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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 9,174 Documents
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Enhancing lung lesion localization in CT-scans: a novel approach using FE_CXY and statistical analysis Nurul Najiha Jafery; Siti Noraini Sulaiman; Muhammad Khusairi Osman; Noor Khairiah A. Karim; Mohd Firdaus Abdullah; Iza Sazanita Isa; Zainal Hisham Che Soh
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp913-925

Abstract

Intelligence algorithm systems rely on a large dataset to effectively extract significant features that can recognize patterns for classification purposes and extensively utilized to assist the physicians in diagnosis of lung cancer. Extracting valuable features from the available dataset is crucial, especially in cases where additional real data may not be readily accessible. In this context, we propose a novel method called feature extraction based on centroid (FE_CXY) for lesion localization, utilizing a statistical approach. The approach begins with a segmentation process that employs image processing techniques to extract features of interest which is data centroid. This extracted data is then used to compute statistical measurements, revealing hidden patterns that contribute to distinguishing between lesion and non-lesion locations. The method’s efficiency is reflected in the development of robust models with improved performance in localizing lung lesions. The study’s statistical findings strongly indicate that FE_CXY plays a crucial role as an important feature for detecting lesion localization supported by a student’s t-test, which identifies a statistically significant difference in the patterns between lesion and non-lesion localization (p<0.05). By incorporating this method into lung cancer detection systems, we anticipate improved accuracy and efficacy, thereby benefiting early diagnosis and treatment planning.
Improving the term weighting log entropy of latent dirichlet allocation Muhammad Muhajir; Dedi Rosadi; Danardono Danardono
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp455-462

Abstract

The process of analyzing textual data involves the utilization of topic modeling techniques to uncover latent subjects within documents. The presence of numerous short texts in the Indonesian language poses additional challenges in the field of topic modeling. This study presents a substantial enhancement to the term weighting log entropy (TWLE) approach within the latent dirichlet allocation (LDA) framework, specifically tailored for topic modeling of Indonesian short texts. This work places significant emphasis on the utilization of LDA for word weighting. The research endeavor aimed to enhance the coherence and interpretability of an Indonesian topic model through the integration of local and global weights. Local Weight focuses on the distinct characteristics of each document, whereas global weight examines the broader perspective of the entire corpus of documents. The objective was to enhance the effectiveness of LDA themes by this amalgamation. The TWLE model of LDA was found to be more informative and effective than the TF-IDF LDA when compared with short Indonesian text. This work improves topic modeling in brief Indonesian compositions. Transfer learning for NLP and Indonesian language adaptation helps improve subject analysis knowledge and precision, this could boost NLP and topic modeling in Indonesian.
Deep learning for economic transformation: a parametric review Tariq, Usman; Ahmed, Irfan; Khan, Muhammad Attique; Bashir, Ali Kashif
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp520-541

Abstract

Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.
Long-term power prediction of photovoltaic panels based on meteorological parameters and support vector machine Saurabh Gupta; Palanisamy Ramasamy; Pandi Maharajan Murugamani; Selvakumar Kuppusamy; Selvabharathi Devadoss; Barath Suresh; Vignesh Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp687-695

Abstract

Solar energy is the most generally accessible energy in the entire globe. Proper solar panel maintenance is necessary to reduce reliance on imported energy. Continuous monitoring of the solar panel's power output is required. The deployment of internet of things (IoT) monitoring of solar panels for maintenance is the basis for the current research. A multi-variable long-term photovoltaic (PV) power production prediction approach based on support vector machine (SVM) is developed in this study with the aim of completely evaluating the influence of PV panels performance and actual operational state factors on the power generation efficiency. This study examines the use of SVM and climatic factors to forecast the long-term output of power from solar panels. A solar power facility in a semi-arid area provided the data utilized in this investigation. Temperature, humidity, wind speed, and sun radiation are some of the meteorological variables that were considered in the study. To anticipate the power generation of the panels, the SVM is trained using the climatic factors and the power generation data. The findings demonstrate that the SVM model consistently predicts the panels' long-term power generation with a high degree of accuracy.
Diabetes mellitus prediction using machine learning within the scope of a generic framework Nidhi Arora; Shilpa Srivastava; Ritu Agarwal; Vandana Mehndiratta; Aprna Tripathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1724-1735

Abstract

Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models.
An improve unsupervised discretization using optimization algorithms for classification problems Rozlini Mohamed; Noor Azah Samsudin
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1344-1352

Abstract

This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. Recognizing the critical role of discretization in enhancing classification performance, the study integrates equal width binning (EWB) with two optimization algorithms: the bat algorithm (BA), referred to as EB, and the whale optimization algorithm (WOA), denoted as EW. The primary objective is to determine the optimal technique for predicting relevant class labels. The paper emphasizes the significance of discretization in data preprocessing, offering a comprehensive approach that combines discretization techniques with optimization algorithms. An investigative study was undertaken to assess the efficiency of EB and EW by evaluating their classification performance using Naive Bayes and K-nearest neighbor algorithms on four continuous datasets sourced from the UCI datasets. According to the experimental findings, the suggested EB has a major effect on the accuracy, recall, and F-measure of data classification. The classification performance using EB outperforms other existing approaches for all datasets.
Masked facial recognition using ensemble convolutional neural network and grey-level co-occurrence matrix Om Pradyumana Gupta; Arun Prakash Agrawal; Om Pal
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp302-311

