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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 10 Documents
 1 
Search results for , issue "Vol 12 No 4: November 2023" : 10 Documents clear
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%.
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%.
Evaluasi Platform Perangkat Keras Sistem Tertanam untuk Unit Kontrol Parkir Otomatis Wahyu Dewanto; Agung Fathurrahman; Agus Bejo
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.v12i4.6277

Abstract

Automatic parking system is one of the parking management technologies that is widely used in various institutions today. An automatic parking system works by controlling a parking gate automatically to open and close the gate and record the vehicle’s license plate when entering and exiting using access control such as a smart card or radio frequency identification (RFID). One of the challenges in implementing an automatic parking system is traffic congestion during high traffic conditions. This challenge arises because the control unit in the automatic parking system takes a relatively long time to process and store images from the camera. This research examined several embedded system platforms as automatic parking system control units, including Raspberry Pi 3B, Raspberry Pi 4B, and Orange Pi Zero Plus. The evaluation is intended to find the best control unit platform based on several criteria, such as the execution time in capturing images, storing images, and the consumed power. From the evaluation results, it can be concluded that the Raspberry Pi 4B platform results in the fastest execution time for capturing and storing images, with an average time of 1,827.9 ms. Meanwhile, the Orange Pi Zero Plus platform achieves the lowest power consumption at 1.9 W. Based on the evaluation results, the Raspberry Pi 4B is recommended as the control unit if the automatic parking system requires a high-performance device. Otherwise, the Orange Pi Zero Plus is more recommended if the automatic parking system requires a low-power device.
INVys: Sistem Navigasi Dalam Ruangan untuk Penyandang Tunanetra Menggunakan Kamera RGB-D Widyawan; Muhamad Risqi Utama Saputra; Paulus Insap Santosa
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.v12i4.6372

Abstract

This research presents the INVys system aiming to solve the problem of indoor navigation for persons with visual impairment by leveraging the capabilities of an RGB-D camera. The system utilizes the depth information provided by the camera for micronavigation, which involves sensing and avoiding obstacles in the immediate environment. The INVys system proposes a novel auto-adaptive double thresholding (AADT) method to detect obstacles, calculate their distance, and provide feedback to the user to avoid them. AADT has been evaluated and compared to baseline and auto-adaptive thresholding (AAT) methods using four criteria: accuracy, precision, robustness, and execution time. The results indicate that AADT excels in accuracy, precision, and robustness, making it a suitable method for obstacle detection and avoidance in the context of indoor navigation for persons with visual impairment. In addition to micronavigation, the INVys system utilizes the color information provided by the camera for macro-navigation, which involves recognizing and following navigational markers called optical glyphs. The system uses an automatic glyph binarization method to recognize the glyphs and evaluates them using two criteria: accuracy and execution time. The results indicate that the proposed method is accurate and efficient in recognizing the optical glyphs, making it suitable for use as a navigational marker in indoor environments. Furthermore, the study also provides a correlation between the size of the glyphs, the distance of the recognized glyphs, the tilt condition of the recognized glyphs, and the accuracy of glyph recognition. These correlations define the minimum glyph size that can be practically used for indoor navigation for persons with visual impairment. Overall, this research presents a promising solution for indoor navigation for persons with visual impairment by leveraging the capabilities of an RGB-D camera and proposing novel methods for obstacle detection and avoidance and for recognizing navigational markers.
Klasifikasi Wilayah Rawan Banjir di Tomohon Menggunakan Citra Satelit Landsat 8 OLI Gabriel Kenisa Meqfaden Baali; Kristoko Dwi Hartomo; Sri Yulianto Joko Prasetyo
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.v12i4.7396

Abstract

Natural disasters often occur unexpectedly, resulting in both material and nonmaterial losses. Floods are among natural disasters that often occurs in several regions in Indonesia, one of which is Tomohon. Tomohon is a city located in the highlands, so it is expected to have a low flood risk level. Nevertheless, in reality, flood still occurs in Tomohon, which then causes material and nonmaterial losses. The data used in this research were the satellite imagery of the Landsat 8 onboard operational land imager (OLI) accessed through the United States Geographical Survey (USGS). The land covers in Tomohon were classified using the supervised classification method with the minimum distance classification (MDC) algorithm. This method provided the advantage of classifying land covers by utilizing training data in Tomohon, achieving an accuracy rate of 99.56%. In addition, the calculations of normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI) were also utilized to determine the level of vegetation and surface soil moisture in Tomohon using the Quantum GIS (QGIS) application. Upon examining the land covers and calculating the index, weighting was once more performed in accordance with criteria. It was done to facilitate the classification of the area into three flood risk classifications: high, medium, and low. The results showed that green spaces in Tomohon are still greater than residential areas. However, NDVI, NDWI, and SAVI calculations indicated that some densely populated areas are susceptible to flood. These areas include Tomohon Selatan and Tomohon Tengah Subdistricts, which have a high level of flood risk and the Tomohon Timur Subdistrict, which has a medium level of flood risk.
Perbaikan Keandalan PLTU Tembilahan dengan Penambahan Kapasitas Pembangkit Fadhil M Hanafi; Dian Yayan Sukma
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.v12i4.7507

