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Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Published by Universitas Gadjah Mada
ISSN : 23014156     EISSN : 24605719     DOI : 10.22146/jnteti
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
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
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 644 Documents
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Pengaruh Synonym Recognition dalam Deteksi Kemiripan Teks Menggunakan Winnowing dan Cosine Similarity Santi Purwaningrum; Agus Susanto; Ari Kristiningsih
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : 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

Abstract

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).
Prakiraan Beban Listrik Menggunakan Metode Jaringan Saraf Tiruan dengan Data yang Terbatas Elang Bayu Trikora; Sasongko Pramonohadi; M. Isnaeni Bambang Setyonegoro
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : 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

Abstract

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.
Kendali Inverted Pendulum: Studi Perbandingan dari Kendali Konvensional ke Reinforcement Learning Ahmad Ataka; Andreas Sandiwan; Hilton Tnunay; Dzuhri Radityo Utomo; Adha Imam Cahyadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : 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

Abstract

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.
Strategi Peningkatan Kinerja DC Microgrid dengan Konfigurasi DC/AC Coupling Adhi Kusmantoro; Ardyono Priyadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : 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

Abstract

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.
Prediksi Muka Air Laut dari Sistem PUMMA Menggunakan SARIMA Irfan Asfy Fakhry Anto; Oka Mahendra; Purnomo Husnul Khotimah; Semeidi Husrin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : 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

Abstract

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.
Peningkatan Akurasi Adaptive Monte Carlo Localization Menggunakan Convolutional Neural Network Riza Agung Firmansyah; Tri Arief Sardjono; Ronny Mardiyanto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : 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

Abstract

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.
Pemodelan Pengukuran Kesiapan untuk Keberhasilan Implementasi Sistem Enterprise Resource Planning Santo Fernandi Wijaya; Jansen Wiratama; Angelina Ervina Jeanette Egeten
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : 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.v12i3.7699

Abstract

In the digital era, companies are required to optimize technological innovation to increase capacity in achieving work that is more efficient, effective, productive, and has a competitive advantage. Companies must use the enterprise resource planning (ERP) system to improve efficiency, effectiveness, and productivity. Implementing an ERP system provides competitive advantages by means of decision-making proven for fast and precise management. ERP system is an integrated information system for strategic company management decision-making. For this reason, implementing an ERP system is necessary. Unfortunately, the fact shows that many companies fail to implement an ERP system, resulting in costly implementation expenses. Previous research has discussed empirical studies on the critical success factors for implementing an ERP system effectively. Therefore, further research still needs to be done by discussing ERP implementation from the perspective of measuring companies’ readiness levels in ERP system implementation. This research methodology criticized and analyzed the comparison of eleven prior research articles as a basis for developing a model to measure companies’ readiness levels in implementing ERP systems. This research examined quantitative data gathered through the use of questionnaire method to rank indicators for measuring companies’ readiness levels by identifying four dimensions and 27 indicators. This research aims to produce a model for measuring companies’ readiness to implement the ERP system on time successfully.
Klasifikasi Penyakit Daun Kopi Robusta Menggunakan Metode SVM dengan Ekstraksi Ciri GLCM Agus Supriyanto; R. Rizal Isnanto; Oky Dwi Nurhayati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 4: November 2023
Publisher : 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.v12i4.8044

