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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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An efficient Grain-80 stream cipher with unrolling features to enhance the throughput on hardware platform Raghavendra Ananth; Panduranga Rao Malode Vishwanatha Rao; Narayana Swamy Ramaiah
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.pp218-226

Abstract

The stream cipher is a fundamental component of symmetric cryptography and offers unique implementation speed and scalability advantages. Additionally, the complexity of the cipher algorithm deployment environment forces new, appropriate designs and challenges on the already-existing cipher algorithms. To increase throughput, an efficient Grain-80 stream cipher with unrolling features is designed in this manuscript. The Grain-80 cipher uses an 80-bit key, and a 64-bit initialization vector (IV) and contains two feedback shift registers (linear and non-linear) and an output function. The register balancing and unrolling features of the proposed Grian-80 cipher combine to increase throughput while requiring little additional hardware. Low latency, fast throughput, excellent efficiency, and reduced attack susceptibility are all features of the unrolling architecture. The proposed Grain-80 cipher utilizes <1% chip area and operates at 542.7 MHz on Artix-7 field programmable gate array (FPGA). The proposed Grain-80 cipher improves the operating frequency by 14.85% over conventional Grain-80 cipher. The Grain-80 cipher obtains the throughput of 4.35 Gbps and 8.69 Gbps for unrolling factors 8 and 16, respectively. Lastly, the proposed Grain-80 cipher is compared with existing Grain-80 ciphers with improved throughput and hardware efficiency.
Blockchain technology integration in service migration to 6G communication networks: a comprehensive review Ahmed Al-Ansi; Abdullah M. Al-Ansi; Ammar Muthanna; Andrey Koucheryavy
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.pp1654-1664

Abstract

The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, software-defined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain.
Agile fusion: developing 'Eat at Right Place' sentiment analysis tool Akash Prabhune; Vinay R Srihari; Neeraj Kumar Sethiya; Mansi Gauniyal
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.pp602-619

Abstract

This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.
Improving Kui digit recognition through machine learning and data augmentation techniques Nayak, Subrat Kumar; Nayak, Ajit Kumar; Mishra, Smitaprava; Mohanty, Prithviraj; Tripathy, Nrusingha; Prusty, Sashikanta
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.pp867-877

Abstract

Speech digit recognition research is growing decisively, and a bulk of digit recognition algorithms are used in European and a few Asian languages. Kui is a low-resourced tribal language locally used in several states of India. Despite its significance, there is not much research on Kui's speech. This research aims to present an in-depth analysis of novel Kui digit recognition using predefined machine learning (ML) techniques. For this purpose, we first gathered spoken numbers i.e. from 0 to 9 of eight different speakers containing a total of 200 words. Secondly, we choose the numbers: ଶୂନ (zero), ଏକ (one), ଦୁଇ (two), ତିନି(three), ସାରି(four), ପାସ (five), ସଅ (six), ସାତ (seven), ଆଟ (eight), ନଅ (nine). Meanwhile, we build nine different ML models to recognize Kui digits that take the Mel-frequency cepstral coefficients (MFCCs) method to extract the relevant features for model predictions. Finally, we compared the performance of ML models for both augmented and non-augmented Kui data. The result shows that the SVM+Augmentation method for Kui digit recognition combined obtained the highest accuracy of 83% than other methods. Moreover, the difficulties and potential prospects for Kui digit recognition are also highlighted in this work.
One level deep convolutional neural network for facial key points detection Abdelaali Benaiss; Rachid El Ayachi; Mohamed Biniz; Mustapha Oujaoura
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.pp1694-1704

Abstract

Facial landmark detection has a lot of applications in face recognition, face alignment, facial expression recognition, video surveillance and security systems. In the existing literature, there are multiple methods utilizing convolutional neural networks (CNNs) that address this problem in various ways. In many cases, the models use a tree-like structure of CNNs to achieve better results. This paper proposes a combination of three parallel deep convolutional neural networks (DCNNs) to estimate the accurate localization of each keypoint. The first one focuses on the whole face to outperform five points, including the eyes, nose, and mouth corners. The second one focuses on the eyes-nose parts to outperform three points, specifically the eyes and nose. The last one focuses on the nose-mouth parts to outperform three points, namely the nose and mouth corners. Further, we combine all outputs of the three DCNNs and take the average value of each detected key point as the final output. In the first step, we improvthe the parameter efficiency and accuracy of each DCNNs through a set of experiments using the labeled face parts in-the-wild database (LFPW) and the helen facial feature dataset (Helen). Then, we demonstrate that our approach yields more accurate estimations of facial key points than two state-of-the-art methods and commercial software in terms of accuracy.
The hybrid of BERT and deep learning models for Indonesian sentiment analysis Dwi Guna Mandhasiya; Hendri Murfi; Alhadi Bustamam
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.pp591-602

