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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 667 Documents
 63   64   65   66   67 
Application of You Only Look Once (YOLO) Method for Sign Language Identification Reni Triyaningsih; Pradita Eko Prasetyo Utomo; Benedika Ferdian Hutabarat
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.21931

Abstract

Limited understanding of sign language has widened the social gap for deaf people, creating barriers in communication and social interaction. To address this challenge, technology-based solutions are required to facilitate inclusive communication. Deep learning-based detection methods, particularly the You Only Look Once (YOLO) algorithm, have gained attention for their speed and accuracy in real-time object detection. This research aims to develop and evaluate a YOLO training model for the identification of Indonesian sign language system (sistem isyarat bahasa Indonesia, SIBI). The dataset was obtained from resource person at the State Special School Prof. Dr. Sri Soedewi Masjchun Sofwan, SH. Jambi, and enriched with additional images collected from external subjects. Augmentation techniques with Roboflow were applied to expand the dataset, and several training schemes were implemented. Model performance was assessed using confusion matrix while considering accuracy and indications of overfitting. The results showed that the quality and quantity of training data, as well as the epoch values, strongly influenced the accuracy of the trained model. The best performance was achieved with 40 primary images per label class, augmented to 60 images, and trained over 24 epochs, resulting in a confusion matrix accuracy of 99.9%. The implemented model was able to recognize SIBI gestures in real-time using a webcam with fast processing. Overall, the proposed YOLO-based model successfully identifies sign language in real-time and demonstrates strong potential for reducing communication barriers among deaf people. However, further refinement and expansion of the dataset are recommended to improve effectiveness and enable broader real-world applications.
Sentiment Analysis Review Threads Google Play Store with RoBERTa Model Natan Kharisma A; Dewi Lestari; Gatot T Pranoto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.22038

Abstract

The rapid development of internet technology globally, including in Indonesia, has drastically changed communication and interaction patterns between individuals. One impact is seen in the increasing use of text-based social media applications, such as Threads, developed by Meta. Within a short time, Threads managed to attract millions of users. However, the large number of user reviews on the Google Play Store presents its own challenges, particularly in manual sentiment analysis, which is very time-consuming and prone to bias. This research aims to overcome these challenges by implementing a variant of bidirectional encoder representations from transformers (BERT), the robustly optimized BERT pretraining approach (RoBERTa) model, which has been optimized for natural language processing. The research process followed the cross-industry standard process for data mining (CRISP-DM) framework, including several main stages: understanding the business context, data exploration and model building preparation, performance evaluation, and model deployment. Data were obtained directly from the Google Play Store and then cleaned through deduplication, normalization, and tokenization stages. The RoBERTa model demonstrated strong performance, with an accuracy of 88%. Precision was recorded at 92% for positive sentiment and 81% for negative sentiment, while recall was at 88% and 87%, respectively. The F1 score was also high, at 90% for positive and 84% for negative sentiment. When compared to algorithms like naïve Bayes and support vector machine (SVM), RoBERTa proved superior. This research opens opportunities for exploring other transformer models or using ensembles to improve performance in the future.
Adaptive PID Auto-Tuning Algorithm on Omron PLC for Speed Control and Stability Nanang Rohadi; Liu Kin Men; Akik Hidayat
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.22693

Abstract

Speed regulation of three-phase induction motors under varying load conditions presents a major challenge in industrial automation due to their nonlinear dynamic behavior. This paper proposes an adaptive speed control system using a proportional-integral-derivative auto-tuning (PIDAT) algorithm implemented on the Omron CP1H-XA40DT-D programmable logic controller (PLC). The initial PID parameters were derived using the Ziegler–Nichols method, and the system continuously monitored the steady-state error during operation. When the error exceeded a predefined 5% threshold, the auto-tuning sequence was triggered. This sequence included a relay feedback test (RFT), system identification using a first order plus dead time (FOPDT) model, and real-time PID parameter recalculation. The system hardware integrated an Omron 3G3MX2 inverter, rotary encoder, and NB7W-TW01B human–machine interface (HMI) to form a closed-loop control structure. Experimental validation was performed under both spontaneous and constant load conditions. The PIDAT method consistently demonstrated superior performance compared to classical Ziegler–Nichols tuning, achieving steady-state errors in no-load tests below 1.70 % and under 0.8% in loaded conditions. Furthermore, the system achieved settling times below 9 s and recovered from load disturbances in less than 4 s. These results validate the proposed PIDAT system as an accurate, fast, and adaptive control solution, reducing the need for manual tuning and enhancing robustness in dynamic industrial environments.
Virtual Inertia Control Topology Addressing Indonesia’s Low-Inertia Renewable Grid Resilience Challenge F. Danang Wijaya; Fikri Waskito; Eka Firmansyah; Juan C. Vasques
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.25234

