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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 63 Documents
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Search results for , issue "Vol 33, No 1: January 2024" : 63 Documents clear
Car make and model recognition using convolutional neural network: fine-tune AlexNet architecture Md. Asifur Rahman Khan; Raju Talukder; Md. Anwar Hossen; Nusrat Jahan
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.pp370-379

Abstract

Artificial intelligence (AI) has significantly contributed to car make and model recognition in this current era of intelligent technology. By using AI, it is much easier to identify car models from any picture or video. This paper introduces a new model by fine-tuning the AlexNet architecture to determine the car model from images. First of all, our car image dataset has been created. Some of these images were taken by us, and others were taken from the website of the car connection. Then we cleaned all the unwanted images for better performance. Our dataset has ten classes containing 5,000 car images split into train and test data. After that, we augmented our data with random flip, rotation, and zoom to reduce overfitting. Finally, we used a pre-trained convolutional neural network (CNN) model AlexNet architecture. We fine-tuned AlexNet (FT-AlexNet) by adding three extra layers for better classification and compared it with the original AlexNet. To measure the performance of these models, accuracy, precision, recall, and F1-score were used. The results show that fine-tune AlexNet architecture outperforms the original AlexNet architecture. The results prove that recognition accuracy has increased due to our improvement approach.
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.
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.
Impedance analysis of squirrel-cage induction motor at high harmonics condition Aleksandr Skamyin; Yaroslav Shklyarskiy; Kirill Lobko; Vasiliy Dobush; Tole Sutikno; Mohd Hatta Jopri
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.pp31-41

Abstract

This study examines different methodologies for representing an asynchronous motor at higher harmonics by utilizing its equivalent circuit. Several approaches were compared based on simulation modeling. An experimental investigation was conducted in a laboratory setting using asynchronous electric motors with power ratings of 1.5 kW and 5.5 kW. The purpose of this investigation was to examine the characteristics of impedance in the presence of high-harmonic conditions. The generation of higher harmonics was achieved through the utilization of a precisely regulated thyristor rectifier in conjunction with a thyristor power controller. The findings indicate that the load on the shaft solely impacts the resistance at the fundamental harmonic, while the resistance at higher harmonics remains unaffected by the operating mode of the asynchronous motor (AM).
A comparative study on time series data-based artificial intelligence approaches for classifying cattle feeding behavior Khalid El Moutaouakil; Noureddine Falih
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.pp324-332

Abstract

Cattle feeding behavior analysis is crucial for optimizing livestock management practices and ensuring animal well-being. This study presents a comparative analysis of three models: two machine learning algorithms including random forest and support vector machine (SVM), in addition to a deep learning convolutional neural networks (CNN) model, for classifying cattle feeding behaviors (eating, ruminating, and other) using time series data generated from a 3-axis accelerometer. The results of this study highlight the performance of these methods in accurately categorizing cattle feeding behaviors and demonstrate the importance of precise and efficient livestock monitoring and contributing to the improvement of animal well-being and enhancing the overall effectiveness of livestock operations.
Chili fruits maturity estimation using various convolutional neural network architecture Najihah Mohd Hussin; Muhammad Noorazlan Shah Zainudin; Wira Hidayat Mohd Saad; Muhammad Raihaan Kamarudin; Sufri Muhammad; Muhd Shah Jehan Abd Razak
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.pp557-567

Abstract

Agricultural robots recently become popular by helping the farmer to conduct their daily chores. A slow process of picking and grading will leads to an inaccurate result thus increasing the production cost. This study represents an innovative and economical alternative for farmers who require to undergone the process of estimating their maturity categories. A total of 1,200 chili images with 256×256 pixel are used, where 840 is used for training and the remaining 360 being served for testing. The maturity is determined by measuring the length of chili structure between the calyx and apex. Various convolutional neural network (CNN) architectures are applied to learn and recognize the chili fruits into three maturity categories; immature, moderately mature, and mature. ADAM and stochastic gradient descent with momentum (SGDM) optimizers with multiple CNN architectures is capable in recognising and classifying chilli fruits with an accuracy of above 85%.
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.
Free space optical communication system in the presence of atmospheric losses Vijayashri V. Belgaonkar; Sundaraguru Ramakrishnan
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.pp159-166

Abstract

Free space optical communication is gaining importance in the field of optical communication due to its high speed and high bandwidth applications. Free space optical communication system (FSOCS) provides many benefits as compared to traditional wireless communication system and fiber optic cables. This makes this technology the reasonable extension of metropolitan area network and also provides the quick recovery during natural disaster. This system performance is limited due to the atmospheric turbulence effect and various atmospheric losses such as rain, and fog. Gamma gamma atmospheric turbulent model is used to analyze the system performance in the presence of moderate to strong atmospheric turbulence. We have designed the FSO gamma gamma turbulent model with non-return to zero (NRZ) modulation format employing wavelength division multiplexing (WDM), spatial diversity multiple input multiple output (MIMO) (8×8) at various atmospheric turbulence levels and attenuation loss of 10 dB/km at the distance of 2-4 km. Using the proposed model, the link distance is enhanced up to 4km in the presence of turbulence and atmospheric losses with minimum laser transmitted power.
Efficient deep learning architecture for the classification of diseased plant leaves Muniyandi Sadhasivam; Manoharan Kalaiselvi Geetha; James Gladson Maria Britto
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.pp198-206

Abstract

The classification of plant leaf diseases via machine learning and deep learning algorithms has a great deal of potential for enhancing agricultural operations by allowing the early and accurate diagnosis of diseases. These systems can potentially develop into useful instruments for environmentally responsible farming and increased food safety as technological advancements continue. In this work, an efficient deep learning architecture has been developed to classify the diseased plant leaves. A ten-layer architecture is designed, which includes 5-convolutional layers using different numbers of filters (32, 64, 128, 256, and 512) and for dimension reduction, five max-pooling layers are used. The PlantVillage dataset which consists of more than 50,000 plant leaf samples is used to analyze the proposed architecture's performance. The performances are evaluated across different training and testing configurations and different dropout configurations. When compared to well-known transfer learning methods using visual geometric group (VGG16), AlexNet, and GoogleNet architectures, the proposed architecture obtains a higher level of performance with 98.18% classification accuracy.
Modification of SHA-512 using Bcrypt and salt for secure email hashing Sean Eljim S. Castelo; Ruben Jolo L. Apostol IV; Dan Michael A. Cortez; Raymund M. Dioses; Mark Christopher R. Blanco; Vivien A. Agustin
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.pp398-404

Abstract

Email security, particularly against phishing, spoofing, and distributed denial-of-service (DoS) attacks, is a pressing concern given the essential role email plays in accessing various online accounts. The study introduced a modified SHA-512 algorithm, implementing additional security layers including randomly generated salt and the Bcrypt algorithm. The modified SHA-512 was comprehensively evaluated on parameters like hash construction, computational efficiency, data integrity, collision resistance, and attack resistance. The results showed its avalanche percentage exceeded the 50% target, reaching 50.08%. Experimental hash-cracking failed to decode the hashes created by the modified algorithm, verifying its protective efficiency. The algorithm also successfully demonstrated data integrity and collision resistance. This indicates that the enhanced SHA-512 algorithm is an effective, more secure hashing method, particularly applicable to email addresses.

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