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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 65 Documents
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Search results for , issue "Vol 39, No 1: July 2025" : 65 Documents clear
A sentiment analysis on skewed product reviews: Ben & Jerry's ice cream Dewi, Nabilla Nurulita; Amalia Utami, Sekar Gesti; Adiar, Shalsabila Aura; Cahyono, Hasan Dwi
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.pp364-373

Abstract

Sentiment analysis of product reviews offers valuable insights into consumer perspectives, which can inform product development and marketing strategies. Given the growing importance of user-generated content like product reviews, this study explored sentiment classification in online reviews of Ben & Jerry's ice cream. We designed and evaluated three machine learning algorithms for sentiment classification: Naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM). The dataset exhibited a significant class imbalance, with substantially more positive than negative reviews. We employed two oversampling techniques: the synthetic minority oversampling technique (SMOTE) and the adaptive synthetic sampling approach (ADASYN). With the original skewed data, NB, LR, and SVM achieved accuracies of 91.90%, 93.77%, and 95.09%, respectively. While SMOTE did not improve performance in some scenarios, ADASYN yielded positive results and generally enhanced model reliability across all algorithms. Post-balancing with ADASYN, the sentiment distribution became less skewed, and accuracies shifted to 92.04% for NB, 94.96% for LR, and 95.23% for SVM. The combination of SVM and ADASYN demonstrated promising results, suggesting this approach may offer robust and efficient performance for binary sentiment classification, especially with imbalanced datasets.
Phasor measurement unit optimization in smart grids using artificial neural network Shiralkar, Ashpana; Ingle, Suchita; Kulkarni, Haripriya; Mane, Poonam; Bakre, Shashikant
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.pp625-633

Abstract

The wide area measurements systems (WAMS) play a vital role in the operation of smart grids. The phasor measurement units (PMU) or synchrophasors are one of the principle components under WAMS. PMU in a smart grid converts power system signals into phasor from voltage and current which enhances the observability of the power system. A variety of operations is performed by the PMUs such as adaptive relaying, instability prediction, state estimation, improved control, fault and disturbance recording, transmission and generation modeling verification, wide area protection and detection of fault location. The PMUs can improve the performance of grid operations and monitoring. Thus, PMU optimization is very necessary to achieve the desired power system observability. The performance of the PMUs can be optimized using artificial intelligence (AI) technologies. The novice method of monitoring maximum power transfer using PMUs equipped with artificial neural networks has been discussed in this paper. In this paper, a two-bus system model is developed that can be generalized to multiple bus systems. The proposed method is novel, simple, feasible, and cost effective for smart grids.
Comparative study of deep learning approaches for cucumber disease classification Shivaraj, Supreetha; Haladappa, Manjula Sunkadakatte
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.pp554-563

Abstract

Cucumber leaf diseases, such as downy mildew and leaf miner, pose significant challenges to crop yield and quality. Accurate and timely detection is essential to efficient management. The current research assesses seven convolutional neural network (CNN) models for the classification of diseases of cucumber leaves: DenseNet121, InceptionV3, ResNet50V2, VGG16, Xception, MobileNetV2, and NASNet. The dataset includes images from the cucumber disease recognition dataset (Mendeley) and 500 real-time images captured between December 2022 and February 2023 in Karnataka, covering varied lighting conditions. After augmentation, the dataset is divided into testing, validation, and training sets and includes 804 leaf miner, 807 downy mildew, and 804 healthy images. With an overall test accuracy of 99.37% and nearly flawless precision, recall, and F1-scores in every class, ResNet50V2 showed exceptional performance. InceptionV3 and MobileNetV2 also exhibited strong performance with accuracies of 97.29% and 97.70%, respectively. DenseNet121, VGG16, Xception, and NASNet performed well but were slightly outperformed by the top models. The findings indicate ResNet50V2 as the most reliable model for cucumber leaf disease classification, providing a robust foundation for developing automated disease detection systems. This work demonstrates how precise disease detection using deep learning models can improve agricultural management.
Web-Based Attacks Detection Using Deep Learning Techniques: A Comprehensive Review Alghofaili, Lujain Nasser; Ibrahim, Dina M.
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.pp466-484

