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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Published by Universitas Ahmad Dahlan
ISSN : 23383070     EISSN : 23383062     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
JITEKI (Jurnal Ilmiah Teknik Elektro Komputer dan Informatika) is a peer-reviewed, scientific journal published by Universitas Ahmad Dahlan (UAD) in collaboration with Institute of Advanced Engineering and Science (IAES). The aim of this journal scope is 1) Control and Automation, 2) Electrical (power), 3) Signal Processing, 4) Computing and Informatics, generally or on specific issues, etc.
Arjuna Subject : -
Articles 601 Documents
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Terahertz Imaging Simulation Using Silicon-based Microstrip Antenna and Horn Antenna for Breast Cancer Detection Herry Tony Andhyka; Catur Apriono; Fitri Yuli Zulkifli
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.24809

Abstract

Breast cancer is one of the most common cancers in the world that cause a lot of mortality. Early cancer detection is crucial to decrease morbidity and mortality rates worldwide. Effective treatment or early intervention is crucial before the disease becomes more incurable. This research contributes to proposing a THz imaging system for early cancer detection, especially breast cancer, by using the benefit of THz radiation. Some approaches are made differently from the previous research, such as the imaging method, the antenna type, and the material for the antenna with the expectation of producing an efficient system and better imaging results. The system consists of one microstrip antenna as a transmitter, 25 horn antenna as a receiver and a breast tissue model. All antenna is designed to meet the requirement specification. The receiver antenna will receive power from the transmitter which will vary due to the absorption of the breast model. The received power will be visualized into a 2D color image. The simulation was able to visualize an image of the breast tissue model. Received power varies from -16.280 dB to -55.241 dB which leads to different color levels to represent the model. Antenna radiation patterns also take a role to cause the phenomenon occurred that leads to differentiation of the breast tissue type. Based on the results, this research has able to simulate a THz imaging system for breast cancer. Further modification to the system can be done to improve the imaging results.
LSTM Network Hyperparameter Optimization for Stock Price Prediction Using the Optuna Framework Edi Ismanto; Vitriani Vitriani
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.24944

Abstract

This article has been retracted by the publisher.This article has been retracted because of misconduct and plagiarism. The document and its content have been removed from the Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, and reasonable effort should be made to remove all references to this article.
Deep Learning Approach For Sign Language Recognition Bambang Krismono Triwijoyo; Lalu Yuda Rahmani Karnaen; Ahmat Adil
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25051

Abstract

Sign language is a method of communication that uses hand movements between fellow people with hearing loss. Problems occur when communication between normal people with hearing disorders, because not everyone understands sign language, so the model is needed for sign language recognition. This study aims to make the model of the introduction of hand sign language using a deep learning approach. The model used is Convolutional Neural Network (CNN). This model is tested using the ASL alphabet database consisting of 27 categories, where each category consists of 3000 images or a total of 87,000 images of 200 x 200 pixels of hand signals. First is the process of resizing the image input to 32 x 32 pixels. Furthermore, separating the dataset for training and validation respectively 75% and 25%. The test results indicate that the proposed model has good performance with a value of 99% accuracy. Experiment results show that preprocessing images using background correction can improve model performance.
MongoDB Based Real-Time Monitoring Heart Rate Using Websocket For Remote Healthcare Emin Guney; Gamze Agirtas; Cuneyt Bayilmis
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25052

Abstract

With the gradual development of Industry 4.0, the internet of things (IoT) concept has become an even more current and fundamental study topic. Consisting of devices and objects with communication capability, IoT is a network that uses internet infrastructure, especially for data collection, display, decision-making, control, and optimization of processes. Recently, patient tracking systems have become even more critical with Covid19 and have diversified in health for IoT topics such as biomedical device tracking and disease diagnosis. Within the scope of this study, a prototype of a patient tracking system was developed over the sensor in order to contribute to the biomedical field. We aimed to observe real-time heart rates using WebSockets to demonstrate its use in the medical field via the web application. Monitoring the heart rate using a WebSocket can help doctors make faster and better diagnoses. The current technology study instantly collected the patient's heart rhythm with the pulse sensor. The pulse data collected in real time was then transferred to a web platform with the NodeMCU ESP 8266 board. With this platform, the patient was monitoring in real-time. With the opportunities provided by the study, the doctor implemented an application monitors the instantaneous pulse of the patients.
Deep Learning-Based SOLO Architecture for Re-Identification of Single Persons by Locations Rotimi-Williams Bello; Chinedu Uchechukwu Oluigbo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25059

