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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
 895   896   897   898   899 
A comparative study of CNN architectures for the detection of tomato leaf diseases Benkrama, Soumia; Ahmed, Benyamina; Hemdani, Nour El Houda
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1587-1594

Abstract

Recent advancements in computer vision and machine learning (ML) have revolutionised various sectors, including precision agriculture (PA). In our study, we focused on detecting tomato leaf diseases (TLD) using deep learning (DL) techniques. Using a convolutional neural network (CNN) model, we developed an agricultural image index to accurately detect TLD. By utilizing available datasets from Kaggle, we trained our model to recognize various TLDs. To determine the most effective one, we compared multiple architectures, including VGG, ResNet, and EfficientNetB1. The obtained results demonstrated a classification accuracy of over 99% on the test set. This approach has allowed us to accelerate and enhance the disease detection process, positively impacting agricultural communities by reducing crop losses and enabling early intervention in case of disease outbreaks. Our study highlights the effectiveness of CNN models in the detection of TLD, paving the way for future applications in PA.
Electrical system load re-phasing: a case of a university building in the Philippines Marcos, Ferdinand L.; Domingo, Enalyn T.; Lapuz, Cid L.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1441-1448

Abstract

In the pursuit of attaining energy efficiency, administrators must delve deeply into the electrical system of a facility, especially if it is an old structure. As years go by, renovations and the addition of new equipment may lead to an imbalance in the electrical system. These imbalances may lead to inefficiencies, contributing to damage to equipment. This study aimed to investigate the electrical system of a university building by determining whether the percent current and voltage unbalance values in the system meet the standards. For non-conforming electrical branches, re-phasing schemes were proposed. Data revealed that the majority of the panelboards in the building have voltage imbalances that are within the allowable limit, while there is a considerable number of panelboards with above-the-minimum current unbalance value. The original configurations of some panelboards were retained to avoid further increase in the percentage of current imbalance associated with re-phasing. Merging certain panelboards, however, resulted in a reduction of current imbalances within the acceptable limit. If the re-phasing and merging of loads are to be implemented, a cost-benefit analysis and a study on the improvements in energy efficiency may be considered for further research.
Deep belief network classification model for accurate breast cancer detection and diagnosis Amirthayogam, G.; R., Deepak; Ram, M. Preethi; J., Nithya; Basha H., Anwar; B., Sriman; Sundar, R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1900-1912

Abstract

Breast cancer is still one of the common malignancies and endemics that are fatal to women across the globe. Early-stage diagnosis helps reduce the percentage of deaths because treatment outcomes are much better at that stage. As the contemporary approaches in machine learning (ML) and deep learning (DL) emerged, the automatic detection of breast cancer has received a great consideration for their ability to improve diagnosis and treatment. We present a new deep belief network (DBN) based breast cancer detection system to increase the accuracy and the dependability of the diagnosis of breast cancer. The major modules of the system are image preprocessing, feature extraction and the DBN-based classification to guarantee accurate detection and classification of malignant and benign breast lesions. We compared the proposed DBN model with the existing DL models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). It is with respect to critical features of the model performance which includes accuracy, precision, recall, specificity and F1-score. The methodologies used in this study show that the performance of the proposed DBN model is significantly better than these conventional algorithms in accuracy and sensitivity where the DBN model is an ideal method for the early detection of breast cancer. Through extensive experimentation, we compared the proposed DBN model with existing DL techniques such as CNNs, RNNs, LSTMs, and GANs. Our results show that the proposed DBN model outperforms these models in several key performance metrics.
Effective vocabulary learning through augmented and virtual reality technologies Arifin, Arifin; De Vega, Nofvia; Rafiqa, Syarifa
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1855-1864

