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INDONESIA
Jurnal Teknik Elektro
Published by Universitas Negeri Semarang
ISSN : 14110059     EISSN : 25491571     DOI : http://dx.doi.org/10.15294/jte
Core Subject : Engineering,
Electrical & Electronics Engineering
Arjuna Subject : -
Articles 482 Documents
 45   46   47   48   49 
Penerapan Algoritma Floyd-Warshall dalam Menentukan Rute Terpendek pada Pemodelan Jaringan Pariwisata di Kota Semarang
Jurnal Teknik Elektro Vol 8, No 1 (2016): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v8i1.8791

Abstract

Kota Semarang merupakan kota yang berpotensi untuk dikembangkan menjadi daerah tujuan wisata. Informasi tentang obyek wisata sangat dibutuhkan oleh para wisatawan salah satunya adalah informasi rute wisata terpendek menuju beberapa obyek wisata populer di Kota Semarang, sehingga dapat mengefisiensi waktu, jarak, dan biaya. Tujuan dari penelitian ini adalah menerapkan algoritma Floyd-Warshall dalam mencari jalur terpendek jaringan pariwisata Kota Semarang. Peta Kota Semarang ditransformasikan kedalam bentuk diagram grafik. Algoritma Floyd-Warshall diterapkan dalam perhitungan bobot path dari diagram grafik untuk mencari rute terpendek ke obyek wisata populer di Kota Semarang. Sistem pencarian rute terpendek pariwisata Kota Semarang ini dapat menjadi media promosi pariwisata Kota Semarang dan dapat dimanfaatkan sebagai alternatif rute perjalanan oleh wisatawan.
Rancang Bangun Antena Bowtie pada Video Sender sebagai Pemancar TV Streaming
Jurnal Teknik Elektro Vol 10, No 2 (2018): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v10i2.16403

Abstract

A streaming television usually broadcasted through internet and can be watched on computer or mobile phone. However it can also be seen through television using video sender as the transmitter. To transmit it, of course, a suitable antenna is needed, in this case a bowtie antenna is selected. This research describe the design of a bowtie antenna that will be used as a transmitter antenna on a video sender. This bowtie antenna is designed by using an aluminum plate works on frequency of 655 MHz and has a return loss value of -21.127 dB, VSWR 1.192, 5.11 dB gain and bi-directional radiation pattern. The analysis process is carried out by analyzing the data taken from the audio and video results received in television as well as data from the measurement of signal strength captured by Register Transfer Level Software Define Radio (RTL SDR) and dipole antennas. Measurement results will be displayed in the sdrSharp software. The results of this measurement will be associated with field strength which is related to television signals. This bowtie antenna can transmit audio and video up to 30 m and has an average field strength value in Line of Sight (LOS) conditions of 120.3208 dBμV / m and at Non Line of Sight (NLOS) conditions of 123.5014 dBμV / m which is in accordance with the field strength standard in Indonesia that regulated in Perkominfo No. 31 Tahun 2014 which is above 70 dBμV / m in band V.
A Minimum Error-Based PCA for Improving Classifier Performance in Detecting Financial Fraud
Jurnal Teknik Elektro Vol 14, No 1 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i1.35787

Abstract

The main challenge of data mining approaches to detect fraud in financial transaction data is the imbalance of data classes in available datasets, with a much smaller fraud class proportion than the non-fraud. This imbalance affects the f1-score to be low due to unbalanced precision and recall. Therefore, the model can predict one class well, but it does not apply to another class. In addition, the lengthy training time duration and high computational resource requirements in implementing data mining also make them a particular concern. Therefore, solely handling imbalanced data is still insufficient to produce the expected performance. Reduction of data dimensions can be a solution to increase the speed of the process. However, this method actually reduces the classifier’s performance when it comes to classification. Furthermore, this study intends to improve the performance of the data mining approach based on the Support Vector Machine (SVM) classifier aiming at detecting financial fraud transactions. The SVM performance was refined by tuning the kernel and hyperparameter integrated with the Random Under Sampling (RUS) and our Minimum error-based Principal Component Analysis (MebPCA). The RUS was used to handle imbalanced data, while MebPCA modified data dimension reduction techniques based on classification errors to speed up computational time without disturbing the performance of SVM. This combination improves the classifier's performance in detecting fraud effectively with a precision improvement of 29.31% and f1-score of 19.8%, and efficiently reduces the duration of training time significantly by 36.39% compared to previous research regarding the SVM method for fraud detection.
Studi Literatur Presentation Attack dan Set Data Anti-Spoof Wajah
Jurnal Teknik Elektro Vol 14, No 1 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i1.36108

