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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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Implementation of Machine Learning and Deep Learning Models Based on Structural MRI for Identification Autism Spectrum Disorder Dimas Chaerul Ekty Saputra; Yusuf Maulana; Thinzar Aung Win; Raksmey Phann; Wahyu Caesarendra
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26094

Abstract

Autism spectrum disorder (ASD) is a developmental disability resulting from neurological disparities. People with ASD frequently struggle with communication and social interaction, as well as limited or repetitive interests or behaviors. People with ASD may also have unique learning, movement, and attention styles. ASD sufferers can be interpreted as 1 in every 100 individuals in the globe having ASD. Abilities and requirements of autistic individuals vary and may change over time. Some autistic individuals are able to live independently, while others have severe disabilities and require lifelong care and support. Autism frequently interferes with educational and employment opportunities. Additionally, the demands placed on families providing care and assistance can be substantial. Important determinants of the quality of life for persons with autism are the attitudes of the community and the level of support provided by local and national authorities. Autism is frequently not diagnosed until adolescence, despite the fact that autistic traits are detectable in early infancy. This study will discuss the identification of Autism Spectrum Disorders using Magnetic Resonance Imaging (MRI). MRI images of ASD patients and MRI images of patients without ASD were compared. By employing multiple machine learning and deep learning techniques, such as random forests, support vector machines, and convolutional neural networks, the random forest method achieves the utmost accuracy with 100% using confusion matrix. Therefore, this technique is able to optimally identify ASD through MRI.
Implementation of Personal Protective Equipment Detection Using Django and Yolo Web at Paiton Steam Power Plant (PLTU) Khoirun Nisa'; Fathorazi Nur Fajri; Zainal Arifin
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26131

Abstract

Work accidents can occur at any time and unexpectedly, so work safety is associated with health because the work safety system in Indonesia is related to the K3 (Occupational Safety and Health) program. To create a safe and healthy work environment, occupational safety and health management are implemented to avoid work accidents by requiring every worker to use Personal Protective Equipment (PPE). This research aims to develop an immediate detection system for violations of Personal Protective Equipment (PPE) in the workplace using the Yolov8 Method and the Django web-based user interface framework. Yolov8 is one of the latest deep-learning object identification models while Django is the most popular Python developer framework. The system is designed to improve workplace safety and prevent accidents by monitoring compliance with PPE requirements. The research methodology involves literature study, image data collection, preprocessing, model training, and system deployment using the Django framework. There are four classes of detection based on the bounding box according to the specified color, the use of helmets and safety vests based on the red bounding box for helmets and blue for vests while when helmets and safety vests are not being used, based on green and yellow bounding boxes. The system successfully detected four PPE classes with an average accuracy of 82.3% from 230 test data, a mAP50 value of 81.6%, a precision value of 90.3%, and a recall value of 75.1%. The findings from this study indicate that the developed system can effectively improve occupational safety and health management. However, there is a detection error factor caused by the lighting and specifications of the camera used. Future research can focus on integrating the system with other work safety systems to provide a comprehensive solution for accident prevention.
Application of SMOTE to Handle Imbalance Class in Deposit Classification Using the Extreme Gradient Boosting Algorithm Dina Arifah; Triando Hamonangan Saragih; Dwi Kartini; Muliadi Muliadi; Muhammad Itqan Mazdadi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26155

Abstract

Deposits became one of the main products and funding sources for banks and increasing deposit marketing is very important. However, telemarketing as a form of deposit marketing is less effective and efficient as it requires calling every customer for deposit offers. Therefore, the identification of potential deposit customers was necessary so that telemarketing became more effective and efficient by targeting the right customers, thus improving bank marketing performance with the ultimate goal of increasing sources of funding for banks. To identify customers, data mining is used with the UCI Bank Marketing Dataset from a Portuguese banking institution. This dataset consists of 45,211 records with 17 attributes. The classification algorithm used is Extreme Gradient Boosting (XGBoost) which is suitable for large data. The data used has a high-class imbalance, with "yes" and "no" percentages of 11.7% and 88.3%, respectively. Therefore, the proposed solution in the research, which focused on addressing the Imbalance Class in the Bank marketing dataset, was to use Synthetic Minority Over-sampling (SMOTE) and the XGBoost method. The result of the XGBoost study was an accuracy of 0.91016, precision of 0.79476, recall of 0.72928, F1-Score of 0.56198, ROC Area of 0.93831, and AUCPR of 0.63886. After SMOTE was applied, the accuracy was 0.91072, the precision was 0.78883, the recall was 0.75588, F1-Score was 0.59153, ROC Area was 0.93723, and AUCPR was 0.63733. The results showed that XGBoost and SMOTE could outperform other algorithms such as K-Nearest Neighbor, Random Forest, Logistic Regression, Artificial Neural Network, Naïve Bayes, and Support Vector Machine in terms of accuracy. This study contributes to the development of effective machine learning models that can be used as a support system for information technology experts in the finance and banking industries to identify potential customers interested in subscribing to deposits and increasing bank funding sources.
Design and Implementation of Embedded Biometric-Based Access Control System with Electronic Lock using Raspberry Pi Youssef Elmir; Abdeldjalil Abdelaziz; Mohammed Haidas
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26162