Abstract

This study investigates the role of augmented reality (AR) and virtual reality (VR) technologies in enhancing vocabulary learning achievement among students. It addresses the need for innovative instructional methods that improve engagement and retention compared to traditional approaches. Utilizing a survey-based quantitative design supplemented by qualitative interviews, the research involved 220 participants from diverse educational backgrounds, providing a robust dataset for analyzing the impact of these immersive technologies on vocabulary acquisition. Structured questionnaires assessed engagement levels, learning outcomes, and user experiences with AR and VR applications designed explicitly for vocabulary enhancement. The findings reveal that 75% of participants reported improved vocabulary retention, highlighting the interactive nature of AR and VR as a significant factor influencing student attitudes toward vocabulary learning. The study concludes contextualized learning scenarios with interactive features are more effective than passive learning environments. Additionally, it suggests future research directions, including developing personalized learning paths and integrating collaborative features to enhance group learning experiences. The implications for educators emphasize the potential of AR and VR technologies to transform vocabulary instruction and foster deeper engagement among learners.
An efficient machine learning framework for optimizing hyperspectral data analysis in detecting adulterated honey Yeole, Ashwini N.; M. S., Guru Prasad; Kumar, Santosh
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1776-1786

Abstract

Honey adulteration detection involves employing spectral data, often utilizing machine learning (ML) techniques, to identify the presence of impurities or additives in honey. This study aims to explore ML models through the collection of a hyperspectral honey dataset with limited samples and 128 features. Three distinct feature selection (FS) methods i.e., Boruta, repeated incremental pruning to produce error reduction (RIPPER), and gain ratio attribute evaluator (GRAE) are applied to extract important features for decision-making. Then, the feature-selected dataset is classified through four effective ML algorithms, such as support vector machine (SVM), random forest (RF), logistic regression (LR), and decision tree (DT). Accuracy, F1-score, Kappa Statistics, and Matthews correlation coefficient (MCC) are the performance metrics used to assess the results of ML algorithms. RIPPER FS technique gave the best results by improving its accuracy values from 79.05% (primary data) to 91.89% (augmented data) for the RF classifier model and 74.93% (primary data) to 91.89% (augmented data) for the DT classifier model. These detailed examinations of the experiments demonstrate that proper finetuning of the ML methods can play a vital role in optimizing hyperspectral data analysis for detecting adulteration levels in honey samples.
Enhancing trust and privacy in iot ecosystems with the distributed trust and privacy consensus framework Priyadarashini, Sushma; T, Anuradha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1990-2000

Abstract

In the contemporary digital landscape, the proliferation of wireless sensor networks (WSNs) and the internet of things (IoT) has revolutionized the way we interact with the physical world, offering unprecedented opportunities for automation and data-driven decision-making. However, this rapid expansion has also introduced significant challenges in terms of ensuring network security, maintaining user privacy, and establishing trust among devices. To address these critical issues, this paper introduces the distributed trust and privacy consensus framework (DTPCF), a novel methodology designed to strengthen trust and privacy within IoT ecosystems through a consensus-based approach. The DTPCF pioneers a distributed mechanism for trust management that evaluates and establishes the reliability of nodes democratically and transparently, thereby enhancing the robustness and scalability of IoT systems against malicious activities. Moreover, the framework integrates privacy preservation directly into the consensus process, employing state-of-the-art cryptographic techniques and protocols to protect sensitive data during transmission and decision-making phases. Through empirical analysis, the efficacy of the DTPCF is validated across various operational scenarios, demonstrating its effectiveness in enhancing network security, privacy, and trust. Performance metrics such as throughput, energy consumption, and node-level security are meticulously evaluated, providing comprehensive insights into the framework's capabilities and potential for real-world implementation.
Detecting pneumonia from chest X-rays using deep learning based neural networks: an hybrid approach Suta Siva Nishanth, Konjeti Naga Venkata Pavana; Basha, Syed Althaf; Srikanth, Vissamsetty; Narendra, Kommi Purna; Rachapudi, Venubabu
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1714-1723