Abstract

Face anti-spoof systems are needed in facial recognition systems to ward off attacks that present fake faces in front of the camera or image capture sensor (presentation attack). To build the system, a data set is needed to build a classification model that distinguishes the authenticity of the face of the input image received by the system. In the past decade anti-face spoof research has produced many data sets that are public, but often researchers need time to build or use the right public data sets that are used to build facial anti-spoof models. This article conducts a literature study of public data sets using a systematic literature review method to find out the types of attacks that appear on the facial anti-spoof system, the development process, evolution, and availability of facial anti-spoof data sets. From the search and selection results based on the specified criteria, there were 42 primary research manuscripts in the period 2010 to 2021. The results of the literature study found that there were three trends in the development of anti-spoof facial data sets, namely, 1) data sets with a very large number, 2) datasets with different types of facial samples, and 3) datasets constructed with various devices and sensors. These various public data sets can be accessed freely but with special rules such as agreeing to an end user license agreement document from the researcher or the institution that owns the data set. However, there are also datasets that cannot be accessed due to invalid URLs or due to special rules from the cloud storage service provider where the datasets are stored.
Cloud-Based Architecture for YOLOv3 Object Detector using gRPC and Protobuf
Jurnal Teknik Elektro Vol 14, No 1 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i1.36537

Abstract

The deep learning-based object detector accuracy has surpassed conventional detection methods. Although implementation is still limited to hardware capabilities, this problem can be overcome by combining edge devices with cloud computing. The recent study of cloud-based object detector architecture is generally based on representational state transfer (RESTful web services), which uses a pooling system method for data exchange. As a result, this system leads to a low detection speed and cannot support real-time data streaming. Therefore, this study aims to enhance the detection speed in cloud-based object recognition systems using gRPC and Protobuf to support real-time detection. The proposed architecture was deployed on the Virtual Machine Instance (VMI) equipped with a Graphics Processing Unit (GPU). The gRPC server and YOLOv3 deep learning object detector were executed on the cloud server to handle detection requests from edge devices. Furthermore, the captured images from the edge devices were encoded into Protobuf format to reduce the message size delivered to the cloud server. The results showed that the proposed architecture improved detection speed performance on the client-side in the range of 0.27 FPS to 1.72 FPS compared to the state-of-the-art method. It was also observed that it could support multiple edge devices connection with slight performance degradation in the range of 1.78 FPS to 1.83 FPS, depending on the network interface used.
Trade-off between Image Quality and Computational Complexity: Image Resizing Perspective
Jurnal Teknik Elektro Vol 14, No 1 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i1.37629

Abstract

This study proposed a new approach for resizing image deal with quality and computational complexity. Here, previous methods in image resizing do analytical works to approximate the original picture element (pixel) or to remove high frequency coefficients. For images with huge pixel, this will result in computational burden due to number of multiplication and addition in the synthesized formula. Instead of the works, this study proposed a new approach in removing the coefficients by exploiting the second-order block matrix without the need to synthesize the formula. It can be called a fully numeric image resizing method. The result shows that the resized version of original image has peak signal to noise ratio (PSNR) equal to 35.24 dB for resizing the famous Lena image which means compareable to the conventional which has PSNR value around 35 dB but here deriving analytical formula is not required. Reducing computational complexity is also achieved as expected with result only 16 addition involved with no multiplication required. This is lower than the conventional in term of computational complexity. Overall, the proposed method has a good balance for both performances than the conventional approaches.
Performance Evaluation of Two System Models for a MIMO System to Hover the Bicopter Unmanned Aerial Vehicle
Jurnal Teknik Elektro Vol 14, No 1 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i1.35746

Abstract

Building unmanned aerial vehicle (UAV) control system models are highly challenging due to multiple inputs and multiple outputs (MIMO). Not only does it have various angular position outputs such as roll, yaw, and pitch, but also flight control has more than one input; for instance, a bicopter has dual rotors. More rotors have more complex model. The hover condition has a zero resultant force which can be utilized to design a system model. On the other hand, an attractive identification system method is applied to develop the design. This research aims to evaluate the performance of two MIMO design on bicopter between methods based on the hover principle and identification technique. Experimental validation by employing bicopter simulator is an excellent strategy to fulfil this purpose. The results of the investigation of the experiment showed that the identification model was more accurate than the hover design, particularly regarding the overshot phenomenon and error. In addition, the hover principle tended to build ideal model because it did not include the dynamic, uncertainty and nonlinear conditions in aeroplane control design.  Although the identification system was complicated because it previously needed to measure the input and output values, it performed closer to the actual experiment. It performed more satisfactory overshoot values compared with the experimental validation than the hover model by 1°, 3°, and 8° in roll, pitch, and yaw angles, respectively.
Classroom Occupancy Monitoring System using IoT Device and the k-Nearest Neighbors Algorithm Fidarliyan, Yarnish Dwi Sagita; Prasetijo, Agung Budi; Eridani, Dania
Jurnal Teknik Elektro Vol 14, No 2 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i2.37141