Abstract

The increasing penetration of renewable energy sources in Indonesia, particularly photovoltaic (PV) systems, into electric power grids has led to a reduction in system inertia, potentially compromising frequency stability during disturbances. This paper proposes a virtual inertia control method for single-phase rooftop PV inverters to enhance frequency response in low-inertia microgrids. A single-phase synchronverter model based on the swing equation was developed and tested on the IEEE 13-bus system. Three scenarios were evaluated: a solar-only microgrid, a wind-integrated microgrid, and a microgrid combining renewable sources with a synchronous generator. Simulation results demonstrated that the proposed virtual inertia method improved frequency and voltage stability, closely mimicking the response of traditional synchronous generators. Within the first 10 s following a disturbance, the system failed to restore its frequency to the nominal value due to insufficient inertia in the inertial response time range. This indicates that the initial 10 s are a critical period for frequency recovery. The poorest frequency response was observed in scenario 1 (solar-only configuration), where system inertia was the lowest among the three scenarios, while the hybrid configuration with a synchronous generator (scenario 3) provided the most stable and robust frequency performance. The findings support the recommendation to implement policies requiring rooftop PV systems to incorporate virtual inertia functionalities, ensuring greater system resilience as renewable energy penetration increases.
Analisis Area Wajah Berdasarkan Tekstur Wajah untuk Mengidentifikasi Risiko Penyakit Jantung Koroner Budi Sunarko; Agung Adi Firdaus; Yudha Andriano Rismawan; Anan Nugroho
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i1.13658

Abstract

Early screening for coronary heart disease (CHD) remains insufficiently addressed, underscoring the need for a more effective screening tool. Previous studies have reported a classification accuracy of only 72.73%, which is inadequate. This study aimed to develop and evaluate a machine learning model or diagnose CHD using facial texture features and to compare the performance across different facial regions to provide recommendations for improvement. The research involved constructing a machine learning model that extracted texture features from six facial regions of interest (ROIs) using the gray level co-occurrence matrix (GLCM) and employed an artificial neural network (ANN) algorithm. The datasets were full-face images of CHD patients (positive) and healthy people (negative). The face parts identified were the right crow’s feet, right canthus, nose bridge, forehead, left canthus, and left crow’s feet. A total of 132 (72 positive and 60 negative CHD) datasets were divided into 80% (n = 106) training data and 20% (n = 26) testing data. The developed model achieved a notable accuracy of 76.9%. The findings revealed that two facial regions—canthus and forehead—demonstrated excellent accuracy of 80.97% and 90%, respectively. Meanwhile, the crow’s feet and nose bridge regions showed good accuracies at 73.50% and 65%, respectively. Based on the results, this research has proven to be able to become a model for early CHD screening with good accuracy and faster execution.
Penggunaan Struktur CSRR untuk Peningkatan Kinerja BPF Berbasis Substrate Integrated Waveguide Junas Haidi; Novelita Rahayu; Achmad Munir
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 15 No 1: Februari 2026
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v15i1.18846

