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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 66 Documents
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Search results for , issue "Vol 23, No 3: September 2021" : 66 Documents clear
Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach Aditya Rajbongshi; Thaharim Khan; Md. Mahbubur Rahman; Anik Pramanik; Shah Md Tanvir Siddiquee; Narayan Ranjan Chakraborty
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1681-1688

Abstract

The acknowledgment of plant diseases assumes an indispensable part in taking infectious prevention measures to improve the quality and amount of harvest yield. Mechanization of plant diseases is a lot advantageous as it decreases the checking work in an enormous cultivated area where mango is planted to a huge extend. Leaves being the food hotspot for plants, the early and precise recognition of leaf diseases is significant. This work focused on grouping and distinguishing the diseases of mango leaves through the process of CNN. DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception all these models of CNN with transfer learning techniques are used here for getting better accuracy from the targeted data set. Image acquisition, image segmentation, and features extraction are the steps involved in disease detection. Different kinds of leaf diseases which are considered as the class for this work such as anthracnose, gall machi, powdery mildew, red rust are used in the dataset consisting of 1500 images of diseased and also healthy mango leaves image data another class is also added in the dataset. We have also evaluated the overall performance matrices and found that the DenseNet201 outperforms by obtaining the highest accuracy as 98.00% than other models.
Content-based image retrieval for fabric images: A survey Silvester Tena; Rudy Hartanto; Igi Ardiyanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1861-1872

Abstract

In recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. Generally, CBIR includes two main components, namely feature extraction and similarity measurement. Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into traditional methods and convolutional neural networks (CNN). Traditional descriptors deal with low-level features, while CNN addresses the high-level, called semantic features. Traditional descriptors have the advantage of shorter computation time and reduced system requirements. Meanwhile, CNN descriptors, which handle high-level features tailored to human perceptions, deal with large amounts of data and require a great deal of computation time. In general, the features of a CNN's fully connected layers are used for matching query and database images. In several studies, the extracted features of the CNN's convolutional layer were used for image retrieval. At the end of the CNN layer, hash codes are added to reduce  search time.
Design of fractional order PID controller for AVR system using whale optimization algorithm Layla H. Abood; Bashra Kadhim Oleiwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1410-1418

Abstract

In this paper a robust fractional order PID (FOPID) controller is proposed to control the automatic voltage regulator (AVR) system, the tuning of the controller gains are done using whale optimization algorithm (WOA) and integral time absolute error (ITAE) cost function is adopted to achieve an efficient performance. The transient analysis was done and compared with conventional PID in terms of overshoot, settling time, rise time, and peak time to explain the superiority of the proposed controller. Finally, a robustness analysis is done by adding external disturbances to the system and changing the system parameters by ±20% from its original value, the controller overcomes the disturbances signals with less than 0.25 s and faces the changes of the system values and returning the response within (0.7-1) sec and led the system to the desired response efficiently. The numerical simulations showed that the smart WOA offers satisfying results and faster response reflected clearly on the robust and stable performance of the proposed controller in improving the transient analysis of AVR system response.
Fraudulent credit card transaction detection using soft computing techniques Aishwarya Priyadarshini; Sanhita Mishra; Debani Prasad Mishra; Surender Reddy Salkuti; Ramakanta Mohanty
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1634-1642

Abstract

Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.
Advanced UI test automation (AUTA) for BIOS validation using OpenCV and OCR Eissa Abdullah Awadh Mohammed; Muslim Mustapa; Hasliza Rahim; Mohd Natashah Norizan
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1350-1356

Abstract

Basic input output system (BIOS) validation is performed on both graphical user interface (GUI) and command-line interface (CLI) by a test engineer. Keyboard and mouse are used to insert test cases commands into system under test (SUT). Test engineer monitors test cases progress on a monitor for validation. This method is time-consuming and relatively more expensive than automation. In this project we designed an independent automation system that able to mimic human interaction in BIOS validation. The approach can be divided into two main parts. The first part is the input device to enter commands into SUT and the second part is the advanced image recognizer. The keyboard and mouse emulator is used as an input device to reproduce test commands and send them to an SUT. The image analyzer algorithm is developed using OpenCV and optical character recognizer (OCR) tools to help automate some test challenges. Our result shows that advanced user interface (UI) test automation (AUTA) can perform a 125 test cases within 5 hours compared to 48 hours for a human to complete the job.
Effect of electrical discharge on the properties of natural esters insulating fluids Imran Sutan Chairul; Sharin Ab Ghani; Nur Hakimah Ab Aziz; Mohd Shahril Ahmad Khiar; Muhammad Syahrani Johal; Mohd Aizzat Azmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1281-1288