Abstract

The occupancy monitoring system is one of the substantial aspects of building management. Through monitoring the occupancy in the area in a building, the obtained information can be used for building management purposes such as controlling indoor area air quality and improving building security. Some technologies such as video surveillance cameras, Radio Frequency Identification (RFID), and motion sensors have been used in the occupancy monitoring system. However, those technologies pose several disadvantages including privacy concerns and limited information generated. A classroom occupancy monitoring system using an Internet of Things (IoT) device and the k-Nearest Neighbors (k-NN) algorithm was built to monitor classroom occupancy by classifying the number of occupants based on classroom environmental data into occupancy levels by using the k-NN classifier model. By utilizing IoT devices, CO2, temperature, and humidity data in a naturally ventilated classroom were recorded using the MQ-135 and BME280 sensors, as well as WiFi-based NodeMCU, was used to distribute data to the cloud. The collected data were trained and tested by the k-NN algorithm to produce a k-NN classifier model. From the tests conducted, the performance of the k-NN classifier model in classifying the number of occupants into occupancy levels resulted in an accuracy of 88%. In addition, the proposed system also produces a web-based classroom occupancy monitoring application that has been integrated with the k-NN classifier model so the classification can be done for real-time data and monitored directly.
Pengkondisi Sinyal RTD Presisi pada Terowongan Angin Indonesian Low-Speed Tunnel Muflih, Muhamad; Riyadi, Munawar Agus; Pane, Ivranza Zuhdi; Parulian, Franky Surya
Jurnal Teknik Elektro Vol 14, No 2 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i2.39415

Abstract

The temperature of the Indonesian Low-Speed Tunnel (ILST) wind tunnel test section was measured using a Pt100-type Resistance Temperature Detector (RTD) sensor. With the upgrade of the Indonesian Low-Speed Tunnel - Data Acquisition and Reduction System (ILST-DARS) using Ethernet communication, an integrated RTD linearization circuit was designed with the Conditioning Unit (CU) Mk3 to replace the Newport 267B 16-bit parallel and DAS-Hub as the current RTD interface. In this research, the design of the signal conditioner uses the RTD_Linearization_v7.xls program from Texas Instruments, the LTspice simulator software, and the AMP01E precision instrumentation amplifier. Based on the calibration results in the range of 20 – 50 0C, this signal conditioner has an average deviation value of 0.38 0C (1.31%). In the wind tunnel speed variation testing with a range of 30 – 65 m/s, the RTD signal conditioner had an average deviation of 0.41 K (0.14%). The Repeatability Test procedure was carried out at a wind speed of 65 m/s with an angle of attack for the test model from -90 to 200 and data were collected 10 times at each angle. The average deviation of temperature against variations in the angle of attack of the test model in this procedure is 0.25 K (0.08%) and the average deviation of wind speed against variations in the angle of attack of the test model is 0.03 m/s (0.04%).
Performance Degradation Evaluation of a Lithium-Ion Battery from Multiple SoC Measurements Saputra, Riza Hadi; Marindra, Adi Mahmud Jaya; Nursyeha, Muhammad Agung; Fariyani, Dwi Kurnia Agung
Jurnal Teknik Elektro Vol 14, No 2 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i2.40226

Abstract

Lithium-Ion (Li-ion) battery is essential in today's energy systems and electric vehicles (EVs). Although Li-ion battery can be charged quickly and have a high energy density, it has several drawbacks, including the rapid degradation of battery performance, especially in terms of battery capacity. Therefore, evaluating its performance degradation is necessary to understand its characteristics. In this paper, the performance degradation of a Li-ion battery is monitored and evaluated from multiple SoC measurements. A simple and low-cost experimental setup consisting of sensors, a microcontroller, and a PC is developed to measure and record the real-time data of Li-ion battery voltage and current. Then, the battery state of charge  (SoC) is determined using the Coulomb Counting method, which is based on the incoming and outgoing currents of the battery. As a result, this study derives three parameters that indicate the performance degradation of a Li-ion battery, i.e., SoC, battery capacity, and discharge time. From multiple direct measurements with constant load and C20 discharge process, the minimum SoC value increases from 11% to 18%, while battery capacity decreases from 8.8Ah to 8.3 Ah and, discharge time decreases from 16.9 hours to 16.4 hours. All of those parameters indicate a degradation of around 7% in battery performance. Therefore, this research paves the way for finding a solution to mitigate the quick performance degradation of Li-ion batteries.

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