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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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Ba3GdNa(PO4)3F:Eu2+ phosphor with blue-red emission colors on white-LED properties Dung, Nguyen Van; Quoc Anh, Nguyen Doan
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.pp1564-1571

Abstract

The blue/red-emission phosphor Ba3GdNa(PO4)3F:Eu2+ (BGN(PO)F-Eu) is used in this work for diodes emit white illumination (wLED). The phosphor is prepared using the solid-phase reaction. The suitable concentrations of Eu2+ ion dopant is about 0.7% and 0.9%. The BGN(PO)F-Eu phosphor can provide wLED light with the spectral wavelength in the region of blue (480 nm) and orange-red colors (595-620 nm). With the resulted emissions the phosphor can be appropriate for plant growing because they compatible with absorption spectra of plants’ carotenoids and chlorophylls for stimulating the photosynthesis. The phosphor influences on the wLED lighting properties depending on the doping dosages. It is possible to enhance the luminous intensity of the wLED with higher BGN(PO)F-Eu phosphor amount. Meanwhile, the color properties does not get significant improvements. Thus, the BGN(PO)F-Eu phosphor could be used with other luminescent materials to stimulate the hue rendering performance.
Improving farming by quickly detecting muskmelon plant diseases using advanced ensemble learning and capsule networks Kannan, Deeba; Sundarasrinivasa Sankaranarayanan, Nagamuthu Krishnan; Venkatarajan, Shanmugasundaram; Mahajan, Rashima; Gunasekaran, Brindha; Murugamani, Pandi Maharajan; Dhandapani, Karthikeyan
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.pp2090-2100

Abstract

In modern agriculture, ensuring plant health is essential for high crop yields and quality. Plant diseases pose risks to economies, communities, and the environment, making early and accurate diagnosis crucial. The internet of things (IoT) has revolutionized farming by enabling real-time crop monitoring and using drones and cameras for early disease detection. This technology helps farmers address challenges with precision and sustainability. This research propose an ensemble learning model incorporating multi-class capsule networks (MCCN) and other pre-trained model with majority voting system is implemented to predict plant diseases and pests early. The research aims to develop a robust MCCN-based ensemble prediction model for timely disease identification. To evaluate the performance of the ensemble model, various key metrics, including accuracy, and loss value, are assessed. Furthermore, a comparative analysis is conducted, benchmarking the MCCN model against other well-known pre-trained models such as residual network-101 (ResNet101), visual geometry group-19 (VGG19), and GoogleNet. This research signifies a substantial stride towards the realization of IoT-driven precision agriculture, where advanced technology and machine learning contribute to the early detection and mitigation of plant diseases, ultimately enhancing crop yield and environmental sustainability.
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.
TextBugger: an extended adversarial text attack on NLP-based text classification model Somanathan Pillai, Sanjaikanth E. Vadakkethil; Vaddadi, Srinivas A.; 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.pp1735-1744

Abstract

Recently, adversarial input highly negotiates the security concerns in deep learning (DL) techniques. The main motive to enhance the natural language processing (NLP) models is to learn attacks and secure against adversarial text. Presently, the antagonistic attack techniques face some issues like high error and traditional prevention approaches accurately secure data against harmful attacks. Hence, some attacks unable to increase more flaws of NLP models thereby introducing enhanced antagonistic mechanisms. The proposed article introduced an extended text adversarial generation method, TextBugger. Initially, preprocessing steps such as stop word (SR) removal, and tokenization are performed to remove noises from the text data. Then, various NLP models like Bi-directional encoder representations from transformers (BERT), robustly optimized BERT (ROBERTa), and extreme learning machine neural network (XLNet) models are analyzed for outputting hostile texts. The simulation process is carried out in the Python platform and a publicly available text classification attack database is utilized for the training process. Various assessing measures like success rate, time consumption, positive predictive value (PPV), Kappa coefficient (KC), and F-measure are analyzed with different TextBugger models. The overall success rate achieved by BERT, ROBERTa, and XLNet is about 98.6%, 99.7%, and 96.8% respectively.
Optimization of sales by applying e-commerce and digital marketing through social networks Lazo-Amado, Misael; Meyluz, Paico-Campos
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.pp2079-2089

