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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 14 No 4: November 2025" : 10 Documents clear
User Experience Development in Elderly Heart Patient Monitoring System Alaric Rasendriya Aniko; Tien Fabrianti Kusumasari; Sinung Suakanto; Muhammad Ivan Fadilah
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.v14i4.18783

Abstract

Heart disease is a major global cause of death, particularly among the elderly. Elderly often face challenges in accessing healthcare due to physical and cognitive limitations, making remote health monitoring systems a crucial solution. However, the effectiveness of these systems depends heavily on a good user experience (UX), which is often a challenge for the elderly. This research aims to develop a user-centered design (UCD) method and design a remote patient monitoring prototype that is specifically tailored to the needs of the elderly. The research employed a design science research methodology (DSRM) and included an in-depth literature review, interviews with five elderly patients and two medical professionals, a needs analysis, and the development of the FlowBeat prototype. The developed UCD method consisted of seven phases, and its validity was assessed by six UI/UX experts using the content validity ratio (CVR) and the content validity index (CVI), including item-CVI (I-CVI) and scale-CVI (S-CVI). The results showed that the research users and test the design phases were rated as the most essential (CVR = 1) and relevant (I-CVI = 1). Conversely, the creating a personal manifesto phase scored the lowest (CVR = -0.33). The overall S-CVI score was 0.93, indicating strong content validity for most of the framework. In conclusion, the creating a personal manifesto and evaluate against requirements phases performed poorly, necessitating their removal. Furthermore, further research requires testing the prototype on elderly to ensure successful implementation in the real world.
Deteksi Pneumonia Menggunakan Explainable AI: Model Hibrid CNN–ViT dan Grad-CAM Atika Hendryani; Vita Nurdinawati; Agus Komarudin
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.v14i4.19822

Abstract

Pneumonia detection through medical imaging presents a significant challenge, particularly in regions with limited access to healthcare professionals. This study presents an explainable artificial intelligence (XAI) model that integrates convolutional neural network (CNN) and vision transformer (ViT) to enhance the accuracy of pneumonia diagnosis using chest X-ray images. The proposed research aims to enhance diagnostic accuracy by providing explanations through gradient-weighted class activation mapping (Grad-CAM) visualization. The methodology includes image preprocessing, local feature extraction via CNN, and global spatial relationship modelling using ViT. The model was trained on a preprocessed chest X-ray dataset and evaluated using standard performance metrics such as accuracy, precision, recall, and F1 score. The proposed CNN-ViT model was assessed using chest X-ray datasets for pneumonia detection. The experimental results demonstrated that the model achieved an accuracy of 96.5%, precision of 96%, recall of 96%, and F1 score of 94%, These results indicate that the integration of CNN and ViT effectively enhances classification performance and provides a reliable tool for medical image analysis. Furthermore, Grad-CAM visualizations highlight the critical regions in the images that influence the model’s predictions, thereby enhancing interpretability. Compared to conventional models, this approach offers improved transparency in AI-driven diagnostics. Consequently, the proposed model represents a promising and reliable diagnostic tool, particularly beneficial in underserved or remote areas with limited medical infrastructure. Additionally, this research opens opportunities for the development of transparent and XAI-based diagnostic systems.
Penerapan Kendali Fuzzy dalam Pengembangan Stimulasi Listrik untuk Mempercepat Penyembuhan Luka Rahmawati; Achmad Arifin; Duti Sriwati Aziz; Gunawan; Raisah Hayati; Siti Amra
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.v14i4.20232

