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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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Smart enterprise architecture framework for developing patent office Prihastomo, Yoga; Prabowo, Harjanto; Trisetyarso, Agung; Soeparno, Haryono
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.pp681-690

Abstract

Technology and communication’s impact on daily life makes innovation vital for economic growth, highlighting prizing intellectual property (IP) asset protection and management. Patent office, pivotal custodians of legal frameworks and repositories of IP assets, grapples with significant challenges, and backlogs stemming from escalating patent applications and outdated processes. Patent office encounters the challenge of balancing innovation and IP protection because of the convergence of rapid advancements in technologies, for instance, AI, and blockchain. This research employs a design science research methodology to generate a tailored framework addressing these multifaceted challenges. The proposed smart enterprise architecture (SEA) framework offers a strategic, multidimensional approach to modernizing the patent office. It integrates principles from enterprise architecture, information systems management, and IP law, emphasizing efficiency, scalability, and security. The framework leverages the quadruple helix model, fostering collaboration between government, industry, academia, and civil society to enhance stakeholder engagement and innovation ecosystems. Optimizing patent office functions and adapting to IP management’s evolution, the SEA framework integrates technology and organizational goals for a comprehensive approach.
Predictive modeling of electric vehicle loads through driving behavior analysis Mishra, Debani Prasad; Pradhan, Rudranarayan; Singh, Saksham; Singh, Anurag; Kumar, Ayush; Salkuti, Surender Reddy
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.pp1431-1439

Abstract

Electric vehicles (EVs) can potentially be integrated into microgrids via vehicle-to-grid (V2G) technology, which enhances the energy system's stability and durability. This paper provides an in-depth examination and evaluation of V2G integration in microgrid systems. It analyses the present state of research as well as possible uses, challenges, and directions for V2G technology in the future. This article addresses the technological, economic, and regulatory aspects of implementing V2G and provides case studies and pilot projects to shed light on potential benefits and barriers associated with its adoption. The research highlights how V2G contributes to more efficient integration of renewable energy sources, grid stabilization, and cost savings for EV owners. It also addresses the latest developments in technology and proposed laws aimed at encouraging growing applications of V2G.
Vulnerability detection in smart contact using chaos optimization-based DL model Vaddadi, Srinivas A; Somanathan Pillai, Sanjaikanth E Vadakkethil; Vallabhaneni, Rohith; Addula, Santosh Reddy; Ananthan, Bhuvanesh
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.pp1793-1803

Abstract

This research article introduces a deep learning (DL) for identifying vulnerabilities in the smart contracts, leveraging an optimized DL method. The proposed method, termed LogT BiLSTM, combines bidirectional long short-term memory (BiLSTM) with logistic chaos Tasmanian devil optimization (LogT) for enhancing detection of vulnerability. The evaluation of the suggested approach is conducted using publicly available datasets. Initially, preprocessing steps involve removing duplicate data and imputing missing data. Subsequently, the vulnerability detection process utilizes BiLSTM, with the optimization of the loss function achieved through LogT. Results indicate promising performance in identifying vulnerabilities in SC, highlighting the efficacy of the LogT-BiLSTM approach.
New technic of transfer learning for detecting epilepsy by EfficientNet and DarkNet models Edderbali, Fatima; El Malali, Hamid; Essoukaki, Elmaati; Harmouchi, Mohammed
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.pp345-352

Abstract

Epileptic seizures are one of the most prevalent brain disorders in the world. Electroencephalography (EEG) signal analysis is used to distinguish between normal and epileptic brain activity. To date, automatic diagnosis remains a highly relevant and significant research topic which can help in this task, especially considering that such diagnosis requires a significant amount of time to be carried out by an expert. As a result, the need for an effective seizure approach capable to classify the normal and epileptic brain signal automatically is crucial. In this perspective, this work proposes a deep neural network approach using transfer learning to classify spectrogram images that have been extracted from EEG signals. Initially, spectrogram images have been extracted and used as input to pre-trained models, and a second refinement is performed on certain feature extraction layers that were previously frozen. The EfficientNet and DarkNet networks are used. To overcome the lack of data, data augmentation was also carried out. The proposed work performed excellently, as assessed by multiple metrics, such as the 0.99 accuracy achieved with EfficientNet combined with a support vector machine (SVM) classifier.
Advanced generalized integrator based phase lock loop under complex grid condition: a comparative analysis Tripathy, Poonam; Misra, Banishree; Nayak, Byamakesh
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.pp23-32

Abstract

Integration of renewable energy systems (RESs) to the grid leads to various power quality issues. A proper control approach for the interfaced inverter is required to mitigate the uncertainties caused in the grid due to the RESs association to maintain the grid stability. The presence of harmonics and DC offset in the input grid voltage of a phase lock loop (PLL) leads to inaccurate phase estimation due to fundamental frequency oscillations. Though many advanced generalized integrator (GI) based PLLs have been developed still there is a need for a robust PLL for synchronization with faster dynamic response, both the harmonics and DC offset rejection ability with precise estimation. This paper proposes some simple yet effective advanced PLLs employing low pass filters (LPFs) in the existing GI based PLLs for faster and accurate phase angle estimation for seamless synchronization under complex grid circumstances. These advanced generalized integrators with LPFs (GI-LPF) based PLLs will provide enhanced and robust synchronization for the grid integrated RESs thereby addressing multiple power quality issues like voltage unbalance, harmonics and DC offsets. The simulation based comparative analysis of the proposed controllers confirm their effective disturbance rejection capability under complex grid conditions by providing advanced and precise response.
Optimization of single electron transistor based digital logic design Gopnarayan, Shobhika Pankaj; Markande, Shriram D.; Raut, Vaishali P
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.pp1553-1563