Abstract

Pneumonia a disease which occurs when the alveoli (air sacs) in the lings fill with fluidlike substance it can be due to infectious agents like virus, bacteria especially in an environment with contaminated air is often considered as lethal disease because the deaths associated with is high. There are several factors which contribute to this disease like age as their immune systems are not fully developed making it easier to get attacked by infections, chronic health conditions like asthma or weak immune systems may worsen the situation. Machine learning (ML) algorithms have tend to perform better while images are given, however compared to them deep learning (DL) algorithms have shown good promising results especially when images are given as an input this is because they have upper hand in identifying key features and loss optimization makes them best suited for this tasks. The significance of this research is to make an extensive review on the pneumonia and early detecting pneumonia by utilizing DL based neural networks.
Exploring the impact of artificial intelligence driven solutions on early detection of cardiac arrest Venkatesha, Tejashree; Sundararajan, Saravana Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1938-1945

Abstract

The advancement of medical science and technology has yet not evolved up with a concrete solution towards early detection of cardiac arrest from practical deployment. It is noted that artificial intelligence (AI) has been proving a potential contributor to address this state of diagnosis emergency. In current era of research work, there has been various implementation model and review work has been carried out towards advocating AI for determining early onset of cardiac arrest; however, there are various contradiction and shortcoming which is quite challenging to be extracted. Hence, the current manuscript presents a review of existing methodology by presenting core taxonomies of recent AI-methods towards early detection of cardiac arrest. Various standard dataset has been studied too to find associated advantages and limitation that restrict the actual potential of AI to prediction. The outcome presents novel highlights of research gap, trade-off, and crisp highlights of effectiveness of existing AI approaches as a study contribution.
Five-Tier BI architecture with tuned decision trees for e-commerce prediction Arjunan, Thiruneelakandan; A., Umamageswari
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1633-1641

Abstract

In recent times, remarkable performance has been shown by large language models (LLMs) in a range of natural language processing (NLP) such as questioning, responding, document production, and translating languages. In today's competitive business landscape, understanding consumer behaviour in online buying is crucial for the success of e-commerce platforms. The work proposes a novel Five-Tier service-oriented BI architecture (FSOBIA) that leverages advanced tuned decision tree (ATDT) techniques for predicting online buying behaviour. The proposed FSOBIA offers e-commerce platforms a scalable and adaptable solution for gaining insights into consumer preferences and making informed business decisions. The goal of FSOBIA's design and implementation is to meet the needs of evolving users and quicker service. Experimental evaluations on real-world datasets in FSOBIA achieved over 95% prediction accuracy, outperforming traditional models: Decision trees (82%), and XGBoost (91%), while offering better scalability and computational efficiency.
Feasibility study and simulation of a 4 MW solar power plant design using PVSyst in Oefafi Village, Kupang, Indonesia Pranata, Nicholas; Saputri, Fahmy Rinanda; Akbar, Agie Malki
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1877-1890

Abstract

Indonesia has a vast amount of potential for renewable energy, with many places still experiencing inaccessibility and inadequacy to electricity. One of the places, Kupang in East Nusa Tenggara, suffers a deficit of 4 MW at night along with a blackout despite the implementation of the Oelpuah Solar Power Plant that can produce 5 MW. Thus, this study presents a feasibility study and simulation of additional solar power plant implementation using PVSyst based on geospatial analysis by ArcGIS to point down the desired location, namely Oefafi. The results produce 7,212,139 kWh/year (almost 20 MWh/day using 6,552 modules of Jinkosolar 610 Wp JKM-610N-78HL4- BDV, 274 units of Growatt 12KTL3-X inverters, and 2000 BYD B-Box PRO 2.5 1,024 V 5,200 Ah battery. The losses of the systems mostly come from temperature loss in PV. The total installation cost is 51 billion IDR with a payback period of 8.3 years, NPV of 6.6 billion IDR, and IRR of 10.62% for an electricity tariff of 1,000 IDR/kWh. The amount of greenhouses saved accounts for 134,110.3 tCO2. This will help in filling the 4MW deficit while also potentially providing to other unsupplied regions. Future research may use other software for comparison and give better explanations.

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