Abstract

Many farmers in Indonesia derive their income from coffee plants, which also play a crucial role in the country’s foreign exchange earnings. However, coffee plant production may decrease due to pests and disease attacks. Leaf diseases, such as leaf spot (Cercospora coffeicola) and leaf rust (Hemileia vastatrix), are among the most common diseases to occur in coffee plants. This research seeks to identify leaf diseases in robusta coffee leaves and determine the classification. The application of machine learning-based image processing using the support vector machine (SVM) classification method based on the gray-level co-occurrence matrix (GLCM) feature extraction can be the proposed solution. The preprocessing must precede the processing stage for easier analysis of the image’s quality. Then, the k-means clustering segmentation process was conducted to distinguish leaf parts affected by leaf spot and rust from those unaffected. The GLCM method was employed as the feature extraction based on the angular second moment (ASM) or energy features, contrasts, correlations, inverse different moment (IDM) or homogeneities, and entropy with angles of 0°, 45°, 90°, and 135°, as well as inter-pixel distances of 1 until 3. The classification was done with the SVM method using the linear, polynomial, and radial basis function (RBF) Gaussian kernels. This research used leaf spot and rust images, with training and test data of 320 and 80 images, respectively. The RBF Gaussian achieved the best test results with the best accuracy of 97.5%, precision of 95.24%, recall of 100%, and F1-score of 97.56%.
Implementasi Sistem Keamanan Presensi Berbasis Kode QR Menggunakan Algoritma RSA dan Hash Arif Indra Irawan; Iman Hedi Santoso; Istikmal; Maya Rahayu
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 1: Februari 2024
Publisher : 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.v13i1.4395

Abstract

The quick response (QR) code-based attendance application contributes to reducing paper usage and attendance input errors. However, in its implementation process, the QR-code-based attendance at a Bandung school demonstrates weaknesses. Absent students can fake their attendance for themselves or friends. This type of attack is known as fake QR code generation. This research proposes a security authentication system using the Rivest–Shamir–Adleman (RSA) encryption algorithm and the secure hash algorithm 1 (SHA-1) to secure QR code-based attendance applications from fake QR code generation attacks. The RSA algorithm encrypts QR code data to maintain privacy, while the SHA-1 algorithm ensures data integrity. Based on this method, the mutual authentication process between the QR code data generated by the student and the attendance reading application by the teacher can be established. The results obtained from a series of tests showed that the security system in the student attendance recording application that had been implemented at Madrasah Aliyah (MA) Al-Mukhlishin could detect and prevent fake QR code generation attacks. The test was conducted by changing the impact of the key length on RSA-1024 bits and RSA-2048 bits. The results showed that in RSA-1024 bits, energy consumption of 0.14 J and time of 1.66 s is more efficient than that in RSA-2048 bits, with energy consumption of 0.19 J and time of 2.09 s. Interestingly, if a higher level of security is required, the key length should be increased at the expense of some energy and time efficiency.
Analisis Sentimen Masyarakat Indonesia Terhadap Vaksin Booster COVID-19 Dionisia Bhisetya Rarasati; Angelina Pramana Thenata; Afiyah Salsabila Arief
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 4: November 2023
Publisher : 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.v12i4.5125

Abstract

The COVID-19 pandemic is still occurring in various countries, including Indonesia. This pandemic is caused by the coronavirus, which has mutated into multiple virus variants, such as Delta and Omicron. As of 9 February 2022, 4,626,936 people were confirmed positive for COVID-19 in Indonesia. This number continues to rise. The Indonesian government has prevented the spread of these virus variants by introducing booster vaccines to the public. However, this vaccination program has caused various sentiments among Indonesians. To optimize efforts to combat COVID-19, the government needs to know these sentiments immediately. Based on these problems, the researcher proposes the application of machine learning technology to develop a system that can analyze the sentiments of the Indonesians toward the booster vaccine. This research has several stages: data collection, data labeling, text preprocessing, feature extraction, and application of the support vector machine (SVM) algorithm using various kernels, namely the linear kernel, Gaussian radial basis function (RBF) kernel, and polynomial kernel. Furthermore, the results of the system were tested for accuracy using a 10-fold cross validation and confusion matrix. The dataset used was 681 tweets with the hashtag “vaksinbooster.” The dataset consists of two classes: negative (0) and positive (1). The results showed that the data were positive for the booster vaccine, as evidenced by the higher number of positive tweets, with 554 data, compared to 127 negative tweets. In addition, the dataset was divided into training data of 545 and testing data of 136. In addition, the test results of this study revealed that the SVM algorithm with the polynomial kernel, which was evaluated with 10-fold cross validation, yielded the highest level of accuracy, namely 79.22%.

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