Abstract

Chronic wound healing remains a clinical challenge due to the limited capacity of conventional therapies to accelerate tissue regeneration. Electrical stimulation (ES) offers a promising therapeutic modality; however, open-loop ES cannot adaptively adjust therapy duration. This study developed a closed-loop ES system incorporating fuzzy controller to dynamically regulate stimulation duration based on wound progression. The method integrates an Atmega32-based ES platform, fuzzy controller algorithms, and preclinical testing on guinea pigs. The ES system operates at a frequency of 20 Hz, a pulse width of 250 µs, and an output voltage of 50 V. The fuzzy controller adjusts stimulation duration within a range of 15–45 minutes according to the difference between the actual and target wound areas, achieving an estimation error of 0.3%. Preclinical evaluations compared the therapeutic effectiveness of closed-loop ES, open-loop ES, and no-ES conditions. Wound-area reduction over seven days in the closed-loop group reached 64–67%, higher than the open-loop (44–50%) and no-therapy (47%) groups. Closed-loop therapy also produced the highest tissue-density outcomes (75–100%), exceeding those of the open-loop (50%) and no-therapy (25–50%) groups. The fuzzy-controlled closed-loop ES accelerated tissue regeneration by approximately 1.5–2 times compared to open-loop and no-therapy conditions. Effectiveness rankings showed the closed-loop system achieving the highest scores (0.90 and 1.00), outperforming the open-loop (0.61) and no-therapy (0.51) groups. These findings indicate that fuzzy-controlled closed-loop ES provides superior wound-healing performance compared to conventional approaches, offering a more adaptive and precise therapeutic strategy with potential for broader medical application.
Spam Email Classification Optimization With NLP-Based Naïve Bayes on TF-IDF and SMOTE Andi Maslan; Azan Rahman; Umar Faruq; Rabei Raad Ali Al-Jawr
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.v14i4.20931

Abstract

The rapid advancement of information and communication technology has transformed the way humans interact and exchange information. Among various digital communication tools, email remains one of the most widely used; however, it is often exploited to send spam messages. Spam emails can contain phishing links, malware, or unsolicited advertisements, posing significant risks to individuals and organizations. Therefore, developing accurate and efficient spam detection methods is becoming increasingly important. This study proposes a lightweight and efficient spam email classification approach using the naïve Bayes algorithm combined with TF-IDF feature extraction and the synthetic minority oversampling technique (SMOTE) to address class imbalance. A series of preprocessing steps tokenization, lemmatization, stopword removal, and term frequency-inverse document frequency (TF-IDF) transformation were applied to normalize and vectorize email text data. The SMOTE technique was applied precisely to the training dataset to balance the class distribution and avoid data leakage during evaluation. Experimental results showed that the naïve Bayes model initially achieved 88% accuracy, 86% recall, 100% precision, and 92% F1 score. After proper application of SMOTE, the model achieved 100% accuracy, precision, recall, and F1 score, indicating perfect classification of spam and non-spam (ham) emails. These results confirm that proper class balancing significantly improves the model’s ability to detect spam emails. Overall, this study highlights the effectiveness of combining TF-IDF, naïve Bayes, and SMOTE as a robust yet computationally efficient solution for modern spam detection, particularly suited to real-time and resource-constrained environments.
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.
PLC Control System Implementation with IoT Based Industrial SCADA Development Hendro Priyatman; Seno D. Panjaitan; Supriono; Muh. Revaldi Frizky
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.v14i4.21506

Abstract

Control system challenges such as remote access and long-distance command execution can be addressed using supervisory control and data acquisition (SCADA) systems. SCADA enables real-time monitoring and control of industrial processes, allowing remote decision-making, improving operational efficiency, and accelerating response to issues. It also enhances data transparency, enabling engineers to detect and analyze failures more effectively. However, SCADA implementation often involves high costs and requires skilled personnel for maintenance. To address these limitations, this research developed an affordable SCADA system tailored for small and medium-sized industries. The system used a programmable logic controller (PLC) for control and a human machine interface (HMI) on the remote terminal unit (RTU). Data from the RTU were sent to a master terminal unit (MTU), which was independently developed and enabled remote monitoring and control via industrial internet of things (IIoT) technology using the message queueing telemetry transport (MQTT) protocol. The system monitored two variables: temperature (RTU 1) and rotational speed (RTU 2), aiming to measure communication time and data accuracy. Testing showed sensor accuracy of 98.6% for RTU 1 and 100% for RTU 2, with 100% communication accuracy between layers. The average real-time communication duration was 0.324 s, demonstrating high efficiency and low latency. These results indicate that an IoT-based SCADA system is reliable, cost-effective, and suitable for small to medium industrial applications. It is also easy to replicate and reconfigure, making it a practical solution for broader industrial adoption.
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.

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