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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 56 Documents
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Search results for , issue "Vol 40, No 2: November 2025" : 56 Documents clear
Hash-based message authentication code with secure hash algorithm-256 for efficient data sharing in blockchain Lingaraju, Naveenkumar; Sunkadakatte, Manjula Haladappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp780-788

Abstract

Recently, cloud servers have increasingly been utilized for storing a large amount of data, which is stored in the form of ciphertext. In a decentralised system, the communication overhead on the network is recognized as the main problem due to the numerous transaction data recorded across the data Sharding and nodes with authorized users. Hash-Based Message Authentication Code with Secure Hash Algorithm-256-bit (HMAC-SHA-256 bit) is proposed for secure and effective data sharing in blockchain to overcome this issue. The secure algorithm HMAC serves for authenticating both the data origin and integrity. That uses a cryptographic hash procedure in combination with a confidential key to validate both the verification and tamper-proof content of a message. HMAC consists of a particular content and an authentication key with a hashing code value. In the Blockchain framework, the HMAC algorithm is utilized with the SHA-256bits to generate and validate the signatures of many transactions. SHA-256 is a hash algorithm that creates a 256-bit cryptographic checksum. The blockchain uses HMAC along with SHA-256bits, which is a safe and clearly expressed algorithm to allocate or convey the data securely. The Authentication of HMAC-SHA-256bits achieves the optimal retrieval times of 0.4s, 1.0s, 1.5s, 1.9s, 2.2s, and 2.8s for file sizes of 50KB, 100KB, 150KB, 200KB, 250KB, and 300KB, correspondingly, when compared to interplanetary file system (IPFS).
Precision in 3D positional forecasting with machine learning and deep temporal architectures Kumar, P. Sirish; Indira Dutt, V. B. S. Srilatha; Oruganti, Sai Kiran
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp601-609

Abstract

We present a comparative analysis of traditional machine learning (ML) models, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), and deep learning (DL) architectures, convolutional neural networks (CNN), and bidirectional long short-term memory (BiLSTM) for high-precision 3D positional forecasting. Conventional approaches often underperform when modeling complex spatiotemporal dependencies, limiting their use in dynamic systems such as robotics and autonomous vehicles. This study highlights BiLSTM's advantage in learning bidirectional temporal features, achieving superior R² scores and stable prediction intervals compared to both classical ML and spatially-focused CNN models. Uncertainty metrics, prediction interval coverage probability (PICP), and mean prediction interval width (MPIW) provide additional insight into model reliability. Experiments on a 22-hour GPS dataset confirm that BiLSTM achieves both high accuracy and predictive confidence, underscoring its suitability for real-world trajectory forecasting.
On-grid vs. off-grid photovoltaic systems for smart greenhouses: a techno-economic case study Simorangkir, Arthur; Halim, Levin
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp545-557

Abstract

Integrating photovoltaic (PV) systems into agricultural applications has gained significant attention as a sustainable energy solution. However, the feasibility of on-grid and off-grid PV systems for smart greenhouse applications in Indonesia remains unclear. This study compares both systems' technical performance, economic viability, and regulatory challenges through simulations and case studies in Lembang, Bandung. The analysis considers solar radiation levels, shading effects, installation costs, energy independence, and long-term operational efficiency. Results indicate that while on-grid systems offer lower initial investment and seamless integration with the utility grid, regulatory constraints and limited capacity approvals pose significant barriers. Despite higher initial costs, off-grid systems provide energy independence and long-term cost benefits by eliminating dependency on grid electricity and avoiding bureaucratic hurdles. The study concludes that off-grid PV systems are a more practical and sustainable solution for smart greenhouse applications in Indonesia, mainly where grid connection processes are complex or unreliable.
Classification of voice pathologies using one dimensional feature vector and two dimensional scalogram Khumukcham, Ranita; Meinam, Sharmila; Nongmeikapam, Kishorjit
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp654-666

