Assessment of cloud-free normalized difference vegetation index data for land monitoring in Indonesia Hadiyanto, Ahmad Luthfi; Sukristiyanti, Sukristiyanti; Hidayat, Arif; Pratiwi, Indri
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp845-853

Abstract

Continuous land monitoring in Indonesia using optical remote sensing satellites is difficult due to frequent clouds. Therefore, we studied the feasibility of monthly land monitoring during the second half of 2019, using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from Terra and Aqua satellites. We divide the Indonesian area into seven regions (Sumatra, Java, Kalimantan, Sulawesi, Nusa Tenggara, Maluku, and Papua) and examine NDVI data for each of the regions. We also calculated the cloud occurrence percentage every hour using Himawari-8 data to compare cloud conditions at different acquisition times. This research shows that Terra satellite provides more cloud-free pixels than Aqua while combining data from both significantly increase the cloud-free NDVI pixels. Monthly monitoring is feasible in most regions because the cloudy areas are less than 10%. However, in Sumatra, the cloudy area was more than 10% in October 2019. We need to include further data processing to improve the feasibility of continuous monitoring in Sumatra. This research concludes that monthly monitoring is still feasible in Indonesia, although some data require further processing. The use of additional data from other satellites in the monitoring can be an option for further research.
Optimizing cloud tasks scheduling based on the hybridization of darts game hypothesis and beluga whale optimization technique Chhabra, Manish; E., Rajesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1195-1207

Abstract

This paper presents the hybridization of two metaheuristic algorithms which belongs to different categories, for optimizing the tasks scheduling in cloud environment. Hybridization of a game-based metaheuristic algorithm namely, darts game optimizer (DGO), with a swarm-based metaheuristic algorithm namely, beluga whale optimization (BWO), yields to the evolution of a new algorithm known as “hybrid darts game hypothesis – beluga whale optimization” (hybrid DGH-BWO) algorithm. Task scheduling optimization in cloud environment is a critical process and is determined as a non-deterministic polynomial (NP)-hard problem. Metaheuristic techniques are high-level optimization algorithms, designed to solve a wide range of complex, optimization problems. In the hybridization of DGO and BWO metaheuristic algorithms, expedition and convergence capabilities of both algorithms are combined together, and this enhances the chances of finding the higher-quality solutions compared to using a single algorithm alone. Other benefits of the proposed algorithm: increased overall efficiency, as “hybrid DGH-BWO” algorithm can exploit the complementary strengths of both DGO and BWO algorithms to converge to optimal solutions more quickly. Wide range of diversity is also introduced in the search space and this helps in avoiding getting trapped in local optima.
High-efficiency multimode charging interface for Li-Ion battery with renewable energy sources in 180 nm CMOS Mamouni, Hajjar; El Khadiri, Karim; El Affar, Anass; Jamil, Mohammed Ouazzani; Qjidaa, Hassan
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp744-754

Abstract

The high-efficiency multi-source Lithium-Ion battery charger with multiple renewable energy sources described in the present paper is based on supply voltage management and a variable current source. The goal of charging the battery in a constant current (CC) mode and controlling the supply voltage of the charging circuit are both made achievable using a variable current source, which may improve the battery charger’s energy efficiency. The battery must be charged with a degraded current by switching from the CC state for the constant voltage (CV) state to prevent harming the Li-Ion battery. The Cadence Virtuoso simulator was utilized to obtain simulation results for the charging circuit, which is constructed in 0.18 μm CMOS technology. The simulation results obtained using the Cadence Virtuoso simulator, provide a holding current trickle charge (TC) of approximately 250 mA, a maximum charging current (LC) of approximately 1.3 A and a maximum battery voltage of 4.2 V, and takes only 29 minutes to charge.
A GRU-based approach for botnet detection using deep learning technique G., Suchetha; K., Pushpalatha
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1098-1105

Abstract

The increasing volume of network traffic data exchanged among interconnected devices on the internet of things (IoT) poses a significant challenge for conventional intrusion detection systems (IDS), especially in the face of evolving and unpredictable security threats. It is crucial to develop adaptive and effective IDS for IoT to mitigate false alarms and ensure high detection accuracy, particularly with the surge in botnet attacks. These attacks have the potential to turn seemingly harmless devices into zombies, generating malicious traffic that disrupts network operations. This paper introduces a novel approach to IoT intrusion detection, leveraging machine learning techniques and the extensive UNSW-NB15 dataset. Our primary focus lies in designing, implementing, and evaluating machine learning (ML) models, including K-nearest neighbors (KNN), random forest (RF), long short-term memory (LSTM), and gated recurrent unit (GRU), against prevalent botnet attacks. The successful testing against prominent Bot- net attacks using a dedicated dataset further validates its potential for enhancing intrusion detection accuracy in dynamic and evolving IoT landscapes.

Page 871 of 914 | Total Record : 9138

 869   870   871   872   873 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue 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