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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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Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion Areen Arabiat; Muneera Altayeb
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.pp1295-1303

Abstract

Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.
A novel fuzzy logic-based approach for textual documents indexing Latifa Rassam; Imane Ettahiri; Ahmed Zellou; Karim Doumi
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.pp254-263

Abstract

In the evolving landscape of information retrieval and natural language processing, the quest for more effective automatic keyword extraction (AKE) techniques from textual documents has become a pivotal research focus. Existing methodologies, while offering valuable insights, often grapple with the challenges posed by the imprecision and variability inherent in human language. This has led to a growing recognition of the need for innovative approaches to navigating textual content’s nuances more adeptly. In response to this imperative, this paper proposes a novel fuzzy indexing approach designed specifically for the indexing of textual documents. Fuzzy indexing, grounded in the principles of fuzzy logic, provides solutions for handling the inherent uncertainty and imprecision in natural language, especially when confronted with the intricacies of linguistic ambiguity and variability. By leveraging the power of fuzzy logic, we aim to enhance the precision of keyword extraction. This paper unfolds the intricacies of our fuzzy indexing approach, detailing the theoretical methodology through empirical evaluation and comparative analysis; we seek to demonstrate the efficacy of our approach in outperforming traditional methods in the context of fuzzy indexing for textual documents.
Hardware-realized secure transceiver for human body communication in wireless body area networks Nataraju, Chaitra Soppinahally; Sreekantha, Desai Karanam; Sairam, Kanduri VSSSS
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.pp601-609

Abstract

Wireless body area networks (WBANs), featuring wearable and implantable devices for collecting physiological data are increasingly critical in healthcare for enabling continuous remote monitoring, diagnostic improvements, and treatment optimization. Secure communication within WBANs is essential to protect sensitive health data from unauthorized access and manipulation. This paper introduces a novel secure digital (SD)- human body communication (HBC) Transceiver (TR) system, tailored for WBAN applications, that prioritizes security and offers significant enhancements in size, power efficiency, speed, and data transmission efficiency over current solutions. Leveraging a combination of frequency-selective (FS) digital transmission with walsh codes (WCs) or quadrature amplitude modulation (QAM), and incorporating one-round encryption and decryption modules, the system complies with the IEEE 802.15.6 standard, ensuring broad compatibility. Specifically, the QAM-based SD-HBC TR system exhibits a 4% reduction in chip area, a 7.6% increase in operating frequency, a 3.4% decrease in power consumption, a 27.5% reduction in latency, and improvements of 33% in throughput and 35.5% in efficiency. Importantly, it achieves a bit error rate (BER) of up to 10-8 , demonstrating high reliability across communication methods. This research significantly advances secure communication in WBANs, offering a promising approach for enhancing the reliability, efficiency, and security of healthcare monitoring technologies.
Electroencephalogram based human emotion classification for valence and arousal using machine learning approach Abhishek Chunawale; Mangesh Bedekar
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.pp920-931

Abstract

Humans have unique ability to express emotions and electroencephalogram (EEG) signals are one of the sought-after ways to analyze a person’s emotional state. However, extracting proper emotion related features from EEG and finding corresponding emotion is challenging because of complex nature of emotions and underlying brain activities. The objective of this paper is to address this issue for more accurate emotion classification based on EEG. It also compares feature extraction methods namely fast fourier transform (FFT) and discrete wavelet transform (DWT). DEAP dataset is used for classification of human emotions through support vector machine (SVM) and K-nearest neighbor (KNN) algorithms by considering features such as standard deviation, mean, variance, power spectrum density (PSD) for FFT; and energy, entropy for DWT. It is observed that feature extraction from FFT yielded better results than DWT and KNN gave more accuracy of 96.61% for valence and 96.42% for arousal as compared to SVM. The proposed method based on PSD and FFT fared better than other existing ones in terms of accuracy when compared against different features and feature extraction techniques. This approach is expected to help researchers to understand feature extraction from EEG signals and decide proper features and techniques for better implementation.
Topic prediction modelling on social media content using machine learning Izmi Dewi Aisha; Lili Ayu Wulandhari
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.pp207-217

Abstract

The simplicity to deliver an opinion about companies or institutions via social media has resulted in both positive and negative judgments. Through social media all positive and negative information will be easily found and spread. It is concerned that negative information will lead to negative public opinion. If this occurs, the company will suffer from a lack of trust, which will harm the company's reputation. Thus, to monitor uncontrolled issues, a company wants to know what topics or opinions are developing in the community. Therefore, the topic modelling using latent dirichlet allocation (LDA) is proposed to identify topics that are being discussed on social media. The findings of this study got the coherence score of 0.558 and based on the direct human judgment, the model got an average 80% correctly. The findings of this study reveal 4 topics groups that represent the corporate social media content. These findings offer information to companies about the latest topics or opinions that are currently developing in society which could provide recommendations related to decision-making on current issues thus increasing the trust and reliability towards the company.
Improving industrial security device detection with convolutional neural networks Orlando Iparraguirre-Villanueva; Josemaria Gonzales-Huaman; Jose Machuca-Solano; John Ruiz-Alvarado
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1935-1943

