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Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
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INDONESIA
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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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.
Studi dan Analisis Hyperparameter Tuning IndoBERT Dalam Pendeteksian Berita Palsu Anugerah Simanjuntak; Rosni Lumbantoruan; Kartika Sianipar; Rut Gultom; Mario Simaremare; Samuel Situmeang; Erwin Panggabean
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.v13i1.8532

Abstract

The rapid advancement of communication technology has transformed how information is shared, but it has also brought concerns about the proliferation of false information. A recent report by the Ministry of Communication and Informatics in Indonesia revealed that around 800,000 websites were involved in spreading false information, underscoring the seriousness of the problem. To combat this issue, researchers have focused on developing techniques to detect and combat fake news. This research centers on using IndoBERT-base-p1 for fake news detection and aims to enhance its performance through three methods to tune the hyperparameter value of the model namely: Bayesian optimization, grid search, and random search. After comparing the outcomes of the three hyperparameter tuning methods, Bayesian Optimization emerged as the most effective approach. Achieving a precision of 88.79%, recall of 94.5%, and F1-score of 91.56% for the “fake” label, Bayesian Optimization outperformed the other hyperparameter tuning methods as well as the model using the fine-tuning hyperparameter value. These findings emphasize the importance of hyperparameter tuning in improving the accuracy of fake news detection models. Utilizing Bayesian Optimization and optimizing the specified hyperparameters, the model demonstrated superior performance in accurately identifying instances of fake news, providing a valuable tool in the ongoing battle against disinformation in the digital realm.
Pemantauan dan Pengendalian Parameter Greenhouse Berbasis IoT Dengan Protokol MQTT Eni Dwi Wardihani; Eka Ulia Sari; Helmy; Ari Sriyanto Nugroho; Yusnan Badruzzaman; Arif Nursyahid; Thomas Agung Setyawan; Media Fitri Isma Nugraha
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.v13i1.8564

Abstract

Modernization in the agricultural sector is expected to have an effect on improving the quality, production quantity, and continuity of the agricultural product supply. Currently, many smart agricultures are developed in greenhouses. However, several greenhouse parameters must be considered to optimize plant growth. This study has created a monitoring and control system for several Internet of things (IoT)-based greenhouse parameters, allowing farmers to monitor and control the greenhouse anytime and anywhere. It can also improve the work efficiency of farmers in monitoring and controlling, especially if there are multiple greenhouses to be monitored or controlled. The greenhouse monitoring data may be viewed in real time and stored on servers, making it easier for farmers to evaluate greenhouses and crops. The monitored parameters were greenhouse temperature, greenhouse humidity, and light intensity in the greenhouse, while the controlled parameters were greenhouse temperature and greenhouse humidity, using exhaust fans. The process of transmitting the greenhouse parameter monitoring and controlling data was carried out using the message queue telemetry transport (MQTT) protocol. Data loss and delay testing on the system was required to determine the reliability of the tool in the process of transmitting and receiving data. The quality of service (QoS) testing results was as follows: average data loss gateway-server monitoring was 10.6%, the average gateway-server monitoring delay was 1.9 s, and the average server-gateway control delay was 7.1 s. When the greenhouse temperature parameter value is less than the specified maximum threshold, the system turns on the drum fan so that the temperature reaches the minimum value at the threshold limit.
Citra Tekstur Terbaik Untuk Gaussian Naïve Bayes Dengan Interpolasi Nearest Neighbor Irwan Budi Santoso; Shoffin Nahwa Utama; Supriyono
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.v13i1.8730

Abstract

One of the factors affecting the performance of the Gaussian naïve Bayes classifier (GNBC) in texture image classification is the image size (dimensions). Image size is one of the best texture image criteria besides its pixel value. In this study, a method is proposed to obtain the size of the best texture image for GNBC by nearest neighbor (NN) interpolation optimization. The best texture image size with interpolated pixel values makes GNBC able to distinguish texture images in each class with the highest performance. The first step of the proposed method was to determine the texture image size for training through a combination of row and column sizes in the optimization process. The next important step in generating the new texture images was resizing each of the original texture images using NN interpolation. The next step was to build GNBC based on the new image from interpolation and determine the classification accuracy. The last step was to select the best texture image size based on the largest classification accuracy value as the first criterion and image size as the second criterion. The evaluation of the proposed method was carried out using texture image data from the CVonline public dataset involving several test scenarios and interpolation methods. The test result shows that in scenarios involving five classes of texture images, GNBC with NN interpolation gives the smallest classification accuracy value of 89% and the largest 100% at the best image size, 14 × 32 and 47 × 42, respectively. In scenarios involving small to large class numbers, GNBC with NN interpolation provides classification accuracy of 81.6%–95%. From these results, GNBC with NN optimization gives better results than other nonadaptive interpolation methods (bilinear, bicubic, and Lanczos) and principal component analysis (PCA).
Penerapan Computation Offloading Pada Sistem Deteksi Pelanggaran Perlintasan Sebidang Berbasis Komputasi Tepi Rian Putra Pratama; Suhono Harso Supangkat
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.v13i1.8795