Abstract

The COVID-19 pandemic proved how face masks became necessary to stop the spread of infection. Due to this, effective identification of people wearing face mask became challenging. Masked facial recognition has significantly increased in accuracy because of developments in convolutional neural networks (CNNs). Small size of the dataset of masked facial images has been a problem in earlier research. As would be expected, this results in poorer accuracy when the model tries to identify faces. In this study, a novel model is proposed with textural feature extraction using grey-level co-occurrence matrix (GLCM) and an ensemble of two pre-trained CNNs DenseNet-121 and VGG-16. Using the minimum redundancy and maximum relevance, the model has improved accuracy by choosing the most important features of the image. The model was trained using in-house dataset that included 38,290 photos of 2,500 people with approximately equal distribution of properly masked, partially masked, and unmasked images. In this, we evaluated the performance of the model on different classifiers multi-class logistic regression (LR) and support vector machine (SVM) with one-vs-rest (OvR) classification and artificial neural network (ANN) and applied a soft voting scheme. The model achieved the highest accuracy of 98.56% at a learning rate of 0.001 on the ANN classifier.
Sampled-data observer design for sensorless control of wind energy conversion system with PMSG Zaggaf, Mohammed Hicham; Mansouri, Adil; El Magri, Abdelmounime; Watil, Aziz; Lajouad, Rachid; Bahatti, Lhoussain
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp52-61

Abstract

This paper presents a nonlinear observer for a variable-speed wind energy conversion system (WECS) utilizing a permanent magnet synchronous generator (PMSG). The study addresses the design of high-gain sampled-data observers based on the nonlinear WECS model, supported by formal convergence analysis. An essential aspect of this observer design is the incorporation of a time-varying gain, significantly enhancing system performance. Convergence of estimation errors is demonstrated using the input-to-state stability method. Simulation of the proposed observer is conducted using the MATLAB-Simulink tool. The obtained results are presented and analyzed to showcase the overall effectiveness of the proposed system.
MAPATON 2023: implementing a tool for the analysis of forest fire zones in Arequipa Natalia I. Vargas-Cuentas; Meyluz Paico-Campos; Abel Cahuana; Elber E. Canto-Vivanco; Tania Valencia; Sebastian Ramos-Cosi; Peter Villena; Avid Roman-Gonzalez
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1511-1523

Abstract

The "Mapaton 2023," a collaborative initiative by the Peruvian Space Agency, Paraguayan Space Agency, and AMERIGEOS, introduces an innovative approach to analyze fire-prone areas in Arequipa, a pivotal endeavor for disaster prevention. Focused on the provinces of Arequipa and Caylloma, recent studies identified over 1,500 affected hectares by forest fires, emphasizing the urgency of employing satellite images and geospatial analysis techniques. Leveraging Landsat 8 satellite imagery, the research calculated indices, including the normalized difference vegetation index (NDVI) for vegetation analysis and the differenced normalized burn ratio (dNBR) for fire severity assessment. Results revealed varying impacts, with some areas exhibiting increased vegetation and others displaying significant damage. The use of ArcGIS online facilitated the presentation of geospatial data, emphasizing the utility of remote sensing in comprehending and addressing forest fires. Drawing insights from analogous studies in Mexico and the Amazon, this research underscores the importance of remote sensing and geospatial analysis in informing preventive measures against wildfires. The findings, crucial for environmental management, are recommended for sharing with relevant authorities, and the continued use of diverse satellite imagery sources is encouraged to enhance accuracy in monitoring and mitigating forest fires.
Improving time efficiency in big data through progressive sampling-based classification model Nandita Bangera; Kayarvizhy Kayarvizhy; Shubham Luharuka; Asha S. Manek
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp248-260

Abstract

The proposed system aims to overcome challenges posed by large databases, data imbalance, heterogeneity, and multidimensionality through progressive sampling as a novel classification model. It leverages sampling techniques to enhance processing performance and overcome memory restrictions. The random forest regressor feature importance technique with the gini significance method is employed to identify important characteristics, reducing the data’s features for classification. The system utilizes diverse classifiers such as random forest, ensemble learning, support vector machine (SVM), k-nearest neighbors’ algorithm (KNN), and logistic regression, allowing flexibility in handling different data types and achieving high accuracy in classification tasks. By iteratively applying progressive sampling to the dataset with the best features, the proposed technique aims to significantly improve performance compared to using the entire dataset. This approach focuses computational resources on the most informative subsets of data, reducing time complexity. Results show that the system can achieve over 85% accuracy even with only 5-10% of the original data size, providing accurate predictions while reducing data processing requirements. In conclusion, the proposed system combines progressive sampling, feature selection using random forest regressor feature importance (RFRFI-PS), and a range of classifiers to address challenges in large databases and improve classification accuracy. It demonstrates promising results in accuracy and time complexity reduction.

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