Abstract

Makalah ini mengeksplorasi pemanfaatan struktur complementary split ring resonator (CSRR) untuk meningkatkan kinerja bandpass filter (BPF) berbasis substrate integrated waveguide (SIW). BPF berbasis SIW yang didesain menggunakan material RO 4003C dengan nilai permitivitas 3,38 dan rugi-rugi dielektrik 0,0027, dibuat dalam bentuk empat persegi panjang dengan ukuran 37,5 mm (panjang) × 35 mm (lebar) × 1,52 mm (tinggi). BPF berbasis SIW terdiri atas permukaan SIW dengan ukuran 22,4 mm (panjang) × 35 mm (lebar) dan 28 via. Pada bagian permukaan SIW dibuat CSRR berbentuk persegi panjang berukuran 4 mm (panjang) × 4 mm (lebar). Untuk mengoptimalkan kinerja BPF berbasis SIW, 12 CSRR dikonfigurasi menjadi enam baris dan dua kolom. Berdasarkan hasil eksplorasi yang telah dilakukan, jarak antara baris dan kolom CSRR secara substansial memengaruhi kinerja BPF. Makin dekat jarak baris antar-CSRR, makin jauh pergeseran frekuensi kerja kedua BPF ke arah frekuensi rendah. Penambahan CSRR pada BPF berbasis SIW berhasil menurunkan nilai koefisien kopling dari 0,38 menjadi 0,28. Penambahan CSRR pada BPF berbasis SIW telah menyebabkan nilai koefisien transmisi (S21) menurun dari -2,32 dB menjadi -0,70 dB, yang berarti meningkatkan kinerja BPF sebesar 1,62 dB. Penambahan CSRR pada BPF berbasis SIW juga telah menurunkan nilai koefisien refleksi (S11) dari -4,56 dB menjadi -10,96 dB atau meningkatkan kinerja BPF sebesar 6,4 dB.
Perbandingan Algoritma Sine Cosine dan Kelelawar untuk Penempatan Pembangkit Listrik Terdistribusi Lindiasari Martha Yustika; Jangkung Raharjo; Rifki Rahman Nur Ikhsan; I Gede Putu Oka Indra Wijaya
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 3: Agustus 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i3.19191

Abstract

The enhancement of electricity distribution is a crucial factor in supporting sustainable development and reducing energy access inequality. To ensure the reliability and stability of energy systems, the integration of distributed generation (DG) has a significant role. Numerous studies have explored optimal DG placement using metaheuristic methods. The study evaluated the performance of both algorithms based on key indicators, including voltage profile improvement and power loss reduction, under normal load conditions and under a 10% load increase to simulate future demand growth. The methods employed were the sine-cosine algorithm (SCA) and the bat algorithm (BA). By comparing these two methods, this study aims to optimize the placement and sizing of DG units, with a case study based on the IEEE 9 bus system configuration. Load flow analysis was performed using Electric Transient Analysis Program (ETAP) software to validate the effectiveness of optimized DG placement under various scenarios. Key performance indicators, namely losses reduction and improvement of voltage profile, were evaluated to determine the relative strengths of each algorithm. The results show that both SCA and BA are effective in optimizing DG implementation. Specifically, SCA achieved reductions in active power losses by up to 85% and reactive power losses by 93%, outperforming BA in certain scenarios. Both algorithms enhance system reliability and stability. These findings highlight the potential of metaheuristic algorithms to address the challenges of modern energy systems and contribute to the broader goal of developing sustainable power systems.
Integrasi IoT pada Evaluasi Efisiensi Panel Surya Off-Grid pada Beban Resistif dan Induktif Aripin Triyanto; Akbar Maulana; Joko Tri Susilo; Yoyok Dwi Setyo Pambudi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 15 No 1: Februari 2026
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v15i1.20351