Abstract

This paper presents the design and implementation of an improved access control system based on biometric recognition, utilising Raspberry Pi technology. The proposed system aims to enhance the security of the existing electronic lock-based system at the SGRE-Lab of University Tahri Mohammed of Bechar in Algeria. The proposed system employs multimodal biometrics, integrating facial recognition and speaker verification for personal identification. Following initial verification by the electronic lock, the system captures the user's face through a camera to perform facial recognition. In cases where the user's identity is uncertain, a voice recognition module prompts the user to say a secret word, confirming their identity through the microphone. The combination of these two biometric techniques ensures access is granted, and an access log is recorded, with an accompanying notification sent to the administrator via SMS. As technical contribution, this paper presents the design and implementation of an embedded biometric-based access control system using Raspberry Pi, which includes the integration with an electronic lock and digicode, in the other hand, a second innovation contribution by combining biometric-based authentication with Raspberry Pi technology, this paper introduces an innovative approach to access control systems that provides a more secure and reliable means of access control than traditional methods based on keys or passwords. An overview of the proposed system's architecture is provided, its operation modes, and necessary hardware and software requirements. The promising obtained results of demonstrations show a notable improvement in security levels, characterized by reduction of false acceptances, however, the paper acknowledges that users unfamiliar with the biometric system may face challenges, potentially leading to false rejections. Future work should focus on mitigating these challenges and addressing user familiarity issues.
Descriptive Analysis and ANOVA Test with File Sending on Computer Networks Attacked with Rogue's Dynamic Host Configuration Protocol (DHCP) Hero Wintolo; Yuliani Indrianingsih; Wahyu Hamdani; Syafrudin Abdie
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26167

Abstract

The requirement for a computer that is physically connected to a computer network to be able to access existing resources on a computer network in the form of an IP address obtained statically or dynamically. On a static IP address, there are not many problems that arise because it is loaded directly into the computer, while for a dynamic IP address, security problems arise in the form of a dynamic IP address sharing server in the form of DHCP Rogue. The configuration that is added to the first router when the network is hit by a DHCP rogue attack is to configure the main router, in this case, the first router, and the switch used as a connecting device between computers. configuration on both switches is done by snooping trust which is useful for securing IP addresses to avoid IP attackers. This research was conducted to find out if a computer network with a dynamic IP address was attacked by sending files between computers. Files with the longest sending time indicate an attack on the computer network. The method used in this study is the ANOVA test with descriptive-based analysis. Based on the results of the analysis, it is known that the average file transfer time on networks affected by DHCP Rogue is higher than the average file transfer time on normal and mitigated networks, and the significant value of the ANOVA test results has a value of 0.004. In general, it can be concluded that there are differences in data transfer when the network is normal, the network is subject to DHCP Rogue, and the network has been mitigated with DHCP Rogue.
Design of a Device for Utilizing Hazardous and Toxic Waste as Fuel For a Stove (Burner) with a PID Control System Weny Findiastuti; Ach Dafid; Rullie Annisa
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26179

Abstract

Used oil waste is treated by spraying oil into the combustion furnace using wind from a blower. Before pouring oil into the furnace, it is necessary to manually heat it up to 300°C to burn off the fat. The controls in this study control the valve and blower to reach the desired temperature up to 800°C by utilizing a k-type thermocouple temperature sensor to detect temperature. This burner stove research uses the PID control method because it has a response that is fast enough to reach the desired temperature. The PID method is a controller that can reduce the error rate in a system to provide an output signal with a fast response, small error rate, and small overshoot. One of the contributions to this research is the importance of reducing the negative impact of hazardous and toxic waste on the environment and being an alternative solution in dealing with hazardous and toxic waste. In other conditions, this research makes an important contribution to the development of technology for processing and utilizing hazardous and toxic waste materials. Hopefully, this research will help contribute ideas and thoughts on preserving the environment and utilizing existing resources more efficiently. From the results of this study, based on the number of test results from seven tests, it can be said that the conditions are optimal because the fire produced from used oil does not contain black smoke. Meanwhile, the maximum temperature generated was 809 °C at the 73rd second, and the temperature continued to fall at the 94th second, and so on until it reached stability. These conditions indicate that the fuel speed ratio (used oil) and applied air pressure have started to improve so that the temperature is stable at 806 °C. In conclusion, the optimum test results at a flame temperature of 806 °C, the resulting flame does not produce black smoke, so the combustion of the used evaporative lubricant produces much cleaner exhaust emissions.
Website Vulnerability Analysis of AB and XY Office in East Java Muchammad Zaidan; Febyola Noeraini; Zamah Sari; Denar Regata Akbi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26183

