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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 36, No 3: December 2024" : 65 Documents clear
Breast cancer identification using machine learning and hyperparameter optimization Arifin, Toni; Prasetyo Agung, Ignatius Wiseto; Junianto, Erfian; Rachman, Rizal; Wibowo, Ilham Rachmat; Agustin, Dari Dianata
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1620-1630

Abstract

Breast cancer identification can be analyzed through genomic analysis using gene expression data, one type of which is mRNA. This involves analyzing gene expression patterns of breast tissue samples to distinguish breast cancer from healthy tissue or to differentiate subtypes of different breast cancers. This research developed the right computational model for breast cancer classification using machine learning and hyperparameter optimization algorithms. The primary objective of this research is to utilize various machine learning algorithms to classify breast cancer based on gene expression and enhance the models developed in previous studies. This paper provides an extensive literature review of prior breast cancer classification research and offers new theoretical perspectives. This research used a problem-solving approach with conventional machine learning techniques, most notably the decision tree. It also evaluates other machine learning algorithms for comparison, including k-nearest neighbor, naïve bayes, random forest, extra tree classifier, and support vector machine. The evaluation process used classification reports that provide insight into the precision, recall, F1-score, and accuracy of each machine learning model. The evaluation results show that the performance of the decision tree algorithm model is superior and impressive, achieving 99.73% accuracy and a score of 1 for precision, recall, and F1-score.
Electronic system to speckle phenomenon characterization for random movement on fiber optics Ortega Galicio, Orlando Adrian; Calvo, Jinmi Lezama; Diaz Leyva, Teodoro Neri; Saavedra, Melina Machaca; Sanchez Lopez, Simon Alejandro; Baldárrago, Alexandra Chávez; Atalaya, Omar Chamorro
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1409-1420

Abstract

Peru is a country located in a telluric area. The early detection of earthquakes will alert the population and avoid human losses. There are different methods to detect it, mainly on mechanical movements and electronic sensors, which are currently used. This article presents the analysis and implementation of a repetitive motion generation and detection system based on the study of the speckle phenomenon through an optical fiber. The analysis is calculated by the technique of averaged difference that allows obtaining the intensity variation of two consecutive frames, as the speckle pattern changes and occupies different positions. Several tests are carried out that show the relationship of the controlled random movement and speckle characteristics obtained, the test system that can be used for the detection of random movements similar to P and S earthquakes waves.
Artificial intelligence and machine learning implementation status on Latam: a systematic literature review Carlos, Palomino Vidal; Patricia, Condori Obregon; Enrique, Stolar Sirlupu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1911-1918

Abstract

Artificial intelligence (AI) and machine learning (ML) are disruptive technologies nowadays. It is well known that many important organizations use them to improve their productivity and processes, and many new applications are being developed as well. In Latin America, the adoption of new technologies is slower than in other parts of the world, limited by budget and trained personnel. The present research is a systematic literature review (SLR) conducted to analyze the implementation status of AI and ML technologies in Latin America, analyzing the improvements that these technologies bring to organizations. The methodology used in this literature review was PRISMA, a popular method widely used in this type of research. The findings were that the most relevant areas using these types of technologies are education and health, identifying also that their implementation improves operative efficiency, technology innovation, and competitiveness. These findings also demonstrate the lack of efforts in implementation in other business sectors like administration, agriculture, and production, which provides a great opportunity to improve in these areas in the future.
A robust method for detecting fake news using both machine and deep learning algorithms Alikhashashneh, Enas Ahmad; Nahar, Khalid M.O.; Abual-Rub, Mohammed; Alkhaldy, Hedaya M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1816-1826

Abstract

Spreading fake news and false information on social media is very common and can be done effortlessly due to the huge number of users of each of the various social media platforms. Another reason for having such a speedy spread of fake news (which makes about 40% of the information published on social media platforms) is the inability of these platform to verify the authenticity of the news before allowing it to be published. This research will use information technology to detect fake news/ false information and change this kind of technology from being the cause of the problem to a tool to solve it. This research provides a method that uses both machine learning (ML) and deep learning (DL) algorithms to detect fake information versus real information and compare the performance of the algorithms. The results of this research indicate that the algorithms that use term frequency inverse document frequency (TF-IDF) have achieved better results than the algorithms that use Word2Vec. Long short-term memory (LSTM) algorithm, however, has achieved the best performance; of 99% accuracy -when using TF-IDF, and 94% -when using Word2Vec.
Enhanced driving assistance: automated day and night vehicle detection system utilizing convolutional neural networks Zaarane, Abdelmoghit; Slimani, Ibtissam; Elhabchi, Mourad; Atouf, Issam
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1532-1542

Abstract

This paper presents an enhanced real-time vehicle detection system using convolutional neural networks (CNNs) for both daytime and night-time conditions. Initially, the system determines the time of capture by analyzing the upper part of input images. For daytime detection, it uses normalized cross-correlation and two-dimensional discrete wavelet transform (2D-DWT) techniques. Night-time detection involves identifying vehicle lamps through color thresholding and connected component techniques, followed by symmetry analysis and CNN classification. The dataset for training includes images from the Caltech Cars, AOLP, KITTI Vision, and night-time vehicle detection datasets, ensuring robust performance across various lighting conditions. Experiments demonstrate the system's high accuracy, achieving 99.2% during the day and 98.27% at night, meeting real-time requirements and enhancing driving assistance systems' reliability.

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