Abstract

Vegetable oils have been an alternative to mineral oil for oil-immersed transformers due to concern on less flammable, environmental-friendly, biodegradable, and sustainable resources of petroleum-based insulating oil. This paper presents the effect of electrical discharges (200 up to 1000 discharges) under 50 Hz inhomogeneous electric field on the properties (acidity, water content, and breakdown voltage) of two varieties of vegetable based insulating oils; i) natural ester (NE) and ii) low viscosity insulating fluids derived from a natural ester (NELV). Results show the water content, acidity and breakdown voltage of NE fluctuate due to applied discharges, while NELV display insignificant changes. Hence, results indicate that the low viscosity insulating fluids derived from natural ester tend to maintain their properties compared to natural ester.
Performance evaluation of SIFT against common image deformations on iban plaited mat motif images Silvia Joseph; Irwandi Hipiny; Hamimah Ujir; Sarah Flora Samson Juan; Jacey-Lynn Minoi
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1470-1477

Abstract

Decorative plaited mat is one of the many examples of rich plait work often seen on Borneo handicraft products. The plaited mats are decorated with simple and complex motif designs; each has its own special meaning and taboos. The motif designs are used as a reflection of environment and the traditional beliefs in the Iban community. In line with efforts from UNESCO’s and Sarawak Government’s, digitization, and the use of IR4.0 technologies to preserve and promote this cultural heritage is encouraged. Towards this end goal, we present a novel image dataset containing 10 Iban plaited mat motif classes. The plaited mat motifs are made of diagonal and symmetrical shapes, as well as geometric and non-geometric patterns. Classification’s accuracy using scale-invariant feature transform (SIFT) features was evaluated against 6 common image deformations: zoom+rotation, viewpoint, image blur, JPEG compression, scale and illumination, across multiple threshold values. Varying degrees of each deformation were applied to a digitally cleaned (and cropped) image of each mat motif class. We used RANSAC to remove outliers from the noisy SIFT matching result. The optimal threshold value is 2.0e-2 with a reported 100.0% matching accuracy for the scale change and zoom+rotation set.
Dual-band doherty power amplifier with improved reactance compensation Li M. Yu; Narendra K. Aridas; Tarik A. Latef
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1550-1556

Abstract

In brief, a dual-band doherty power amplifier employing reactance compensation with gallium nitride high-electron-mobility transistor technology is discussed. This design is developed for long-term evolution (LTE) frequency operation, particularly for the application of two-way radio to improve the efficiency at the back-off point from saturation output power for selected dual frequencies in the LTE bandwidth. Measurements show that the prototype board has enhanced performance at the desired frequencies, namely a saturation output power of 40.5 dBm, and 6 dB back-off efficiencies of 43% and 47%, which exhibit a gain of approximately 10 dB at 0.8 GHz and 2.1 GHz, respectively.
The rogue access point identification: a model and classification review Diki Arisandi; Nazrul Muhaimin Ahmad; Subarmaniam Kannan
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1527-1537

Abstract

Most people around the world make use of public Wi-Fi hotspots, as their daily routine companion in communication. The access points (APs) of public Wi-Fi are easily deployed by anyone and everywhere, to provide hassle-free Internet connectivity. The availability of Wi-Fi increases the danger of adversaries, taking advantages of sniffing the sensitive data. One of the most serious security issues encountered by Wi-Fi users, is the presence of rogue access points (RAP). Several studies have been published regarding how to identify the RAP. Using systematic literature review, this research aims to explore the various methods on how to distinguish the AP, as a rogue or legitimate, based on the hardware and software approach model. In conclusion, all the classifications were summarized, and produced an alternative solution using beacon frame manipulation technique. Therefore, further research is needed to identify the RAP.
Novel deep learning model for vehicle and pothole detection Gayathri K.; Thangavelu S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1576-1582

Abstract

The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural network (Faster R-CNN) with single shot detector (SSD) and you only look once (YOLO) algorithms. The proposed method shows good progress than the existing methods such as SSD and YOLO. The measure of performance evaluation is Accuracy. The proposed method shows an improvement of 5% once compared with the previous methods such as SSD and YOLO.

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