Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Topics cover the fields of (but not limited to): 1. Information Technology: Software Engineering, Knowledge and Data Mining, Multimedia Technologies, Mobile Computing, Parallel/Distributed Computing, Artificial Intelligence, Computer Graphics, Virtual Reality 2. Power Systems: Power Generation, Power Distribution, Power Conversion, Protection Systems, Electrical Material 3. Signals, Systems, and Electronics: Digital Signal Processing Algorithm, Robotic Systems and Image Processing, Biomedical Instrumentation, Microelectronics, Instrumentation and Control 4. Communication Systems: Management and Protocol Network, Telecommunication Systems, Wireless Communications, Optoelectronics, Fuzzy Sensor and Network
Articles
11 Documents
Search results for
, issue
"Vol 12 No 3: Agustus 2023"
:
11 Documents
clear
<![CDATA[Investigasi Kinerja Relai Proteksi Saluran Transmisi dengan Kompensator Seri ]]>
<![CDATA[Nanang Rohadi]]>;
<![CDATA[Nendi Suhendi]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.4810
<![CDATA[This paper aims to investigate the measurement results of closed loop fault impedance using conventional distance relay algorithms (SEL-421 distance relay) when used as protective tools on transmission lines with series compensators and several uncertainty parameters (factors). Several system’s factors can emerge concurrently, and the series compensators may affect the relay algorithm’s performance, particularly on the phase fault to ground. However, the existing testing method of the relay performance only alters one factor while simultaneously keeping others constant. This technique is no longer relevant when several factors are not considered simultaneously, affecting the relay performance during faults. For algorithm investigations as in actual conditions, several fault scenarios were performed at the fault point before and after series compensators while simultaneously changing the values of several factors in the system model through fault simulations. This research employed the DIgSILENT PowerFactory for power system modeling and fault simulation. In fault testing simulations, Thevenin equivalent circuit with two sources and 42% series compensator were placed in the center of a 300 km of a 400 kV transmission line. Several fault scenarios and the fault impedance measurement as a function of changes in several factor values were performed automatically. An automated testing simulation was developed using the DIgSILENT Programming Language (DPL) to read data samples generated through the SIMLAB software for several factors. A series compensator affected the performance of the relay algorithm for calculating the fault impedance when faults occurred after the compensator. For faults after the compensator, changing several factors simultaneously affects the relay’s accuracy and aggravates the relay’s performance, specifically relay operation failure in the form of underreaching and overreaching. The developed testing technique is expected to be utilized as a cutting-edge testing tool for the development and implementation of relays in a timely manner and as in actual conditions.]]>
<![CDATA[Pengembangan Sistem Kelistrikan Tanah Merah Mempertimbangkan Energi Baru Terbarukan dan Emisi CO2 ]]>
<![CDATA[Amrisal Kamal Fajri]]>;
<![CDATA[Sarjiya]]>;
<![CDATA[Lesnanto Multa Putranto]]>;
<![CDATA[Adlan Bagus Pradana]]>;
<![CDATA[Fransisco Danang Wijaya]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.5254
<![CDATA[The condition of the electricity system in the Papua region still has an electrification ratio of 94% with a high electricity generation cost of IDR3,041/kWh. In addition, the existing electricity system still consists of over 100 small systems, the majority of which are diesel power plants. One of the systems is the Tanah Merah area, with a population of 19,136 people and an energy demand of 6.89 GWh. The region is projected to experience expansion and population growth, resulting in a corresponding rise in the demand for electrical energy. Therefore, planning for the development of power plant systems needs to be done to meet the growing demand for electrical energy. Planning in remote areas is typically done for a short-term timeframe spanning from 2025 to 2030, involving the optimization process of several proposed power plant candidates. The proposed candidate power plants consider gas and fuel supply, as well as the availability of local primary energy and technology. Optimization will minimize the total cost of the to-be-selected power plant, which has features including initial investment costs, ongoing operation and maintenance costs, fuel expenses, and the residual value of the assets throughout the planned duration. In planning, a greenhouse gas emission reduction of 29% and an energy mix proportion of 23% need to be considered in accordance with government policy. Therefore, two scenarios covering both economic and environmental aspects were considered in the simulation process, namely the business as usual (BaU) scenario and the nationally determined contributions (NDC) scenario for emission limitation. Optimization was developed based on mixed-integer linear programming (MILP) performed in HOMER software. The simulation results indicate that the electricity generation cost for the BaU scenario is more economical compared to the NDC scenario at IDR2,559.8/kWh versus IDR3,104.64/kWh.]]>