Abstract

Web applications are utilized extensively by a broad user base, and the services provided by these applications assist enterprises in enhancing the quality of their service operations as well as increasing their revenue or resources. To gain control of web servers, attackers will frequently attempt to modify the web requests that are sent by users from web applications. Attacks that are based on the web can be detected to help avoid the manipulation of web applications. In addition, a variety of research has offered many methods, one of which is artificial intelligence (AI), which is the method that has been utilized the most frequently to identify web-based attacks recently. When it comes to the protection of web applications, anomaly detection techniques used by intrusion prevention systems are preferred.  Deep learning, often known as DL, is going to be covered in this paper as anomaly-based web attack detection methods and machine learning techniques. With the purpose of organizing our selected techniques into a comprehensive framework that encourages future studies, we first explained the most concepts that related to web-based attacks detection, then we moved on to discuss the most prevalent web risks and may provide inherent difficulties for keeping web applications safe.  We classify previous studies on detecting web attacks into two categories: deep learning and machine learning. Lastly, we go over the features of the previously utilized datasets in summary form.
Enhancing urban cyclist safety through integrated smart backpack system Gómez, Sergio; Mejía, Daniel; Martínez, Fredy
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.pp118-130

Abstract

The increasing adoption of bicycles as a sustainable mode of urban transportation has underscored the urgent need for enhanced safety measures for cyclists. This paper presents the development and implementation of an integrated smart backpack system designed to improve the safety and visibility of urban cyclists. The system leverages advanced technologies, including the ESP32 microcontroller, GPS modules, proximity sensors, and LED lighting, to create a semiautomatic solution that adapts to environmental conditions and cyclist behavior in real-time. Extensive testing under various conditions, including low visibility and adverse weather, demonstrated the system’s reliability in enhancing cyclist visibility and reducing accident risks. The smart backpack also features a userfriendly mobile application, providing real-time data on speed, distance, and location, which further contributes to rider safety. The results indicate significant potential for this technology to be widely adopted, offering a practical and effective solution to the growing safety concerns of urban cyclists. This work not only advances the field of wearable safety technologies but also sets the foundation for future innovations in smart transportation systems, contributing to safer and more sustainable urban mobility
Boosting real-time vehicle detection in urban traffic using a novel multi-augmentation Ashari, Imam Ahmad; Syafei, Wahyul Amien; Wibowo, Adi
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.pp656-668

Abstract

Real-time vehicle object detection in urban traffic is crucial for modern traffic management systems. This study focuses on improving the accuracy of vehicle identification and classification in heavy traffic during peak hours, with particular emphasis on challenges such as small object sizes and interference from light reflections. The use of multi-label images enables the simultaneous detection of various vehicle types within a single frame, providing more detailed information about traffic conditions. You only look once (YOLO) was chosen for its capability to perform real-time object detection with high accuracy. Multi-augmentation techniques were applied to enrich the training data, making the model more robust to varying lighting conditions, viewpoints, object occlusions, and issues related to small objects. YOLOv8n and YOLOv9t were selected for their speed and efficiency. Models without augmentation, 10 single-augmentation techniques, and 5 multi-augmentation techniques were tested. The results show that YOLOv8n with multiaugmentation (scaling, zoom in, brightness adjustment, color jitter, and noise injection) achieved the highest mAP50-95 score of 0.536, surpassing YOLOv8n with single-augmentation Blur, which had an mAP50-95 of 0.465, as well as YOLOv8n without augmentation, which scored 0.390. Multiaugmentation proved to significantly enhance YOLO’s performance.
OPT-TMS: a transport management system based on unsupervised clustering algorithms Reguemali, Soufiane; Moussaid, Abdellatif; Elaoudi, Abdelmajid
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.pp425-435