Abstract

This paper addresses the challenge of high-power consumption and delay in conventional complementary metal-oxide-semiconductor (CMOS) circuits, particularly in the design of digital logic gates. The objective is to develop a hybrid CMOS-single-electron transistor (SET) model that reduces power consumption while maintaining acceptable performance in terms of delay. The proposed model leverages coulomb oscillation in SETs to create a changeable transconductance area, which significantly reduces energy usage. Simulation results demonstrates that the hybrid CMOS-SET circuits achieve up to 30% lower power dissipation compared to traditional CMOS designs, although a slight increase in delay is observed in complex gates like the OR gate. The novelty of this work lies in its use of coulomb oscillation for dynamic transconductance control, providing an innovative approach to balancing power efficiency and speed in nano-scale digital circuits. This makes the proposed model a promising candidate for future low-power, high-performance integrated circuits.
Segmentation and classification of plant leaf disease using advanced deep learning approach and ensemble classifier Huddar, Suma S.; Rudagi, Jayashri; Jakati, Jagadish S.
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.pp1489-1502

Abstract

An essential component of maintaining global food production is plants. On other hand, a number of plant diseases can threaten agricultural output and cause large losses if left unchecked. Agricultural specialists and botanists physically track plant diseases in a labor-intensive, error-prone manner using a conventional method. AI can give evaluations that are quicker and more accurate than those made using conventional approaches by automating the identification and analysis of diseases. This technical development presents a viable way to lessen crop losses and lessen the severity of infections. As a result, we describe an ensemble machine learning strategy for plant disease classification in this study that is enabled by deep learning. Data augmentation is done in the first part of the study, and in the second step, we provide a modified Mask R-CNN model for plant leaf segmentation. Afterwards, a model to extract the deep features based on CNN is shown. Lastly, the ensemble classifier is built using support vector machine classifier (SVM), random forest (RF), and decision tree (DT) with the aid of majority voting. The suggested method's effectiveness is tested on plant village, apple, maize, and rice, yielding overall accuracy values of 99.45%, 96.30%, 96.85%, and 98.25%, in that order.
An approach for loss minimization and capacity savings in residential microgrid networks in Oman Sreedharan, Sasidharan; Solanki, Parmal Singh; Abdelfatah, Magdy S.; Wartana, I Made
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.pp1-10

Abstract

In this paper, an approach for end-user-based energy saving and loss reduction technique in residential networks has been proposed. The proposed approach is applied to an Oman case study of community microgrid networks by connecting automatically switched capacitors to improve power factor and analyzed for capacity saving and loss minimization. The proposed approach can reduce the cost of electrical bills in the total community microgrid by minimizing losses and the capacity investment cost saving of all equipments in the transmission and distribution line from the generation to the end user. In addition, this study focuses on the healthy conclusion that average kVA capacity could be saved to an extent of 12.22% and in economic terms, approximately USD 3.68 per hour in the microgrid. This proposed technique can be implemented as a model community project for other similar residential community systems.
Web GIS-based postcode alternative system for resolving “last mile” problem in Jordan’s home delivery Omar, Firas; Nabot, Ahmad; Sowan, Bilal
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.pp531-544

Abstract

As more and more people shop online, the postal code system must be more dependable. Due to the absence of a comprehensive postcode system, online purchases and shipping in the developing country of Jordan are complicated. This research paper proposes an alternative delivery system for delivering online purchases to customers without postal codes. Smartphone and computer-based geographic information system (GIS) applications evaluated in Jordan. The scientists found that the users were eager to adopt the system based on its ease of use and adoption rate. A questionnaire survey was distributed to 167 retail stores, delivery logistics employees, university students, and academics. The data collected were then analyzed using SPSS techniques such as POST HOC and ANOVA. To find a home delivery solution, we tested the suggested system app on both desktop and Smartphone platforms. The findings show that it is easier to locate a residential neighborhood. Customer trust and satisfaction with online purchases should increase due to the additional benefits of the system installation. Improve the effectiveness of home delivery services in Jordan with the use of artificial intelligence (AI). Both customers and stores prefer this system for online shopping rather than using postcodes. According to these data, experts can enhance their items by implementing digital sales strategies.
A comprehensive access control model integrating zero trust architecture Jyosthna, Pattabhi Mary; Reddy, Konala Thammi
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.pp1896-1904

Abstract

In contemporary IT landscapes, trust in entities, whether internal or external, within organizations has become obsolete. Establishing and enforcing strict access controls, alongside continuous verification, is imperative to safeguard organizational resources from potential insider and outsider threats. The emergence of zero trust architecture (ZTA) addresses this need by advocating for a paradigm shift in security. This research proposes a comprehensive access control model aligned with the fundamental ZTA security principles, namely least privilege, conditional access, and continuous monitoring. The model integrates well-established access control paradigms, including role-based access control (RBAC) to uphold the least privilege principle, attribute-based access control (ABAC) to support conditional access, and trust-based access control (TBAC) to enable continuous monitoring. To determine the trust level of a user requesting access, an analysis of the user's log activities is conducted using the Nmedian outlier detection (NMOD) technique. This analysis aids in evaluating the trustworthiness of the user seeking access to resources. Furthermore, this research assesses the efficiency and efficacy of the proposed integrated access control model in comparison to existing access control models, primarily focusing on their respective functionalities.

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