Abstract

Level crossings remain a problem in several cities due to high violations. Currently, surveillance at level crossings is still performed conventionally. Since problems at level crossings are increasingly complex and conventional solutions are no longer effective, an intelligent video surveillance system is necessary. Intelligent video surveillance system implementation is a complex task and requires devices with extensive computing resources. This research aims to optimize the system for processing data in real-time by conducting computation near the data source and dividing computing tasks across several edge devices. This research proposes a solution in the form of an edge computing-based intelligent video surveillance system with a computation offloading method on limited devices. This research has two development stages. The initial stage involved developing an object detection model using a dataset of level crossings in Bandung City. The second stage was developing an edge computing-based system by applying the computation offloading method on limited computing devices. The edge computing method extends cloud computing to the network’s edge, enabling calculations near the data source. Conversely, the computation offloading method improves edge computing performance by dividing computing tasks. Results showed an increase in computing speed of around 1.5 times faster, with a violation detection accuracy rate reaching 89.4%. Additionally, GPU temperature decreased by 5.50 °C, GPU usage decreased by 44.05%, memory usage decreased by 301 Mb, and power consumption decreased by 2.28 W. The system developed is effective and efficient in optimizing the performance of the violation detection system in level crossings on limited computing devices.
Implementasi Deteksi Penggunaan Masker Menggunakan SVM dan Haar Cascade Pada OpenCV Hustinawaty; Muhammad Farell
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.v13i1.9292

Abstract

Despite a decline in global COVID-19 cases, the persisting threat of SARS-CoV-2 coupled with waning public awareness of the virus threat has raised concerns. A notable number of individuals disregard mask usage or do so incorrectly. It is particularly concerning given that COVID-19 has high transmissibility, especially in crowded areas like shopping centers. Enforcement officers often face challenges in identifying those wearing masks improperly. Herein lies the significance of automated mask detection to aid enforcement officers in containing the spread of the virus. Hence, this paper aims to highlight the importance of automated mask detection in combatting COVID-19 transmission. Previous mask detection algorithms were intricate because they relied heavily on resource-intensive machine learning algorithms and libraries. These algorithms, however, failed to address the problem of incorrect mask usage adequately. Therefore, despite the apparent usage of masks, the virus managed to find transmission pathways. In contrast, this research focuses on creating algorithms that pinpoint improper mask usage and optimize resource utilization without compromising detection quality. The Haar cascade algorithm was utilized to detect faces and the support vector machine (SVM) was used to train the dataset. The model attained an average accuracy of 95.8%, precision of 99.7%, recall of 92.3%, and F1-score of 93.7%. The metrics aligned with prior studies, affirming their reliability. Nevertheless, limitations exist as the model faces challenges in detecting obscured facial features, requiring further research to enhance its detection capabilities. This research contributes to ongoing efforts to improve mask detection technology for more effective virus containment.
Sistem Berbasis Komputasi Kabut Untuk Sistem Parkir Pintar Terdesentralisasi Menggunakan Firebase Haposan Yoga Pradika Napitupulu; I Gde Dharma Nugraha
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.v13i1.10095

Abstract

The growth of vehicle number is unavoidable whilst the availability of parking is not directly proportional with this condition. Nowadays, many shopping centers do not have sufficient parking spot, causing customers to have difficulty in finding available parking spots. Research has been conducted to tackle the issue of finding available parking spots. Much of this research proposed the narrowband-Internet of things (NB-IoT) as a fog node. For communication purposes, this NB-IoT-based fog node has some shortcomings, such as security and privacy, lower data rate, higher cost in development, dependency with wireless system, and only covers one area. In this research, the fog computing was proposed to decentralize smart parking system by using Firebase to cover several areas or malls in one system and interface. Instead of using NB-IoT, this research employed decentralized local server as a fog node to deliver a fast data exchange. Firestore database (Firebase) was also used to secure, manage, and analyze the data in the cloud. Conjunctively, the Android application was created as a user interface to book and find the availability of parking spots. The Android application was built using Android Studio and implemented authentication to keep the data access secure and private. The testing scenario was done following the design unified modeling language (UML). The research results confirmed that the fog computing system successfully supported the decentralized smart parking system and was able to be implemented for covering several areas or malls in one system.
Evaluasi Kinerja PLTS On-Grid 600 kWp pada Jaringan Distribusi Gili Trawangan Rifky Irawan; Fransisco Danang Wijaya; Adha Imam Cahyadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 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.v13i2.6495