Abstract

Transportation management within modern logistics has become increasingly complex, particularly with the expansion of industrial zones outside urban centers. This paper introduces OPT-TMS, a cutting-edge transportation management system (TMS) designed to optimize employee transportation using advanced machine learning techniques, specifically unsupervised learning and clustering algorithms. OPT-TMS integrates a comprehensive dataset that includes employee locations, entry times, bus capacities, and other critical parameters to enhance resource utilization, reduce costs, and improve overall efficiency. The proposed system follows a systematic workflow encompassing data collection, preparation, and adaptive clustering using the K-means algorithm with constraints. The innovative approach leverages real-time data integration through the open route services (ORS) API to optimize bus routes and collection points. Extensive validation, involving both data verification and physical testing, confirms the system’s accuracy and effectiveness across multiple Moroccan cities, including Casablanca, Kenitra, and Marrakech. The development of OPT-TMS into a user-friendly web application further demonstrates its practical utility, offering decision-makers a dynamic tool for real-time adjustments and efficient transportation management. This paper concludes that OPT-TMS represents a significant advancement in transportation logistics, enhancing both employee satisfaction and operational efficiency through data-driven optimization.
An improved efficientnet-B5 for cucurbit leaf identification Ha, Quang Hung; Hoang, Trong-Minh; Pham, Minh Trien
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.pp336-344

Abstract

Plant diseases significantly impact the quality and productivity of crops, leading to substantial economic losses. This paper introduces two enhanced EfficientNet-B5 architectures, EfficientNetB5-sigca and EfficientNetB5- sigbi, specifically designed to detect and classify diseases in cucurbit leaves. We employ EfficientNet-B5 for feature extraction, using a 456×456×3 input and omitting the top layer to generate feature maps with Swish activation. A global average pooling 2D layer replaces the conventional fully connected layer, producing a flattened vector. This is followed by a dense layer with four output units, L2 regularization, and sigmoid activation, using either categorical or binary cross-entropy as the loss function. We also developed a novel image dataset targeting cucumber and cantaloupe leaves, including 11,425 augmented images categorized into four disease classes: anthracnose, powdery mildew, downy mildew, and fresh leaf. Our experiments dataset demonstrates that the EfficientNetB5-sigbi achieves an accuracy of 97.07%, marking a significant improvement in classifying similar diseases in cucurbit leaves.
Real-time driver drowsiness detection based on integrative approach of deep learning and machine learning model Shankara Chari, Gowrishankar Shiva; Prashant, Jyothi Arcot
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.pp592-602

Abstract

Driver drowsiness is a major factor that contributing to road accidents. Several researches are ongoing to detect driver drowsiness, but they suffer from the complexity and cost of the models. This paper introduces a hybrid artificial intelligence (AI)-driven framework integrating deep learning (DL) and machine learning (ML) models for real-time drowsiness detection. The system utilizes a robust DL model to classify driver states based on facial images and support vector machine (SVM) model is trained to develop a cost-efficient yet robust facial landmark detector to extract key features such as eye aspect ratio (EAR) and mouth aspect ratio (MAR). We also introduce a multi-stage decision fusion mechanism that combines convolutional neural network (CNN) probability scores with EAR/MAR thresholds to enhance detection reliability and reduce false positives. Experimental results demonstrate that the proposed model achieves 98% accuracy and F1-score, significantly outperforming traditional DL approaches. Additionally, the SVM-based landmark predictor shows improved efficiency with lower mean squared error (MSE) without having higher computational requirements.
An efficient segmentation using adaptive radial basis function neural network for tomato and mango plant leaf images Smitha, Jolakula Asoka; Shadaksharappa, Bichagal; Parvathy, Sheela; Veena, Kilingar; Jenifer, Albert; Nirmala, Baddala Vijaya; Murugan, Subbiah
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.pp202-213

Abstract

Agriculture has become simply to feed ever-growing populations. The tomato is arguably the most well-known vegetable in agricultural areas and plays a significant role in the growth of vegetables in our daily lives. However, because this tomato has multiple diseases, image segmentation of the diseased leaf shows a key role in classifying the disease by the leaf's symptoms. Therefore, in this paper, an efficient plant disease segmentation using an adaptive radial basis function neural network (ARBFNN) classifier. The proposed radial basis function (RBF) neural network is enhanced by using the flower pollination algorithm (FPA). Firstly, the noise is detached by an adaptive median filter and histogram equalization. Then, from every leaf image, different kind of color features is extracted. After the extraction of features, those are fed to the segmentation phase to section the disease serving from the input image. The efficiency of the suggested method is analyzed based on various metrics and our technique attained a better accuracy of 97.58%.

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