Abstract

Study this aim for analyze and identify vulnerability existing security on AB and XY Service Websites in East Java. Contribution study this is give more understanding deep about type vulnerability specific security and its impact to field website security. Method research used involve data scanning, analysis vulnerabilities, and Brute Force experiments. A total of 2 samples of AB and XY Service Websites were analyzed For identify existing vulnerabilities the data. However so, necessary noted that method study this own a number of limitations. First, size sample used possible limited to AB and XY Service Websites in East Java only, so generalization results study against other websites needs done with be careful. Second, analysis statistics used only covers analysis descriptive, so study this not yet investigate linkages between existing variables. Although thus, results study show exists necessary weaknesses and vulnerabilities corrected on AB and XY Service Websites. A number of findings covers problem website configuration and handling vulnerability that is not adequate. With highlight specific susceptibility, research this give more understanding deep about threat security faced by AB and XY Service Websites. In context field website security, research this own implication important. With understand existing vulnerabilities on AB and XY Service Websites, steps repair proper security can take for protect sensitive data and improve protection security in a manner whole. Kindly whole, research this identify and analyze vulnerability security on AB and XY Service Websites, as well give more understanding Specific about type existing vulnerabilities. Although there are limitations in method study this is the result still give valuable insight in field website security and can become base for repair more security effective and more data protection on both the AB and XY Service Websites.
Slaging Analysis Based on Boiler Wall Temperature at PLTU Paiton Unit 3 Muhammad Hasan Basri; Tijaniyah Tijaniyah; Risto Moyo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26202

Abstract

Slagging on boiler surfaces in power plants is still a serious problem to reduce thermodynamic efficiency and threaten the operation of generating units. In this study, an innovative slagging diagnosis method based on analysis of vibration signals from tube panels is proposed to monitor slagging conditions. We analyze boiler wall temperature under various slagging conditions and air velocity at PLTU Paiton unit 3. Root Mean Square (RMS) in the time domain and decomposition in the frequency domain are used to extract features from vibration signals and predict slagging conditions without turning them off in the future. It was found that the RMS value of the tube panel signal decreased with increasing slagging weight, especially at low air speeds. The relative signal energy at a certain frequency will experience significant changes. In order to verify the experimental results on changes in the tube panel vibration signal features under various slagging conditions, we have successfully demonstrated our laboratory results through analysis of the tube panel vibration signal. This indicates that the vibration signal of the tube panel between the heater and the furnace wall panel can be collected and used for the diagnosis of slagging in the walls of a coal boiler. Our study is promising for the prediction of slagging and further mitigating the risk caused by slagging of exchange panels in boilers
Classification of Corn Seed Quality Using Convolutional Neural Network with Region Proposal and Data Augmentation Budi Dwi Satoto; Rima Tri Wahyuningrum; Bain Khusnul Khotimah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26222

Abstract

Corn is one of the essential commodities in agriculture. All components of corn can be utilized and accommodated for the benefit of humans. One of the supporting components is the quality of corn seeds, where a specific source has the physiological qualities to survive. The problem is how to get information on the quality of corn seeds at agricultural locations and get information through the physical image alone. This research tries to find a solution to obtain high accuracy in classifying corn kernels using a convolutional neural network because there is a profound training process. The problem with convolutional neural networks is the training process takes a long time, depending on the number of layers in the architecture. This research contributes to increasing the computing time with the proposed contribution by adding Region proposals with a convex hull to use on a custom layer. The method's purpose is a region proposal area with a convex hull to increase the focus on the convolution multiplication process. It affected reducing unnecessary objects in background images. A custom layer architecture by maintaining the priority layer is an option to get a shorter computational time in constructing a model. In addition, the architecture that is made still considers the stability of the training process. The results on the classification of corn seeds are obtained by a model with an average accuracy of 99.01%—the Computational training time to get the model is 2 minutes 30 seconds. The average error value for MSE is 0.0125, RMSE is 0.118, and MAE is 0.0108. The experimental data testing process has an accuracy ranging from 77% -99%. In conclusion, using region proposals can increase accuracy by around 0.3% because focused objects assist the convolution process
Performance Evaluation of Sliding Mode Control (SMC) for DC Motor Speed Control Dimas Dwika Saputra; Alfian Ma'arif; Hari Maghfiroh; Muhammad Ahmad Baballe; Angelo Marcelo Tusset; Abdel-Nasser Sharkawy; Rania Majdoubi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26291

Abstract

DC motor is an industrial motor that is practical for many applications and implementations. However, the speed of a DC motor often decreases because of the given load, thus causing it to be unstable and inconstant. In addition, parameter uncertainty is another issue of DC motors. The performance of the system will be impacted by the uncertainty. Therefore, in this study, SMC is used as speed control of the DC motor since it can handle non-linear plants. The performance also compares with PID to know the effectiveness of the SMC method in DC motor speed control. This study proposes a hardware design and implementation of DC motor angular speed control on Arduino UNO as an embedded control system. The performance comparison analysis results proved that both controllers could perform well. However, both controllers need further fine-tuning. There are still overshoot and steady-state errors for PID and SMC, respectively. In future work, the optimization method can be used to find the optimal gain or by combining it with an adaptive algorithm.

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