Abstract

Analyzing and judging of captured and retrieved images of the targets from the surveillance video cameras for person re-identification have been a herculean task for computer vision that is worth further research. Hence, re-identification of single persons by locations based on single objects by locations (SOLO) model is proposed in this paper. To achieve the re-identification goal, we based the training of the re-identification model on synchronized stochastic gradient descent (SGD). SOLO is capable of exploiting the contextual cues and segmenting individual persons by their motions. The proposed approach consists of the following steps: (1) reformulating the person instance segmentation as: (a) prediction of category and (b) mask generation tasks for each person instance, (2) dividing the input person image into a uniform grids, i.e., G×G grid cells in such a way that a grid cell can predict the category of the semantic and masks of the person instances provided the center of the person falls into the grid cell and (3) conducting person segmentation. Discriminating features of individual persons are obtained by extraction using convolution neural networks. On person re-identification Market-1501 dataset, SOLO model achieved mAP of 84.1% and 93.8% rank-1 identification rate, higher than what is achieved by other comparative algorithms such as PL-Net, SegHAN, Siamese, GoogLeNet, and M3L (IBN-Net50). On person re-identification CUHK03 dataset, SOLO model achieved mAP of 82.1 % and 90.1% rank-1 identification rate, higher than what is achieved by other comparative algorithms such as PL-Net, SegHAN, Siamese, GoogLeNet, and M3L (IBN-Net50). These results show that SOLO model achieves best results for person re-identification, indicating high effectiveness of the model. The research contributions are: (1) Application of synchronized stochastic gradient descent (SGD) to SOLO training for person re-identification and (2) Single objects by locations using semantic category branch and instance mask branch instead of detect-then-segment method, thereby converting person instance segmentation into a solvable problem of single-shot classification.
Cheating Prevention in E-proctoring Systems Using Secure Exam Browsers: A Case Study Hussein Mahmood Mohammed; Qutaiba Ibrahem Ali
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25094

Abstract

In this research, a case study has been conducted to analyze the possibility of preventing cheating or reducing it by using one of the lockdown browsers during the exam. An e-exam has been created using Moodle platform, and the exam has been conducted with the Safe Exam Browser (SEB) as a restriction program at one time and without it at another time, and an analysis has been made of the extent of the possibility of cheating during the exam for both cases. Wireshark and Registry Changes View programs have been used to observe the possibility of opening programs and applications or the ability of the examinee to use Windows tools during the exam. The use of Wireshark and Registry Changes View software showed high effectiveness in analyzing the examinee's device data and identifying the examinee's activity during the electronic exam, to give a clear perception of the possibility of preventing access to resources and applications on the examinee's device. The researchers concluded that the use of lockdown browsers is very necessary to prevent the examinee from accessing the resources on his device, which leads to a significant reduction in cheating during the electronic exam. The research contributions are two, the first one is the use of analyzing programs to observe the examinee`s activity during the exam, and the second one is presenting the lockdown browsers` features.
A Deep Neural Network Model for Realtime Semantic-Segmentation Video Processing supported to Autonomous Vehicles Trung-Nguyen Bui; Hanh Phan-Xuan; Thuong Le-Tien
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25120

Abstract

Traffic congestion has been a huge problem, especially in urban area during peak hours, which causes a major problem for any unmanned/autonomous vehicles and also accumulate environmental pollution. The solutions for managing and monitoring the traffic flow is challenging that not only asks for performing accurately and flexibly on routes but also requires the lowest installation costs. In this paper, we propose a synthetic method that uses deep learning-based video processing to derive density of traffic object over infrastructure which can support usefull information for autonomous vehicles in a smart control system. The idea is using the semantic segmentation, which is the process of linking each pixel in an image to a class label to produce masked map that support collecting class distribution among each frame. Moreover, an aerial dataset named Saigon Aerial with more than 110 samples is also created in this paper to support unique observation in a biggest city in Vietnam, HoChiMinh city. To present our idea, we evaluated different semantic segmentation models on 2 datasets: Saigon Aerial and UAVid. Also to track our model’s performance, F1 and Mean Intersection over Union metrics are also taken into account. The code and dataset are uploaded to Github and Kaggle repository respectively as follow: Saigon Aerial Code, Saigon Aerial dataset.
Low-Cost Active Monitoring of Attendance using Passive RFID Technology Wael A Farag; Mohamed Abouelela
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25168