Abstract

Energi surya merupakan salah satu sumber energi terbarukan yang banyak dimanfaatkan, terutama dalam sistem off-grid. Namun, efisiensi konversinya dipengaruhi oleh jenis beban yang digunakan. Beban resistif dan induktif memiliki karakteristik konsumsi daya yang berbeda, sehingga memengaruhi kinerja panel surya. Oleh karena itu, diperlukan analisis mengenai dampak kedua jenis beban terhadap efisiensi panel surya, dengan pemantauan berbasis internet of things (IoT) untuk pengumpulan data secara real-time. Penelitian ini bertujuan untuk menganalisis pengaruh penggunaan beban resistif (lampu pijar) dan induktif (kipas) terhadap efisiensi sistem panel surya off-grid 50 Wp serta mengevaluasi efektivitas IoT dalam pemantauan kinerja sistem. Metode yang digunakan meliputi pengujian dengan menghubungkan panel surya ke kedua jenis beban tersebut. Parameter yang diamati mencakup tegangan, arus, dan daya yang dihasilkan oleh panel surya serta daya yang dikonsumsi oleh masing-masing beban. Data dikumpulkan menggunakan sensor dan dikirim ke platform IoT untuk dianalisis secara jarak jauh. Hasil penelitian menunjukkan bahwa beban resistif menghasilkan efisiensi lebih tinggi, berkisar antara 44,47% hingga 49,54%, dibandingkan dengan beban induktif yang hanya mencapai 39,61% hingga 48,12%. Efisiensi yang lebih rendah pada beban induktif disebabkan oleh komponen reaktif yang menurunkan faktor daya dan kinerja sistem. Maka, dapat disimpulkan bahwa jenis beban berpengaruh signifikan terhadap efisiensi panel surya off-grid dan implementasi IoT terbukti efektif dalam pemantauan kinerja sistem secara real-time.
Analisis Sentimen Terhadap Ulasan Aplikasi IKD di Play Store Menggunakan Random Forest Kelvin H.; Erlin; Yenny Desnelita; Dwi Oktarina
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 3: Agustus 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i3.20473

Abstract

The rapid growth of digital applications in population administration services has increased the importance of sentiment analysis to understand user perceptions more deeply. This study focuses on the Digital population identity (Identitas Kependudukan Digital, IKD), a digital identity application developed by the Indonesian government. It aims to classify user reviews of the IKD application into positive, neutral, and negative sentiments using the random forest algorithm. The dataset consisted of 28,134 user reviews from the Google Play Store, including usernames, review texts, timestamps, and star ratings. The research stages included data preprocessing, labeling, handling missing values, and text processing (cleansing, tokenizing, stopword removal, and stemming). The data were divided into 80% training and 20% testing sets. The best-performing model used the parameters: max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, and n_estimators=300, achieving an average accuracy of 83.78%. To address class imbalance, the synthetic minority oversampling technique (SMOTE) was applied, resulting in improved performance with an accuracy of 86.29%. Evaluation metrics before SMOTE showed 83.85% accuracy, 80.40% precision, 83.85% recall, and 81.73% F1 score. After SMOTE, precision increased to 81.22%, while accuracy and recall slightly decreased to 80.86%, with an F1 score of 81.03%. Furthermore, sentiment trend analysis using N-gram techniques (unigram, bigram, trigram) was conducted to identify frequently mentioned topics and user concerns. These insights support the research objective of guiding application improvements aligned with user needs and enhancing the overall digital service experience.
Optimization of Parallel Neural Network Layer Configuration in English Text Sentiment Analysis Nugroho, Agung; Arief Setyanto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.21069

Abstract

Accuracy in analyst sentiment classification is very important so that the trained model can be implemented well to make business decisions. Researchers proposed a method for configuring neural network models arranged in parallel to improve classification accuracy. The results of the first stage, a bidirectional long short-term memory (Bi-LSTM) algorithm with Keras embedding with a sequential layer configuration, produced the best accuracy of 80.20%. The results of this first stage served as the baseline to be used as a reference for the combination in the second stage of the experiment. In the second stage of the experiment, a combination of the Bi-LSTM algorithm with other algorithms was carried out in parallel, such as gated recurrent unit (GRU), recurrent neural network (RNN), and Simple RNN with Keras embedding. It was found that the combination of three parallel layers of GRU-BiLSTM-RNN with Keras Embedding produced the highest accuracy for sentiment analysis of three classes, with a value of 88%. A statistical test of the t-test method was carried out with a critical p-value of 0.05 to prove the accuracy that has been produced between the sequential and the parallel configuration. The results of the t-test between the sequential configuration and the parallel configuration obtained a p-value of 0.5e-9 which is much smaller than the critical p-value of 0.05 so that in statistical testing the average accuracy produced from the two configurations is significantly different.

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