<![CDATA[Pengolahan Data Sensor Gerak Ponsel untuk Klasifikasi Karakteristik Mengemudi ]]>
<![CDATA[Lisa Dinda Yunita]]>;
<![CDATA[Ema Utami]]>;
<![CDATA[Ainul Yaqin]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.6050
<![CDATA[Driving behavior significantly influences road safety. Unsafe driving behaviors, such as driving under the influence, speeding, and using mobile phones, can lead to serious accidents and fatalities. This research aims to observe driving characteristics by utilizing smartphone motion sensor data. The data collection method involved recording the driver’s smartphone motion sensor during trips. The data were then exported from the system for further processing. The main objective of this study is to process the data by creating a classification model with the best performance in handling smartphone motion sensor data. The results of this research are expected to be implementable models to address road safety issues in the future. Additionally, by utilizing driver characteristic detection technology, awareness of safe driving practices can be enhanced. The research methodology used data mining with machine learning classification modeling using random forest (RF), support vector machine (SVM), and decision tree (DT) methods. The test results indicate that the RF model performed the best with an accuracy of 91.22%. Furthermore, this study found that speed was the most influential factor in identifying safe or unsafe driving behavior. The developed classification model shows the potential to improve traffic management efficiency and contribute to safer transportation. By leveraging driver characteristic detection technology, it is hoped that awareness of safe driving practices will increase, leading to a safer road environment.]]>
<![CDATA[Metode Kalibrasi Probe Ultrasonik dari Phantom Kawat Tunggal Menggunakan Algoritma Levenberg-Marquardt ]]>
<![CDATA[Tri Arief Sardjono]]>;
<![CDATA[Eko Mulyanto Yuniarno]]>;
<![CDATA[I Made Gede Sunarya]]>;
<![CDATA[I Ketut Eddy Purnama]]>;
<![CDATA[Mauridhi Hery Purnomo]]>;
<![CDATA[Norma Hermawan]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.6282
<![CDATA[A freehand three-dimensional (3D) ultrasound system is a method of acquiring images using a 3D ultrasound probe or conventional two-dimensional (2D) ultrasound probe to give a 3D visualization of an object inside the body. Ultrasounds are used extensively in clinical applications since they are advantageous in that they do not bring dangerous radiation effects and have a low cost. However, a probe calibration method is needed to transform the coordinate position into a 3D visualization display, especially for image-guided intervention. The current ultrasound probe calibration system usually uses the numerical regression method for the N-wire phantom, which has problems in accuracy and reliability due to nonlinear point scattered ultrasound image data. Hence, a method for ultrasound probe positional calibration of single-wire phantom using the Levenberg-Marquardt algorithm (LMA) was proposed to overcome this weakness. This experiment consisted of an optical tracking system setup, a 2D ultrasound probe with marker, an ultrasound machine, and a single-wire object in a water container equipped with a marker. The position and orientation of the marker in a 2D ultrasound probe and the marker in the water container were tracked using the optical tracking system. A 2D ultrasound probe was equipped with a marker connected wirelessly using an optical tracking system to capture the single-wire object. The resulting sequences of 2D ultrasound images were reconstructed and visualized into 3D ultrasound images using three transformations, ultrasound beam to ultrasound probe’s marker, single-wire phantom position to container’s marker, and the 3D visualization transformation. The LMA was used to determine the best optimization parameters for determining the exact position and representing that 3D visualization. The experiment result showed that the lowest mean square error (MSE), rotation error, and translation error were 0.45 mm, 0.25°, and 0.3828 mm, respectively.]]>
<![CDATA[Pengaruh Synonym Recognition dalam Deteksi Kemiripan Teks Menggunakan Winnowing dan Cosine Similarity ]]>
<![CDATA[Santi Purwaningrum]]>;
<![CDATA[Agus Susanto]]>;
<![CDATA[Ari Kristiningsih]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.6375
