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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
 901   902   903   904   905 
Impact of artificial light color on microgreen green spinach growth in an IoT-controlled environment Ihsan, Fadhil Azmi; Fitrianah, Devi
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.pp619-628

Abstract

This study investigates the effect of different artificial colors red-blue and white on the growth of green spinach microgreens under an internet of things (IoT) based controlled environment and integrated sensors: DHT22 for temperature and humidity, and YL-69 for soil moisture. The experiment compared plant growth in two lighting scenarios over 10 days evaluating parameters including plant height and number of leaves. Results indicate that spinach microgreens grown under red-blue LED light achieved a slightly higher average height of 4.6cm and more leaves of 50 compared to white LED light with an average height of 4.5cm and 36 leaves. Although the difference between the two lighting conditions appears minor, a t-test was conducted to determine statistical significance. The results show that the difference in the number of leaves is statistically significant, suggesting that morphological responses particularly leaf growth take precedence over vertical steam elongation as an adaptive strategy to optimize environmental conditions.
Deep-learning-based hand gestures recognition applications for game controls Ngo, Huu-Huy; Le, Hung Linh; Tuyen, Man Ba; Dung, Vu Dinh; Thanh, Tran Xuan
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.pp883-897

Abstract

Hand gesture recognition is among the emerging technologies of human computer interaction, and an intuitive and natural interface is more preferable for such applications than a total solution. It is also widely used in multimedia applications. In this paper, a deep learning-based hand gesture recognition sys tem for controlling games is presented, showcasing its significant contributions toward advancing the frontier of natural and intuitive human-computer interac tion. It utilizes MediaPipe to get real-time skeletal information of hand land marks and translates the gestures of the user into smooth control signals through an optimized artificial neural network (ANN) that is tailored for reduced com putational expenses and quicker inference. The proposed model, which was trained on a carefully selected dataset of four gesture classes under different lighting and viewing conditions, shows very good generalization performance and robustness. It gives a recognition rate of 99.92% with much fewer param eters than deeper models such as ResNet50 and VGG16. By achieving high accuracy, computational speed, and low latency, this work addresses some of the most important challenges in gesture recognition and opens the way for new applications in gaming, virtual reality, and other interactive fields.
Improved YOLOv8 for rail squat detection and identification Do, Van-Dinh; Nguyen, Phuong-Ty; Ha, Minh-Tuan
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.pp1129-1140

Abstract

Rail transport plays a vital part in the country's economy by ensuring the safe movement of both goods and passengers. Therefore, maintaining rail safety through consistent surface defect inspection is extremely importan. However, squat defect detection on rail surfaces faces considerable difficulties due to weather impacts, lighting changes, and variations in image contrast. These challenges hinder the accuracy and reliability of traditional inspection methods. To solve this problem, this study proposes an improved YOLOv8 model for the identification and classification of squat defects. The methodology involves capturing images of the rail track, preprocessing them to enhance image quality, labeling squat defects for training purposes, and training the proposed model using the labeled dataset. The improved YOLOv8 model incorporates enhancements such as multi-scale convolution modules and attention mechanisms to improve feature extraction and defect recognition. Experimental results demonstrate the effectiveness of the proposed method, achieving an impressive accuracy of 0.92 in detecting and categorizing squat defects. These findings highlight the potential of the proposed approach to enhance railway safety by providing a reliable and efficient solution for rail surface inspection.
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.

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