Abstract

Artificial intelligence (AI) is one example of how data science innovation has advanced quickly in recent years and has greatly improved human existence. Neural networks, which are a type of machine learning model, are a fundamental component of deep learning in the field of AI. Deep learning models can carry out feature extraction and classification tasks in a single design because of their numerous neural network layers. Modern machine learning algorithms have been shown to perform worse than this model on tasks including text classification, audio recognition, imaginary, and pattern recognition. Deep learning models have outperformed AI-based methods in sentiment analysis and other text categorization tasks. Text data can originate from a number of places, including social media. Sentiment analysis is the computational examination of textual expressions of ideas and feelings. This study employs the convolutional neural network (CNN), long-short term memory (LSTM), CNN-LSTM, and LSTM-CNN models in a deep learning framework using bidirectional encoder representations from transformers (BERT) data representation to assess the performance of machine learning. The implementation of the model utilises YouTube discussion data pertaining to political films associated with the Indonesian presidential election of 2024. Confusion metrics, including as accuracy, precision, and recall, are then used to analyse the model’s performance.
PQ enhancement in grid connected EV charging station using novel GVCR control algorithm for AUPQC device Dharavatu, Anil Kumar; Ramavathu, Srinu Naik
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.pp1-13

Abstract

The rapid increase of environmental impacts together global warming is conquered by substantial selection of electric-vehicles (EV’s) over the internal-combustion engine (ICE) vehicles. The replacement of these vehicles in transportation industry has led to reducing the running cost, ecological emissions, vehicle maintenance. The EV’s are operated by available battery energy and energized through utility-grid integrated EV charging stations. It is noted that, such charging stations may introduce power-quality issues, highly impacting the electric-grid due to presence of power electronic conversion devices in EV charging stations. The primary emphasis of power-quality impacts on electrical distribution grid are counteracted by employing active universal power-quality conditioner (AUPQC) device. The main role of AUPQC has been selected for mitigation of various PQ problems on both electric-grid side and charging station by using feasible control objective. In this work, a novel generalized voltage-current reference (GVCR) control objective has been proposed for extraction of fundamental reference voltage-current signals. The key findings are simple mathematical notations, no transformations, fast response, low dv/dt switch stress, low switching loss and maximum efficiency. The main goal is design, operation and performance of proposed GVCR controlled AUPQC device has been validated under integration of various EV chargers to electric-grid by using MATLAB/Simulink computing tool, simulation results are presented for analysis and interpretation.
Proactive ransomware prevention in pervasive IoMT via hybrid machine learning Usman Tariq; Bilal Tariq
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.pp970-982

Abstract

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem.
Optimal capacity threshold for reversible watermarking using score function Chaiyaporn Panyindee
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.pp1598-1604

Abstract

Histogram shifting is an important technique of reversible watermarking, which can embed large payloads into digital images with low distortion. The technique must determine two threshold values to achieve the lowest possible distortion. Appropriate threshold values might be found by iterative methods, but it is computationally inefficient when the payloads are high and varied. In this paper, we show that the optimal threshold values lie on a straight line and occur at the boundary of the payload-satisfying region. Moreover, we propose a high performance algorithm to approximate the optimal threshold values. Under the same image quality, experimental results indicate that the proposed scheme could get closer threshold values to the optimal threshold values, compared to previous work. Therefore, it requires a smaller number of iterations to obtain the desirable threshold values.
Fresnel lenses and auto tracking to increase solar panel output power Sindak Hutauruk; Libianko Sianturi; Irvan Togatorop
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.pp1389-1398

Abstract

This solar panel, which is equipped with microcontroller-based auto tracking, is also equipped with a Fresnel lens to obtain more optimal power because the Fresnel lens has the property of bending or refracting light rays that pass through it so that it can focus sunlight. The Fresnel lens focuses sunlight radiation onto the solar panels. To determine the magnitude of the influence of Fresnel lenses on increasing the power produced by solar panels, two solar panels of the same size (11×11) cm were moved by auto tracking using the same axis. One solar panel is not fitted with a Fresnel lens, and the other is fitted with a Fresnel lens measuring 30×30 cm, where the distance between the Fresnel lens and the solar panel is 5 cm. Measurements of the output power of both solar panels were carried out simultaneously, namely from 08.00 to 18.00 WIB (West Indonesia time). The power output of both solar panels was measured in 30-minute intervals, resulting in 21 measurements. Solar panels using Fresnel lenses produce an output power that is 105.306% more significant than that of those without Fresnel lenses.

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