<![CDATA[Plagiarism is an act of imitating, quoting and even copying or acknowledging the work of others as one’s own work. A final project is one of the mandatory requirements for students to complete learning at college. It must be written by the students based on their own ideas. However, there is much plagiarism because it is easy to carry out just by simply copying the text of other people’s ideas and then pasting it into a worksheet and admitting that the ideas are theirs. In addition, replacing some words in other people’s sentences with their own language style without properly acknowledging the original source of the quotation is also an act of plagiarism. A manual check for the final project also becomes an issue for the final project coordinator, i.e., it needs high accuracy and a relatively long time to check the plagiarism in the final project document. Therefore, implementing plagiarism detection mechanisms is necessary to mitigate the escalation of plagiarism occurrences. In response to those matters, this study aims to design a system capable of identifying textual similarities by focusing on sentences containing synonymous words. One of the used algorithms is synonym recognition, which detects words that possess synonymous meanings by comparing each term with the entries in a dictionary. The synonym recognition is combined with the winnowing method, functioning as a fingerprint-based text weighting. After the weight of each document is obtained, the similarity level between documents is calculated with the cosine similarity algorithm. The inclusion of synonym recognition in conjunction with the winnowing weighting method resulted in a notable gain of 3.11% in the average similarity scores for title and abstract detection, compared to the absence of synonym recognition. The results show that the used algorithms are accurate with accuracy testing and root mean squared error (RMSE).]]>
<![CDATA[Prakiraan Beban Listrik Menggunakan Metode Jaringan Saraf Tiruan dengan Data yang Terbatas ]]>
<![CDATA[Elang Bayu Trikora]]>;
<![CDATA[Sasongko Pramonohadi]]>;
<![CDATA[M. Isnaeni Bambang Setyonegoro]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.6437
<![CDATA[As time changes, electric load demand forecasting is one of the vital things in generation and distribution planning. Various ways can be implemented in forecasting electrical energy demands, one of which is by using the artificial neural network method, which is a method that mimics the ability of the human brain to receive an input and then carry out processing between the neurons within to produce information based on the processes that occur within the neurons. This research uses a neural network method to forecast the electric load in Jayawijaya Regency. This research builds a neural network architecture suitable to the data obtained from National Electricity Company (Perusahaan Listrik Negara, PT PLN Indonesia) UP 3 Wamena to find an architecture model suitable with high accuracy. Due to the limited data owned to forecast electric load, an interpolation method based on the owned original data was carried out to increase the amount of the existing data. In this way, more data can be used as input, allowing the model to forecast load requirements more accurately. These propagated data were used as input data in the artificial neural network model. After conducting iterative testing using a neural network, it is found that the model that fitted the data was feed-forward long short-term memory (LSTM) network, this model can obtain errors in accordance with the standards of a model to perform forecasts of 0.04% with nine epochs.]]>
<![CDATA[Kendali Inverted Pendulum: Studi Perbandingan dari Kendali Konvensional ke Reinforcement Learning ]]>
<![CDATA[Ahmad Ataka]]>;
<![CDATA[Andreas Sandiwan]]>;
<![CDATA[Hilton Tnunay]]>;
<![CDATA[Dzuhri Radityo Utomo]]>;
<![CDATA[Adha Imam Cahyadi]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.7065
<![CDATA[The rise of deep reinforcement learning in recent years has led to its usage in solving various challenging problems, such as chess and Go games. However, despite its recent success in solving highly complex problems, a question arises on whether this class of method is best employed to solve control problems in general, such as driverless cars, mobile robot control, or industrial manipulator control. This paper presents a comparative study between various classes of control algorithms and reinforcement learning in controlling an inverted pendulum system to evaluate the performance of reinforcement learning in a control problem. A test was performed to test the performance of root locus-based control, state compensator control, proportional-derivative (PD) control, and a reinforcement learning method, namely the proximal policy optimization (PPO), to control an inverted pendulum on a cart. The performances of the transient responses (such as overshoot, peak time, and settling time) and the steady-state responses (namely steady-state error and the total energy) were compared. It is found that when given a sufficient amount of training, the reinforcement learning algorithm was able to produce a comparable solution to its control algorithm counterparts despite not knowing anything about the system’s properties. Therefore, it is best used to control plants with little to no information regarding the model where testing a particular policy is easy and safe. It is also recommended for a system with a clear objective function.]]>
<![CDATA[Strategi Peningkatan Kinerja DC Microgrid dengan Konfigurasi DC/AC Coupling ]]>
<![CDATA[Adhi Kusmantoro]]>;
<![CDATA[Ardyono Priyadi]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.7151