Abstract

To ensure good generating system reliability, the presence of operating generating units or adequate power reserves in the generating system is very important. The availability of power reserves in the system depends on various factors, including the frequency of disturbances in generating units and the peak capacity required by the system. One example of a generating system is the Tembilahan steam power plant, which has a capacity of 2 × 7 MW and serves a peak load of 14.31 MW in 2019 until 2022. However, in that period, the reliability of the generating system, as measured by the loss of load expectation (LOLE) index, only reached 33 days/year, far below the standard of the State Electricity Company (Perusahaan Listrik Negara, PT PLN) 2021 to 2030 Electricity Supply Business Plan (Rencana Usaha Penyediaan Tenaga Listrik, RUPTL) of 1 day/year. To improve the reliability of the Tembilahan steam power plant generation system in 2027, an analysis is needed to consider the procurement period of the plant and the availability of land in the system. This analysis involves using the recursive convolution method to calculate the loss of load probability (LOLP)/ loss of load expectation (LOLE) index and the simple linear regression method to estimate the peak load in that year. Based on the results of the analysis, it was found that the addition of three generating units of 7 MW could improve the reliability of the generation system. The area required for these additional units was 2,030.91 m2 and the available land was still sufficient. After the improvements were made, the reliability index LOLE of the generating system increased to 0.078 days/year for the year 2027, meeting the standards for the reliability level of the plant based on PT PLN’s 20212030 RUPTL.
Perbandingan Fase Ekspresi Menggunakan Local Binary Pattern Histogram Untuk Pengenalan Ekspresi Mikro Ulla Delfana Rosiani; Priska Choirina; Yessy Nindi Pratiwi Pratiwi; Septiar Enggar Sukmana
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.v12i4.7818

Abstract

Microexpression is an emotional representation occurring spontaneously and cannot be controlled consciously. It is temporary (short duration) with subtle movements, making it difficult to detect with the naked eye. Microexpressions’ muscle movements are generated in only a few small areas of the face, so observation of specific areas results in faster computation time and provides important information compared to observation of the entire face. This research proposes reducing the observation area and phase for microexpression recognition. The observed areas in the Chinese Academy of Science Micro-Expressions (CASME II) dataset are left and right eyebrows, right and left eyes, and mouth. The observation phase of microexpressions included analyzing the comparison in the onset to offset phase (“fullOAO”) and in the onset, apex, and offset phase (“OAO”). Feature extraction was performed using a simple local binary patterns histogram (LBPH) method, which can represent local features in the facial area. The best result of the proposed method was the “fullOAO” phase with an accuracy of 96.8% (using support vector machine-radial basis function, SVM-RBF) and an average computation time of 0.192 ms per frame and 10.473 ms per video. In “OAO” phase type, an accuracy of 87.7% was achieved with a computation time of 0.159 ms per frame and 0.576 ms per video. The difference in accuracy and computation time between the two-phase types occurs because the number of frames in “fullOAO” type is greater than in “OAO”, resulting in a different amount of processing time and feature extraction data. However, the 9% decrease in accuracy does not significantly affect the accuracy since the accuracy rate is still relatively good, above 80%. Furthermore, the correct measurement for computation time was the time taken to process each frame in the input video. Therefore, the proposed method can produce fast computation time and relatively accurate recognition.
Pemodelan dan Simulasi MPPT pada Sistem PLTS Menggunakan Metode DNN Edi Leksono; Robi Sobirin; Reza Fauzi Iskandar; Putu Handre Kertha Utama; Mochammad Iqbal Bayeqi; Muhammad Fatih Hasan; Irsyad Nashirul Haq; Justin Pradipta
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.v12i4.7931