Abstract

Employee safety is paramount in the manufacturing industry to ensure their well-being and protection. Technological advancements, particularly convolutional neural networks (CNN), have significantly enhanced this safety aspect by facilitating object detection and recognition. This project aims to utilize CNN technology to detect personal protective equipment and implement a safety implement detection system. The CNN architecture with the YOLOv5x model was employed to train a dataset. Dataset videos were converted into frames, with resolution scale adjustments made during the data collection phase. Subsequently, the dataset was labeled, underwent data cleaning, and label and bounding box revisions. The results revealed significant metrics in safety equipment detection in industrial settings. Helmet precision reached 91%, with a recall of 74%. Goggles achieved 85% precision and an 87% recall. Mask absence recorded 92% precision and an 89% recall. The YOLOv5x model exhibited commendable performance, showcasing its robust ability to accurately locate and detect objects. In conclusion, the utilization of a CNN-based safety equipment detection system, such as YOLOv5x, has yielded substantial improvements in both speed and accuracy. These findings lay a solid foundation for future industrial security applications aimed at safeguarding workers, fostering responsible workplace behavior, and optimizing the utilization of information technology resources.
Safety hysteresis comparator design for transient overvoltage detection Ukrit Kornkanok; Sansak Deeon; Saktanong Wongcharoen
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.pp69-80

Abstract

This research introduces a safety hysteresis comparator that detects transient overvoltage in the track circuit relay interlocking of railway signaling system. This overvoltage is caused by voltage faults transmitted through the electric conductor on the track feed unit to the receiver equipped with the track relay, which acts as the occupied track circuit controller. The circuit was designed using safety system design principles and concepts. Findings illustrated that the transient overvoltage detection of the safety hysteresis comparator in the track circuit activated when the input voltage (Vin) was higher than the high hysteresis signal level (Vhyst_H). When Vin was less than low hysteresis signal level (VVhyst_L), the output voltage (Vo) state was low. Otherwise, it was high. The hysteresis voltage was 4.4 V. The installation of the transient overvoltage detector in the track circuit was to monitor the transient overvoltage fault in the track circuit and to confirm that the hysteresis comparator was in the safety failure mode, which was the safety function for the track circuit to be compliant with the IEC 61800-5-2 standard to ensure the system stability and reliability. It would maximize the performance of the controlling function and commanding of train arrangement of State Railway of Thailand (SRT).
R-based neural networks decision model for water air cooler system Mohammed, Haidas; Soumia, Kerrache
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1091-1098

Abstract

Water air cooler is a process that uses an evaporative system. In its first generation, there was no temperature sensor, and its fan was turned by an electric motor in an open loop system with a fixed speed. As well, the water’s flow and level in the tank are governed by a mechanical system, which is generally a floating ball attached to a shaft. In order to ameliorate this classical system with more advantages and performances, many ideas are included in subsequent generations such the integration of embedded systems, intelligent control and components of manufacturing materials. In this paper, the current study aims to integrate an intelligent system, which is the neural networks by using R language to give a smart decision model to command relays switching dedicated to control the electric motors, where the first one is tied up with a fan and the other to an electro-pump. The HC-SR04 ultrasonic and DHT11 sensors supervise the two desired parameters control, water level in the tank and the outside temperature successively.
Ethical hacking: real evaluation model of brute force attacks in password cracking Buthayna Al Sharaa; Saed Thuneibat
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.pp1653-1659

Abstract

Despite ongoing efforts to convince users of the value of password security and to enforce password creation standards on them, in many information systems the human factor still plays a role. In addition, not only do most users’ password creation and management practices largely remain unchanged, but password cracking tools and more critically, computer hardware also continue to advance. In this paper we present a model in ethical hacking; the proposed model concentrated on brute force attacks for password cracking. The main novelty of our work is that it first presents a mathematical model that calculates the number of different password permutations of varying lengths. Then the brute force attack is modelled using the Markov chain model and a method is developed to formulate the conventional optimization problem, which is classified as a discrete nonlinear problem. The experiments’ results demonstrate and validate the method’s effectiveness and suitability.
Empowered corrosion-resistant products through HCP crystal network: a topological assistance Khalid Hamid; Nasir Ayub; Mohammad Amir Delshadi; Muhammad Ibrar; Nor Zairah Ab Rahim; Yasir Mahmood; Muhammad Waseem Iqbal
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1544-1556

Abstract

Human computer interaction (HCI) aims to enhance product effectiveness and efficiency by empowering users. This research examines corrosion resistance in alloys, a concern due to technological advancements. Metals and alloys are susceptible to degradation, leading to functionality loss, structural collapse, and environmental contamination. Improving corrosion resistance is crucial for product efficiency. In this paper, HCI identifies requirements that emphasize taking an existing hexagonal closely packed (HCP) network, investigating the network for requirements in the form of vertices and edges, mapping different vertices and edges of the network graph with topological invariants, solving the network graph by invariants, and providing results for modeling and design of advanced networks and architectures. The HCI also ensures and investigates the optimization of results produced under the specifications. The study examines network graph results for irregularities, providing guidelines for engineers and manufacturers to create advanced alloy architectures with characteristics through mathematical and graphical methods.

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