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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 505 Documents
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Dynamic Voltage Restorer for Mitigation of Voltage Sags Due to 3 Phase Motor Starts Based on Artificial Neural Networks Sujito Sujito; Langlang Gumilar; Imron Ridzki; Abdullah Iskandar Syah; Moh. Zainul Falah
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.v9i1.25897

Abstract

The Direct On-Line (DOL) process of starting a high-power 3-phase induction motor causes voltage sags in the distribution system that is connected to one point of common coupling (PCC). Voltage sag can cause damage and failure of sensitive loads. This article analyzes and proposes a simulation of voltage sag recovery using a Dynamic Voltage Restorer (DVR) based on an Artificial Neural Network (ANN). ANN is used as a detector and regulator of the voltage compensation value. In this study, a 3-phase induction motor will be connected to a sensitive load, and the DVR will be placed in series with a voltage source or PCC with a sensitive load. The simulation test system uses Simulink-Matlab R2016a with different configurations of induction motor parameters. Based on the simulation results show that the parameters of the 3-phase induction motor cause the depth and duration of the voltage sag. DVR with ANN control can detect and compensate for a voltage sag of 0.5 pu so that the voltage will be normal to 1 pu.
Interactive Solar System Learning Media Using the Raspberry Pi 3B Mochamad Fajar Wicaksono; Myrna Dwi Rahmatya; Syahrul Syahrul; Sri Nurhayati
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.v9i2.25948

Abstract

The current learning solar system process uses solar system props. This research aimed to create an interactive solar system learning tool for elementary school students. With this tool, students can learn about the planets in the solar system in learning mode. In addition, there is a question mode to test students' knowledge abilities. This research's contribution was to provide an engaging way for sixth-grade elementary school students to learn about and recognize the planets in the solar system. The method used in this research is the experimental method. The primary part of this system is the Raspberry Pi. In this tool, there are two modes: learning mode and question mode. The learning mode involves the input button for eclipse mode and the LDR sensor as a trigger for activating the DC motor and reading solar system material using gTTS. The question mode involves a question bank on the web application, gTTS for reading questions, and speech recognition for processing answers given by students.   The teacher can add, change, or delete questions and learning materials through the web application. The test on the learning tool is 100% successful. In learning mode, the device can read input from the LDR sensor and provide sound output, and in question mode, the device will ask questions, receive answers in voice form and then process the response based on program scenarios. On the other side, Based on UAT results from 20 sixth-grade elementary school student,  95.14% of student agreed that solar system learning media and quiz features make the learning process more engaging, easy to use, help students understand solar system material, and can be used as a learning tool.
Pilates Pose Classification Using MediaPipe and Convolutional Neural Networks with Transfer Learning Kenneth Angelo Tanjaya; Mohammad Farid Naufal; Heru Arwoko
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.v9i2.25975

Abstract

A sedentary lifestyle can lead to heart disease, cancer, and type 2 diabetes. An anaerobic exercise called pilates can address these problems. Although pilates training can provide health benefits, the heavy load of pilates poses may cause severe muscle injury if not done properly. Surveys have found that many teenagers are unaware of the movements in pilates poses. Therefore, a system is needed to help users classify pilates poses accurately. MediaPipe is a system that accurately extracts the real time human body skeleton. Convolutional Neural Network (CNN) with transfer learning is an accurate method for image classification. There have been several studies investigated pilates poses classification. However, there is still no research applies the MediaPipe as a skeleton feature extractor and CNN with a transfer learning to classify pilates poses. In addition, previous research still does not implement the pilates poses classification in real-time. Based on this problem, this study creates a system using MediaPipe as a feature extractor and CNN with transfer learning as a real-time pilates poses classifier. This system runs on a mobile device and gets information from a camera sensor. The results from MediaPipe then be classified by pre-trained CNN architectures with transfer learning: MobileNetV2, Xception, and ResNet50. The best model was obtained by MobileNetV2, which had an f1 score of 98%. Ten people who didn't know much about Pilates also tested the system. They all agreed that the app could accurately identify Pilates poses, make people more interested in Pilates, and help them learn more about Pilates.
Classification and Clustering of Internet Quota Sales Data Using C4.5 Algorithm and K-Means Eriska Vivian Astuti; Asep Afandi; Dwi Marissa Effendi
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.v9i2.25970

Abstract

The number of restrictions or limits on internet use is known as internet quota. When you use internet data for a short time, you usually run out of bandwidth. In the Candimas South Abung area, many quotas have been sold in various variants. Visitors to quota outlets have access to various kinds of quota references that they can buy. Apart from guaranteeing the quality of the quotas sold, sales always increase every year, especially in the various quota variants. Based on quota data for 2019 to 2022. This study aims to analyze internet quota sales statistics in the Candimas area between 2019 and 2022. In 2021-2022 the classification produces an accuracy of up to 100% where the best-selling data dominates while clustering remains at the same figure, namely 19 data are very salable, 43 data are lacking sold, and 178 data did not sell. We use the C4.5 classification algorithm and K-Means clustering to identify patterns in the data and provide insight into which brand quotas are the most popular. Our findings can help Xena Cell counter owners make informed decisions about which quota to add or remove to optimize sales and minimize losses.
Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset Joanito Agili Lopo; Kristoko Dwi Hartomo
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.v9i2.25929

Abstract

Detecting fraud in the healthcare insurance dataset is challenging due to severe class imbalance, where fraud cases are rare compared to non-fraud cases. Various techniques have been applied to address this problem, such as oversampling and undersampling methods. However, there is a lack of comparison and evaluation of these sampling methods. Therefore, the research contribution of this study is to conduct a comprehensive evaluation of the different sampling methods in different class distributions, utilizing multiple evaluation metrics, including , , , Precision, and Recall. In addition, a model evaluation approach be proposed to address the issue of inconsistent scores in different metrics. This study employs a real-world dataset with the XGBoost algorithm utilized alongside widely used data sampling techniques such as Random Oversampling and Undersampling, SMOTE, and Instance Hardness Threshold. Results indicate that Random Oversampling and Undersampling perform well in the 50% distribution, while SMOTE and Instance Hardness Threshold methods are more effective in the 70% distribution. Instance Hardness Threshold performs best in the 90% distribution. The 70% distribution is more robust with the SMOTE and Instance Hardness Threshold, particularly in the consistent score in different metrics, although they have longer computation times. These models consistently performed well across all evaluation metrics, indicating their ability to generalize to new unseen data in both the minority and majority classes. The study also identifies key features such as costs, diagnosis codes, type of healthcare service, gender, and severity level of diseases, which are important for accurate healthcare insurance fraud detection. These findings could be valuable for healthcare providers to make informed decisions with lower risks. A well-performing fraud detection model ensures the accurate classification of fraud and non-fraud cases. The findings also can be used by healthcare insurance providers to develop more effective fraud detection and prevention strategies.
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.
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
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.
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
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.

Page 26 of 51 | Total Record : 505

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