<![CDATA[DC microgrid is a good solution for increasing demand for electricity loads and is an effective way to utilize renewable energy sources into distributed generation systems. Solar energy has intermittent properties when DC microgrids are used. In the previous research, the battery was used as an energy reserve to overcome fluctuations in the output power of photovoltaic (PV) arrays. However, the use of many batteries requires a high cost. This study aims to reduce power fluctuations on the DC bus, when the DC microgrid source from the PV array and battery is disconnected. The research method was carried out using MATLAB simulations, by designing a DC/AC coupling hybrid configuration. This configuration used two PV arrays, two multi-battery sources, and a utility network. DC microgrid settings were done separately by each converter by sending a reference signal to the converter control. In the first condition, the DC load and AC load were supplied from the PV array. In the second condition, the load was supplied from the battery. Meanwhile, in the third condition, the load was supplied from the utility network. The results showed that when using a PV array source, the DC bus voltage remained stable at 48 V, even though there was a spike at 08.00 and 15.00. Likewise, when using a battery source and utility network, the DC bus voltage was maintained at a level of 48 V. In this study, the DC microgrid was able to supply the load uninterruptedly using three conditions or modes. Therefore, the DC microgrid hybrid configuration can provide continuous electric power.]]>
<![CDATA[Prediksi Muka Air Laut dari Sistem PUMMA Menggunakan SARIMA ]]>
<![CDATA[Irfan Asfy Fakhry Anto]]>;
<![CDATA[Oka Mahendra]]>;
<![CDATA[Purnomo Husnul Khotimah]]>;
<![CDATA[Semeidi Husrin]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.7372
<![CDATA[The rising sea levels can threaten millions of people residing along the coast or lowlands. The risk can be mitigated by the sea-level prediction done by collecting information on the likelihood of rising sea levels. The Ministry of Marine Affairs and Fisheries of Indonesia has developed Perangkat Ukur Murah untuk Muka Air Laut (Inexpensive Device for Sea Level Measurement, PUMMA) to measure sea levels. PUMMA is located in remote monitoring stations based on Indonesian maritime area. The PUMMA system currently lacks a prediction feature. This objective of this study is to model the sea-level prediction using the dataset for one year, from July 2021 until July 2022. The seasonal autoregressive integrated moving average (SARIMA) method was used because SARIMA proved to be a flexible and versatile method for a dataset having noncomplex nature and seasonal patterns. This study has developed several models of the SARIMA. The model performance was evaluated using the mean absolute percentage error (MAPE), R-squared, mean square error (MSE), and root mean square error (RMSE) metrics. The SARIMA(1, 1, 0)(1, 1, 1)12 model achieved the lowest prediction error with an R-squared of 0.508, MSE of 0.0479, and RMSE of 0.069. Based on the performance, SARIMA(1, 1, 0)(1, 1, 1)12 model is feasible for predicting sea levels using the PUMMA dataset.]]>
<![CDATA[Peningkatan Akurasi Adaptive Monte Carlo Localization Menggunakan Convolutional Neural Network ]]>
<![CDATA[Riza Agung Firmansyah]]>;
<![CDATA[Tri Arief Sardjono]]>;
<![CDATA[Ronny Mardiyanto]]>
<![CDATA[ Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023 ]]>
Publisher : <![CDATA[Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/jnteti.v12i3.7432
<![CDATA[This paper explains the increase in localization system accuracy of the adaptive Monte Carlo localization (AMCL) in robots utilizing a convolutional neural network (CNN). The localization system in robots is defined as the position recognition process of robots within their working environment. This system is essential as it allows robots to navigate and map efficiently and accurately. Without appropriate localization, robots cannot operate effectively and can encounter troubles such as losing direction or bumping into objects. AMCL is a popular localization system and is widely applied in robots. This method utilizes the changes in the robots’ position and light detection and ranging (LiDAR) sensor reading as input. Reading of robot position changes is susceptible to error due to slips or wheel deformations. The inaccuracy of reading the robots’ position change results in the inaccuracy of the robots’ position prediction by AMCL, so improvements are required. Novelty in this paper includes providing compensation values from AMCL results for the error to be small. These compensation values were obtained from the CNN training results; hence, the proposed method was dubbed AMCL+CNN. Inputs given to the CNN were the changes in wheel odometry values and distance reading by the LiDAR sensor. CNN outputs were compared to the target data in the form of the robots’ actual position from observation results. Network training was conducted for as many as 200 epochs to achieve the lowest validation loss. Testing was done on a robot installed with a robot operating system (ROS). Training and testing datasets were obtained from rosbag data when the robot traversed the testing area. In straight and turn scenarios, obtained AMCL+CNN algorithms had fewer errors than the regular AMCL and Monte Carlo localization (MCL). Results obtained are also superior in terms of positional error metrics when compared to several other comparison methods.]]>
