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Lie Jasa
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Majalah Ilmiah Teknologi Elektro
Published by Universitas Udayana
ISSN : 16932951     EISSN : 25032372     DOI : https://doi.org/10.24843/MITE
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Majalah Ilmiah Teknologi Elektro (MITE) is peer review journal, published twice a year by the Study Program of Magister Electrical Engineering, Faculty of Engineering, Universitas Udayana. This journal discusses the scientific works containing results of research in the field of electrical, include power systems, telecommunications, informatics, and electronics. Authors are expected to include original scientific papers in accordance with the scope of the discussion of this journal including all aspects of the theory and practice are used.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 20 Documents
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Search results for , issue "Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro" : 20 Documents clear
Prediksi Nilai Cryptocurrency Dengan Metode Bi-LSTM dan LSTM Ni Ketut Novia Nilasari; Made Sudarma; Nyoman Gunantara
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P09

Abstract

The current rapid development of technology can facilitate all human activities, so that all aspects cannot be separated from technology, including the financial sector. With the development of technology, it is also accompanied by the introduction of various investment instruments. Every time you make an investment, of course there will always be various risks that come with it, including investing in cryptocurrencies, one of which is bitcoin. Unlike conventional currencies, bitcoin is not decentralized so that its price development is not under the supervision or control of any party, where as conventional money there is a certain institution that oversees and controls its movements. This causes the price of the exchange rate of bitcoin to be inconsistent or unstable. With the prediction method, bitcoin users can determine the right time to carry out transactions. This study aims to predict bitcoin prices using the LSTM and Bi-LSTM methods. Based on the research results, the best prediction results were obtained using the Bi-LSTM method with an RMSE of 1482.73 whereas with LSTM it produces an RMSE of 1768.69 so that it can be concluded from an accuracy perspective that Bi-LSTM gives more accurate results but with Bi-LSTM it requires more resources.
RSSI Measurement Analysis of Zigbee-Based Wireless Sensor Networks in Various Topologies for Solar Panel Monitoring Dodi Setiabudi; Daris Irfan Atmaja; Dedy Wahyu Herdiyanto; Gamma Aditya Rahardi
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P14

Abstract

Abstract— Solar panels require performance monitoring when energy shortages occur in the area. Monitoring solar panels requires a detection device to detect solar panels, including the ACS712 current sensor for monitoring the output current of the solar panel, a DC voltage sensor for monitoring battery performance, and a DHT11 temperature sensor for monitoring the temperature of the solar panel storage battery. The WSN technology that can work is ZigBee communication. This ZigBee communication research uses various topology methods, including star, tree, and mesh topologies. Solar panel monitoring tests are carried out indoors and outdoors. The ZigBee communication device used is the XBee S2C device, which operates at a frequency of 2.4 GHz. From a comparison of various topologies, the mesh topology is a topology with good performance because it has the characteristic of choosing a router with the fastest path to reach the coordinator. It is proven by monitoring solar panels with an optimal distance of 80 m, which is carried out from outdoors with an RSSI value of -84 dBm, a throughput of 0.55 kbps, and a delay of 2.77 ms, producing power for 150 minutes with an average current of 0.14 A, voltage of 9.86 V, and temperature of 31.48°C. The number of disturbances and weather conditions greatly affect signal propagation, which can hamper the data transmission process. Throughput fluctuates due to uncertain weather conditions, and delay fluctuates due to a lot of interference from other radio signals. Keyworrds— Wireless Sensor Nirbael (WSN), RSSI, ZigBee, Panel Surya, Thinger.IO
Model Object Detection Neural Network Berbasis Hand Gesture Recognition sebagai Kontrol Prostesis Tangan I Made Esa Darmayasa Adi Putra; I Made Putra Arya Winata; Ilham Fauzi; Karuna Sindhu Krishna Prasad; I Wayan Widhiada
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P05