Abstract

Most research work focus only on binary classification of voice pathologies such as normal and pathological classification. However, the current work gives importance to multiclass classification too. The paper compares onedimensional (1D) feature vectors based machine learning (ML) techniques and two-dimensional (2D) scalogram image based deep learning (DL) model for binary and multiclass classification of voice pathology. The multiclass classification classifies the voice signal into four categories which are healthy, hyperkinetic dysphonia, hypokinetic dysphonia, and reflux laryngitis. The current work demonstrates the evaluation of 1D feature vectors extracted from speech signal such as MFCC (mel-frequency cepstral coefficient) and pitch with various ML techniques like K-nearest neighbor (KNN), Naïve Bayes, and discriminant analysis (DA). Another technique that uses time-frequency scalograms derived using three different wavelets, i.e., analytical Morlet (amor), Bump, and Morse, are used for training a pretrained GoogleNet architecture, which is a very popular DL model. Experimental results show that 2D scalogram image based DL model for binary (96.05%) and multiclass (89.8%) classification of voice pathology gives better performance while comparing with 1D feature vectors based ML techniques.
Query keyword extraction in discriminative marginalized probabilistic neural method for multi-document summarization Subeno, Bambang; Budi, Indra; Yulianti, Evi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp907-915

Abstract

The large number of textual documents in the medical field makes it very difficult for readers to obtain comprehensive information. Users usually use a query approach to get the desired information. Using the correct query will produce relevant information. In the existing discriminative marginalized probabilistic neural method, referred to as DAMEN, used for multi-document summarization, a background sentence query is used to retrieve the top-K relevant documents and then generate a summary of these documents. However, the background sentence query used to retrieve the top-K documents did not provide accurate summary results. The author improved the DAMEN model by adding a keyword extraction process to the query background sentence. We call this model Q-DAMEN. Our model shows significant improvement over the original DAMEN method, with the best results achieved by the variation of using a keyword query entered into the discriminator component and a background sentence query entered into the generator component. The multipartieRank keyword extraction method shows the best results with a Rouge-1 value of 29.12, Rouge-2 of 0.79, and Rouge-L of 15.53. The results demonstrate that the more accurate the keywords extracted from the sentence background query, the more accurate the multi-document summaries generated.
Intrusion detection system using hybrid CNN-LSTM model in cloud computing Alshehri, Maha Mohammad; Alshehri, Shoog Abdullah; Alajmani, Samah Hazzaa
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp840-849

Abstract

Cloud computing has revolutionized online service delivery with its flexibility and cost efficiency. Nevertheless, the growing importance of stored data makes it a target for cyberattacks, posing security and privacy risks. This calls for effective solutions to safeguard data and infrastructure, particularly with regard to intrusion attacks and distributed attacks such as distributed denial of service (DDoS). Therefore, there is a need to develop an effective intrusion detection system (IDS) using deep learning to ensure the protection of cloud data and infrastructure. In this paper, a hybrid model aims to leverage the power of convolutional neural networks (CNNs) to analyze spatial features and extract complex patterns, while long short-term memory LSTMs are used to understand temporal data sequences and detect attacks that evolve over time to detect intrusions in cloud computing environments on the CSE-CIC-IDS2018 dataset. The model was trained and tested on DDoS attacks, and the results demonstrated high performance in detecting attacks with high accuracy and efficiency. This hybrid model achieved an accuracy of 99.88%, a precision of 99.83%, a recall of 99.94%, and an F1-score of 99.88%.
Enhancing document text classification using hybrid deep contextual and correlation network Shilpa, Shilpa; Soma, Shridevi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1100-1108

Abstract

Document analysis involves the extraction and processing of information from documents, a task increasingly automated through the use of deep learning (DL) technologies. Despite the high predictive power of DL models, their black-box nature poses challenges to transparency and interpretability, hindering their integration into the industry. This paper introduces the hybrid deep contextual and correlation network (HDCCNet), a novel methodology designed to improve both the accuracy and interpretability of multi-category classification tasks. HDCCNet leverages a hybrid layer category correlation module to deepen category connections, thereby enhancing the understanding and prediction of category interrelations. To address potential prediction divergence, residual connections are incorporated, ensuring stable and reliable performance. Furthermore, HDCCNet reduces model parameters, accelerating convergence and making the model more efficient. This efficiency is particularly beneficial for practical applications, allowing faster deployment and scalability. By bridging the gap between DL’s capabilities and industry needs for transparency, HDCCNet provides a robust solution for automated document processing, paving the way for broader adoption of DL technologies in business environments.
Evaluating multilingual encoder models for few-shot named entity recognition tasks Bouabdallaoui, Ibrahim; Guerouate, Fatima; Bouhaddour, Samya; Saadi, Chaimae; Sbihi, Mohammed
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp745-757