Abstract

Indonesia is one of the countries that remains reliant on the utilization of fossil energy. The increasing demand for fossil energy is causing a decrease in the availability of fossil energy, consequently leading to an increase in fossil fuel prices. Therefore, one of the efforts that can be undertaken involves establishing new renewable energy (NRE) plants to diminish reliance on the use of fossil-fueled plants. One of the NRE plants built by the government is the 600 kWp Gili Trawangan On-Grid solar power plant. After ten years of operation, an evaluation of the 600 kWp Gili Trawangan On-Grid solar plant is necessary to assess its performance in meeting electricity load requirements. The power and electrical energy generated by a solar power plant can be evaluated by comparing its current power and energy production with the potential power and energy that should be generated. To determine the current power and energy production generated by the solar power plant, the measurement results from the PLN. Additionally, to ascertain the potential power and energy that the solar power plant should be capable of producing at this time, simulation results from Homer software were employed. The results demonstrate that the power and energy measured by PLN are lower than the potential power and energy that should be achievable using Homer software for solar power plants. According to PLN measurement, the average power production and energy were 196.72 kW and 765.92 kWh, respectively. Meanwhile, based on the simulation results were 207.10 kW and 840.39 kWh.
Routing Region Adaptif Mandiri pada Lalu Lintas Heterogen Jaringan Sensor Nirkabel Muhammad Nur Rizal; P. Delir Haghighi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 3: Agustus 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.v13i3.8020

Abstract

The paper presents a new routing scheme using the information on the locations of nodes to create a routing region that controls the region of packet routing to achieve route optimization. The proposed scheme aimed to reduce the occurrence of packet detours or other routing overheads caused by the undirected packet transmission. The strength of this approach is that it can improve the lifetime of nodes in the network while decreasing the time taken for a packet to arrive at its destination or base station (BS). The proposed scheme used a self-adaptive algorithm that dynamically adjusted the routing region based on the BS’s calculation of the network layer parameters to achieve energy efficiency while satisfying data quality. The routing region limits the area of routing and restricts data flooding in the entire network, which potentially will waste resources and cause data redundancy. The simulation showed that the proposed scheme outperformed, the original fitness scheme and SPEED, according to energy consumption, transmission delay, throughput, and reliability (packet delivery ratio) under different congestion levels. The proposed scheme offered double the throughput and shortened packet delay by 20%. Furthermore, it had a longer lifetime, exceeding other schemes by approximately twofold when the traffic was not too congested. However, the gap decreases when the network becomes worse.
Analisis Dampak Skenario Net Zero Emission terhadap Penyediaan Energi Nasional dengan LEAP Widhiatmaka; Joko Santosa; Nona Niode; Nurry Widya Hesty; Afri Dwijatmiko; Prima Trie Wijaya; Agus Nurrohim; Arif Darmawan; Erwin Siregar
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 3: Agustus 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.v13i3.9012

Abstract

The achievement of the national energy supply target based on new and renewable energy (NRE) by 2025, as stated in the National Energy Policy, is still far below expectations. This shortfall is due to the continued fossil energy dominance in all sectors. To achieve net zero emission (NZE) targets by 2060, systematic and consistent transitions from fossil fuels to NRE are essential. The fossil energy utilization (domestic and imported) is expected to decline, while the substitution with NRE will increase. This study aimed to provide a forecast analysis of national energy supply and utilization across various sectors, including household, industry, power generation, transportation, and commercial sectors, until 2060. The analysis used energy modeling simulations with business as usual (BAU) and NZE scenarios, conducted using the Low Emission Analysis Platform (LEAP) software. LEAP is an integrated, scenario-based energy model used to determine energy demand, production, and resource extraction across all economic sectors. The simulation results for the NZE scenario indicate significant reductions in fossil energy usage across all sectors compared to the BAU scenario, with an increase in NRE utilization, especially in the power generation sector. By 2060, domestic coal, natural gas, fuel oil, and liquefied petroleum gas supplies are projected to decrease by 81%, 74%, 87%, and 84%, respectively; meanwhile, the demand for petroleum remains unchanged. Overall, the supply of NRE under the NZE scenario is expected to grow by an average of 9% per year from 2019 to 2060, amounting to 2.3 times the supply in the BAU scenario.

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