Abstract

Most people with disabilities due to accident injuries are patients after an arm amputation which can cause psychological disorders and even major trauma. The urgency of hand prosthesis functionality is increasingly needed. Hand gesture recognition (HGR) can be used to control hand prostheses, judging from the similarity in shape of objects/objects that tend to make the same hand movements. This development uses three common types of movement, namely pinch, pick, and grab. Developing a neural network model capable of implementing this concept is necessary. The neural network model developed uses the YOLOV7 and YOLOV7 tiny pre-trained networks with datasets collected through the public image data scrapping method. The dataset is 317 images and 2278 object labels with a training ratio of 80:20 testing. The training process uses the Pytorch framework with 300 epochs. The results of the loss values for each epoch show that the model is trainable in the given dataset. The training results are then evaluated by evaluating number of parameters, frame per seconds (FPS) and mean average precision (mAP) using a testing dataset. The overall results show the highest evaluation metrics in the model, with YOLOV7 pretrained with parameter number of 36,9 million, FPS of 161, and mAP of 98,11%. The model has the potential to be developed and implemented as a support for the control functionality of hand prostheses.
Model Utilisasi Dan Visualisasi Resource Menggunakan Prometheus Dan Grafana Untuk Pengelolaan Server Di Universitas Udayana Agus Permana Putra; Gede Sukadarmika; Dewa Made Wiharta
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P19

Abstract

Monitoring utilization server which refers to the use of server resources such as CPU, memory and storage is important in managing Information Technology (IT) infrastructure by network administrators in an effort to help maximize server performance, identify performance constraints and make decisions to increase efficiency servers. Monitoring is done by checking resource usage one by one on the server to find out which server resources are running. However, the more servers there are, the more time it takes to monitor so that it can interfere with the performance of network administrators. To make it easier for network administrators to monitor, researchers created a resource utilization and visualization model using Prometheus and Graphana which can display server resources without having to check directly on the server you want to monitor. The final result of the research produced a model that can visualize server resources in a dashboard without the need to check the servers one by one so that it helps network administrators work to monitor server resource usage.
Analisis Efisiensi Energi antara Lampu LED dan Lampu Konvensional (Studi kasus: Pada Hotel Cap Karoso) Lukito Pramono; Linawati Linawati; Rukmi Sari Hartati
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P10

Abstract

LED lamps are an example of an efficiency method compared to conventional lamps because of their higher energy efficiency. In this research, a comparative analysis of electrical efficiency was carried out between LED lamps and conventional lamps. This research collected data from a number of lamps used in a comparative study of LED lamps and conventional lamps at the Cap Karoso Hotel and were tested under the same conditions. The types of LED lights used are recessed downlights, track lights, pendant lights and wall scones. While the types of conventional lamps used are incandescent, halogen, fluorescent and HID. Comparisons and calculations were made in terms of watt energy and electricity costs. Based on these parameters, it can be seen the energy produced and the costs required from both types of LED lamps and conventional lamps. Based on calculations and comparisons of the two types of lamps used, it is calculated that LED lamps can save energy and cost savings by 27%.
Sistem Monitoring Debit Air Berbasis Internet of Things pada Saluran Air Tole Sutikno; Defri Rahmad Susanto; Hendril Satrian Purnama
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P01

Abstract

Manual measurement and recording of water debits has many drawbacks, including inaccuracies, time inefficiencies and requires a lot of human effort. Along with technological developments, the Internet of Things (IoT) is the right option to reduce human interaction with hardware in terms of continuous monitoring. This study aims to design a prototype of a water discharge monitoring system with precise accuracy and efficient use of electrical energy. The water discharge monitoring device system is designed based on IoT with NodeMCU as the main processor, while the water flow sensor is used as a transducer in charge of collecting data. Furthermore, the data collected by the hardware will be sent to the server with the IoT protocol via Blynk application. The results of the study show that the designed system works well, able to transmit data via IoT remotely, and has relatively high accuracy.
Analisis Penentuan Respons Twitter sebagai Media Komunikasi dan Informasi Pemerintah Berbasis Metode Rabin-Karp Luh Ayu Diah Fernita Sari; Nyoman Putra Putra Sastra; Rukmi Sari Hartati
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P15