Abstract

Companies must have a strategy plan to satisfy their users and implement new methods to work with technology since people nowadays are more related to technology avoiding traditional sales and having virtual sales is why it has the objective of optimizing sales in companies by applying e-commerce and digital marketing through social networks. The methodology was carried out with Scrum, which has five stages (planning meeting, sprint backlog, daily meetings, sprint review, and retrospective review) that allows to comply with each established sprint showing as a result a functional project. As a result indicates the solution of each phase of the methodology getting the ecommerce system, with a validation by 7 experts specialized in (realism, integration, adaptability, technology, innovation, functionality, and usability) indicating a total of 93% showing a perfect state of the system and meets the satisfaction for the user and finally indicates the development of digital marketing by the social network Facebook showing a great improvement in their sales reaching up to triple their sales.
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.
An efficient implementation of credit card fraud detection using CatBoost algorithm Suryanarayana, Vadhri; Maddileti, Kuruva; Satyanarayana, Dune; Jyothi, R Leela; Sreekanth, Kavuri; Mande, Praveen; Miriyala, Raghava Naidu; Sudhakar, Oggi
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.pp1914-1923

Abstract

Transaction fraud has grown to be an important issue in worldwide, banking and commerce security is easier access to trade information. Every day, there are more and more incidents of transaction fraud, which causes large financial losses for both consumers and financial professionals. The ability to identify transaction fraud is getting closer to reality due to improvements in computer science's machine learning (ML) and data mining areas. So, one of them that is becoming dangerous is credit card fraud (CCF). Millions of people are experiencing financial loss and identity theft as a result of these malicious operations. The CCF of many illegal activities that fraudsters are always using new methods to carry out. One major problem facing financial services sector is CCF. To overcome this, categorical boosting (CatBoost) algorithm is explained as a solution to these problems. Fraud or fraudulent transactions are identified using this effective CatBoost algorithm implementation for identification of CCF. Thus, in terms of accuracy, precision, and detection rate this method gives better performance.
Modern machine learning and deep learning algorithms for preventing credit card frauds Kumar, Indurthi Ravindra; Hameed, Shaik Abdul; Annapurna, Bala; Paladugu, Rama Krishna; Narayana Reddy, Veeramreddy Surya; Kaveti, Kiran Kumar
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.pp1673-1680

Abstract

Credit card fraud poses a significant threat to financial institutions and consumers, particularly in the context of online transactions. Conventional rule-based systems often struggle to keep pace with the evolving tactics of fraudsters. This research paper investigates the application of advanced machine learning and deep learning algorithms for credit card fraud detection. By reviewing existing methodologies and addressing the challenges associated with fraud detection, we explore the potential of stateof-the-art techniques in enhancing detection accuracy and efficiency. Key aspects such as transaction data analysis, feature engineering, model evaluation metrics, and practical implementations are discussed. The findings underscore the importance of leveraging advanced algorithms to combat fraudulent activities effectively, thereby safeguarding the integrity of online transactions.
Trust evaluation in online social networks for secured user interactions Yarava, Anitha; Bindu, C. Shoba
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.pp2070-2078

Abstract

Online social network is a good platform, where users can share their opinions, ideas, products, and reviews with known (friends and relatives) and unknown users. The growing fame and its easy accesses of new users sometimes lead to security and privacy issues. Many methods are reported so far to address these issues but usage of high complex cryptographic algorithms creating new set of performance related challenges to the mobile users. In this paper, light weight soft security (trust) method is proposed. The proposed method “Trust evaluation in online social networks for secured user interactions-TEOSN” uses user social activities in estimation of his trustworthiness. Each user is observed in terms of followed factor-???????? (his interactions with others) and follower factor-???????? (others interaction with him). The factors ???????? and ???????? are estimated using fuzzy logic and user trust-???? is estimated using beta distribution. The performance of TEOSN is verified theoretically and practically. In experimental results, TEOSN is verified against different number of users; especially it outperformed existing methods in trust computation of target users at 2 to 4-hop distances.

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