Abstract

In this paper, a smart attendance system for students attending schools is proposed. The proposed attendance system is based on Radio Frequency Identification (RFID) technology to facilitate automation and convenience. The proposed RFID Attendance System (RFID-AS) should be used by school administration to ensure safety for students as well as using it for grading and evaluation purposes. After careful study, passive RFID technology is selected to be used by the proposed system for its reasonable cost. The main components of the system are an RFID tag, an RFID reader, Visual Studio (XAF Tool), and SQL Server to compare the data from the RFID tag with the students’ database to record attendance automatically. A Graphical User Interface (GUI) is developed using Visual Studio (XAF Tool) to allow parents and school faculty to log in and browse the students’ records. Students will pass the classroom door, which will have an integrated RFID reader device to read their RFID. The paper discusses the design of the solution as well as the testing scenarios.
Classification of Leukocytes Using Meta-Learning and Color Constancy Methods Eduardo Rivas-Posada; Mario I. Chacon-Murguia; Juan Alberto Ramirez-Quintana; Carlos Arzate-Quintana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25192

Abstract

In the human healthcare area, leukocytes are very important blood cells for the diagnosis of different pathologies, like leukemia. Recent technology and image-processing methods have contributed to the image classification of leukocytes. Especially, machine learning paradigms have been used for the classification of leukocyte images. However, reported models do not leverage the knowledge produced by the classification of leukocytes to solve similar tasks. For example, the knowledge can be reused to classify images collected with different types of microscopes and image-processing techniques. Therefore, we propose a meta-learning methodology for the classification of leukocyte images using different color constancy methods involving previous knowledge. Our methodology is trained with a specific task at the meta-level, and the knowledge produced is used to solve a different task at the base-level. For the meta-level, we implemented meta-models based on Xception, and for the base-level, we used support vector machine classifiers. Besides, we analyzed the Shades of Gray color constancy method commonly used in skin lesion diagnosis and now implemented for leukocyte images. Our methodology, at the meta-level, achieved 89.28% for precision, 95.65% for sensitivity, 91.78% for F1-score, and 94.40% for accuracy. These scores are competitive regarding the reported state-of-the-art models, especially the sensitivity which is very important for imbalanced datasets, and our meta-model outperforms previous works by +2.25%. Additionally, for the basophil images that were acquired from a chronic myeloid leukemia-positive sample, our meta-model obtained 100% for sensitivity. Moreover, we present an algorithm that generates a new conditioned output at the base-level obtaining highly competitive scores of 91.56% for sensitivity and F1 scores, 95.61% for precision, and 96.47% for accuracy. The findings indicate that our proposed meta-learning methodology can be applied to other medical image classification tasks and achieve high performances by reusing knowledge and reducing the training time for new similar tasks.
Optimized PID-Like Neural Network Controller for Single-Objective Systems Gunawan Dewantoro; Johanes Nico Sukamto; Fransiscus Dalu Setiaji
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25237

Abstract

The utilization of intelligent controllers becomes more prevalent as the hype of Industry 4.0 arises. Artificial neural network (ANN) exhibits the mapping ability and can estimate the output by means of either interpolation or extrapolation. These properties are sought to supersede the classical controllers. In this study, the ANN establishment was initiated by collecting dataset from the input and output of a well-known PID controller. The dataset was trained using a set of control factor combinations, including the number of neurons, the number of hidden layers, activation functions, and learning rates. Two kinds of ANN controllers were investigated, including one-input and three-input ANN. The testing was conducted under normal and uncertain conditions. These uncertainties include external disturbances, plant variations, and setpoint variations. The integral absolute error (IAE) was selected as the single objective to assess. The simulation results show that the response of three-input ANN controllers could yield smaller IAE at their best combinations under most kinds of conditions. Besides, the three-input ANN outperforms the one-input ANN both qualitatively and quantitatively. These facts might lead to a broader utilization of ANN as controllers.

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