Abstract

Social media in this digital era, especially Twitter, has an important role in the interaction between government and society. Given the wide variety of activities within government circles, the task of monitoring and responding to messages is complex and time-consuming. Therefore, this study aims to develop an effective approach in determining the appropriate response from the government to people's tweets. This study proposes using a combination of the Rabin-Karp method to quickly determine relevant responses. The Rabin-Karp method, known for its efficiency in pattern matching, is used to match tweets to a set of tweets that have already been given a response. Furthermore, the Word2Vec technique is used to improve understanding of the meaning of the text. The use of the Rabin-Karp method with the addition of the Word2Vec method shows that the response accuracy rate is 74.55%. The results of this study also show that the lower the K-Gram value, the higher the similarity value, and vice versa. These results are expected to contribute in the context of government that is responsive to societal issues discussed on Twitter.
Analisis Komputasi Paralel pada Image Encoding Framework untuk Konversi Citra Data Deret Waktu Sistem Kontrol Industri Helmy Rahadian; Muhammad Rizalul Wahid; Zaenal Arifin
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P06

Abstract

Sensors in industrial control systems send a series of data each time, known as time series data, to the controller. The data contains important information for the controller to determine the control signal for the actuator. The appearance of anomalies in time series data can be detected using the Convolutional Neural Network (CNN) method utilizing image encoding techniques such as Gramian Angular Field (GAF) and Markov Transition Field (MTF). This technique converts time series data into images through data preparation, encoding, and conversion. Dividing extensive data into many smaller segments requires repeated encoding and conversion processes. Repeated processes that are done serially take a long time, which slows down the detection of anomalies and the responses that must be taken. This research applies parallel computation with Joblib and Mpire libraries on the GAF and MTF image encoding provided by the Python-based pyts library. The n_jobs configuration determines the number of CPU logical cores used to execute the program. According to the number of CPU logic cores of the computer, applying the value of n_jobs = 8 can save an average processing time of 63% (Joblib) and 49% (Mpire), which theoretically will be able to detect anomalies that occur at least every 62.73 ms (Joblib) and 86.20 ms (Mpire) compared to 167.51 ms in serial computing.
Rancang Bangun Trainer Modul Praktikum Programmable Logic Controller Berbasis Outseal PLC Mega V.3 Standar PP Susi Susanti Gea; Pratolo Rahardjo; I Putu Elba Duta Nugraha
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P20

Abstract

The aim of this research is to design and build a learning trainer using the Outseal Mega V.3 Standard PP PLC board to support the practical in the Programmable Logic Controller + Lab course. The method used in this research is divided into three stages, such as including system design, hardware design, and software design. Testing the practical module of Programmable Logic Controller + Lab consists of three experiments, such as basic operation of Outseal PLC, logic gates, and bell quiz. Based on the result, it can be concluded that the 3 test experiments can be declared successful because the standard Outseal Mega V.3 PLC can be controlled to receive, send, and store data.
Sistem Inspeksi Visual Penempatan Label Produk Lip Cream Line Menggunakan Metode Deep Learning Moch Deny Triatmaja; Lie Jasa
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P11

Abstract

SBL (Single Bottom Labeller) machine which was still not precise for placing the bottom label batches of lip cream products. Postal operators must then check one by one to ensure product quality control so that there are bottlenecks. Therefore, this research uses the YOLOv5 deep learning method to solve this problem. The YOLOv5 algorithm uses the idea of regression, making it easier to learn generalizations, target characteristics and solve speed problems. The YOLO algorithm uses a one-stage neural network to complete the localization and classification of detected objects in real time. The core idea of YOLO is to take the entire image as network input and directly return the bounding box position and bounding box class in the output. In YOLO, each bounding box is predicted by the features of the entire image, and each bounding box contains the five predictions and a confidence, which is relative to the grid cell in the middle of the bounding box. The results of research using the YOLOv5 method can classify into three classification categories of bottom label placement, namely accept, reject and no label. In addition to classifying, this study will also send triggers to the controller if the bottom label is said to be rejected and no label to do product sorting to assist QC operators in sorting lip cream production defects.

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