Abstract

This work provides a thorough analysis of few-shot learning approaches in the realm of multilingual named entity recognition (NER). Our research is driven by the need to enhance linguistic inclusivity and performance efficiency across diverse languages. We focus on benchmarking a selection of prominent encoder models including XLM-RoBERTa (XLM-R), multilingual BERT (mBERT), DistilBERT, character architecture for eNcoders IN embeddings (CANINE), and multilingual text-to-text transfer transformer (mT5), to illuminate their capabilities and limitations within few-shot learning paradigms, particularly for underrepresented languages. Results indicate that models like XLM-R and mT5 demonstrate superior adaptability and accuracy, outperforming others in complex linguistic settings, which suggests their potential in supporting more inclusive artificial intelligence (AI) technologies. The impact of this study extends beyond academic interest, offering pivotal insights for the development of more inclusive, adaptable and efficient NER systems. By advancing our understanding of few-shot learning in multilingual contexts, this work contributes to the broader goal of creating AI applications that are linguistically diverse and more reflective of global communication patterns. These results provide crucial insights for advancing entity recognition capabilities across diverse artificial intelligence systems, facilitating development of more precise, equitable, and sophisticated linguistic processing frameworks.
A deep learning-integrated proxy model for efficient cryptocurrency payments Kasula, Vinay Kumar; Yadulla, Akhila Reddy; Konda, Bhargavi; Yenugula, Mounica; Ayyamgari, Supraja
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp1023-1039

Abstract

Blockchain technology allows decentralized cryptocurrencies to change digital finances by providing secure, pseudonymous transactions to users. Since blockchain ledgers operate in a public environment, users can face potential privacy risks due to the exposure of their transaction patterns. Conventional cryptocurrency systems use block generation for transaction confirmation, yet this process produces latency and impacts the real-time efficiency of transactions. This paper develops a proxy-assisted cryptocurrency payment system that employs blind signature principles to achieve better system privacy and enhanced speed. The core functionality of this proposed system aims to protect transaction secrecy as it speeds up confirmation processes. A proxy node handles transaction requests through blind signature protocols that guarantee data confidentiality as part of the methodology. The proposed system utilizes deep learning tools, which include recurrent neural networks (RNN), graph neural networks (GNN), and reinforcement learning (RL) to forecast confirmation results, identify scams, and control proxy functions dynamically. Research indicates that the introduced method substantially boosts privacy features, decreases transaction latencies, and enhances the security of all transactions by providing an encouraging roadmap for secure cryptocurrency systems that preserve privacy.
Detection of short circuit faults in two-level voltage source inverter using convolution neural network Aioub, Sai; Zakariya, Belghiti; Lamiaâ, El Menzhi
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp580-589

Abstract

Voltage source inverters (VSIs) play a critical role in modern industrial systems, particularly in controlling the operation of equipment such as induction motors. Ensuring their reliable performance is crucial, as faults like short circuits can severely disrupt industrial processes. This paper introduces a new diagnostic approach for detecting and localizing short circuit faults in VSIs. The method uses Lissajous curves derived from the Clark transformation of the VSI’s 3-phase voltage components (Vα, Vβ). These curves serve as input data for a convolutional neural networks (CNNs) model, enabling the accurate classification of single and double short circuit faults. Simulation results using MATLAB/Simulink demonstrate that the proposed method achieves 100% classification accuracy within 100 ms, highlighting its suitability for real-time applications. The approach offers significant advantages in speed and accuracy over traditional techniques, with potential implications for enhancing the reliability and safety of inverter-driven systems in industrial environments.

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