Abstract

The maximum power point tracking (MPPT) feature in solar power plants is an essential function in increasing the efficiency of electricity production. The incremental conductance (InC) algorithm controls MPPT, aiming to maximize the output power of photovoltaic (PV) panels and increase the efficiency of the solar power plant system. Even though the InC algorithm is simple and practical, this algorithm tends to lack support in precise switching speeds, is sensitive to the measurement precision level, and is inadequate to eliminate power oscillations due to tight switching cycles. The deep neural network (DNN) algorithm has the potential to answer the challenges of MPPT dynamics. DNN’s learning capabilities enable the controller to better recognize the dynamics of shifts in maximum power values, thereby providing more appropriate contact actuation. The input for the DNN is the duty ratio produced by the InC algorithm. The DNN algorithm was implemented on three DC-to-DC power converter topologies, namely buck, boost, and buck-boost, to determine MPPT performance under standard tests and actual environmental conditions. DNN has demonstrated the ability to reduce oscillation effects, speed up steady-state time, and increase efficiency. In actual environmental conditions, the results showed that the buck converter consistently produced the highest power, followed by the boost and the buck-boost converters. Regarding performance efficiency, the buck converter achieved the highest efficiency at 94.58%, followed by the boost converter at 90.79%. Conversely, the buck-boost converter had the lowest performance efficiency, with an efficiency of 79.34%.
Skema Proteksi Resonansi Tegangan Lebih Saluran Udara Tegangan Ekstra Tinggi 500 kV Imam Ghozali; Mochammad Facta; Abdul Syakur
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.v12i4.8114

Abstract

Shunt reactors are frequently installed at the end of a 500 kV double circuit extra high voltage transmission lines (EHVTL) to help overcome the voltage rise caused by the long line capacitance. However, voltage problems may still occur, especially during the voltage injection process in new transmission lines that have been installed, due to voltage induction and parameter reinforcement from other circuits within the same line that have been injected earlier. The problem of overvoltage occurrence becomes even more complex because the line and shunt reactor have been completely installed, making it impossible to change the length of the line and the capacity of the shunt reactor. The overvoltage phenomenon in this research occurs when one circuit in the transmission line has not been injected. However, the shunt reactor has been connected so that the line receives induction and strengthening from another circuit because, at the same time, the second circuit has been injected with operating voltage. Overvoltage in this research occurs when one circuit in the transmission line has not been injected. Nevertheless, the shunt reactor has been connected so that the line receives induction and reinforcement from another circuit because, at the same time, the second circuit has been injected with operating voltage. This paper proposes modifications to the voltage injection maneuver scenario into the line, modifications to the protection scheme in the event of a phase-to-ground fault, and the addition of an overvoltage relay to overcome the overvoltage conditions. Modifying the voltage injection maneuver scenario could decrease the overvoltage on lines not injected with voltage up to 31.9 kVp. Changing the protection scheme when a phase-to-ground fault occurred by commanding the shunt reactor’s circuit breaker (CB) to open could prevent voltage rises in the undisturbed phase. A voltage relay was added in order to anticipate the occurrence of overvoltage when there was a disturbance in the line, and the shunt reactor CB could not open due to internal disturbances.
Teachable Machine: Deteksi Dialek Sumba Timur (Kambera) Menggunakan Layanan Open Source Edwin Ariesto Umbu Malahina
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.v12i4.8174

Abstract

This research seeks to develop a phonetic detection system for the Kambera dialect, the East Sumba local language, based on the TensorFlow framework that will be implemented in mobile applications. As part of this initiative, this research compiled a representative dataset of Kambera dialect phonetic samples. The main objective is to improve precision in phonetic recognition. Using the Kambera dialect as a case study, the data were extracted and trained using the open-source Teachable Machine service. This research adopted a convolutional neural network (CNN)-based approach combined with the Mel-frequency cepstral coefficients (MFCC) method for more accurate feature extraction. After data collection, model training, testing, and implementation, the model was integrated into the Android platform to benefit the public who wished to understand the Kambera dialect of East Sumba. The development and testing of this system were designed to detect and interpret the phonetics of the local language of East Sumba with the Kambera dialect, making a significant contribution to optimizing phonetic recognition and providing a dataset for ongoing research interests. It also serves as an accessible linguistics educational tool and supports linguistic inclusion and diversification in digital technology. Empirical evaluation showed that the overall average dialect detection precision rate reached 98.3% to 99.6%, with the user satisfaction rate reaching 99.33%. These results confirm that the developed system has a very efficient and good detection capability.

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