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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
 61   62   63   64   65 
Sistem Antropometri Lingkar Kepala Manusia berbasis Machine Vision Susetyo Bagas Bhaskoro; Sandy Bhawana Mulia; Afiq Hasydhiqi
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.v14i3.20175

Abstract

This study aims to develop an automated anthropometric system based on machine vision, integrated into a medical cyber-physical system (MCPS), to measure human head circumference. Head circumference is a critical parameter in growth monitoring, particularly for detecting abnormalities such as microcephaly and macrocephaly, which can affect cognitive development and overall health. To address this challenge, the study proposed an anthropometric system that enabled automated, accurate, and contactless measurements, accessible in real-time by healthcare professionals. The system was designed using a machine vision approach, incorporating object detection technology and elliptical model-based perimeter estimation to determine head circumference noninvasively. A 1,920 × 1,080-pixel (1080p) camera operating at 30 fps with a 60° field of view was mounted on a three-axis motion mechanism driven by stepper motors to automatically capture frontal and side views of the head. The measurement process began with head detection and bounding box adjustment to obtain head width parameters. Euclidean distance was used for measurement, followed by elliptical geometry modeling to estimate head circumference. Experimental results showed the lowest error rate of 2.29% at a distance of 50 cm under 300 lux lighting conditions. Performance evaluation using a confusion matrix yielded an accuracy of 92.8%, precision of 100%, recall of 97.5%, and F score of 98.7%. The proposed system provides an effective solution for healthcare professionals to perform growth screening quickly, accurately, and safely. It also supports remote healthcare services, particularly in areas with limited access to medical facilities.
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.
Interpretable Machine Learning untuk Prediksi Penempatan Kerja: Analisis Fitur Berbasis SHAP Swono Sibagariang
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.v14i3.20516

Abstract

Predictive modeling is important in analyzing graduates’ job outcomes, especially in forecasting job placements based on academic performance and courses. This study aims to improve predictive accuracy and interpretability in job placement classification using advanced machine learning models and SHapley Additive exPlanations (SHAP) analysis. Utilizing a dataset containing graduates’ academic records, including course grades, grade point average (GPA), and internship duration, this research employed several classification models, including decision tree, random forest, extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), CatBoost, and logistic regression. Evaluation metrics showed that most models achieve 92% precision, 92% recall, and 92% F1 score, with an accuracy of 85%, while logistic regression excelled with 100% recall, 96% F1 score, and 92% accuracy. SHAP analysis identified key features such as Administration, Computer Organization, Information Systems, Entrepreneurship, Professional Ethics, and Web Programming as the most influential in predicting job placement. Other significant contributors include Introduction to Information Technology, Software Engineering II, and Data Mining, although with relatively lower influence. Extracurricular activities and internship experiences were also found to be influential factors, highlighting the importance of academic and nonacademic elements in shaping graduates’ career prospects. These findings highlight and emphasize the need to provide students with certain academic courses to better prepare them for the job market. These findings emphasize the importance of interpretable machine learning models in career forecasting, enabling educational institutions to optimize curriculum design and enhance graduates’ employability. Future research should explore feature selection techniques, temporal analysis, and personalized recommendation systems to refine predictive accuracy.
Perencanaan Perluasan Pembangkitan dan Transmisi Berkelanjutan Menggunakan MOPSO-BPSO di Jaringan Listrik Astuty; Zainal Sudirman
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.v14i3.20795

Abstract

As of 2023, approximately 85% of power plants operating in South Sulawesi relied on fossil fuels, such as coal, gas, and oil. To meet the increasing demand for electricity while reducing carbon emissions, it is essential to integrate renewable energy sources into the power system. Renewable energy not only helps conserve fossil fuels but also supports global environmental sustainability. South Sulawesi possesses significant hydro potential, offering opportunities to develop both small and large-scale hydroelectric power plants (pembangkit listrik tenaga air, PLTA). This study employed a multi-objective particle swarm optimization (MOPSO) approach to develop optimal scenarios for generation expansion planning (GEP), and binary particle swarm optimization (BPSO) to determine the necessary transmission expansion planning (TEP). The planning process was supported by long-term load forecasting using the moving average method based on historical electricity demand data in South Sulawesi. Results showed that the proposed integrated GEP and TEP optimization framework successfully identified an optimal scenario maximizing renewable energy used while ensuring transmission reliability. By 2030, PLTA is projected to contribute 67.9% of total electricity generation. Meanwhile, steam-fired power plants (pembangkit listrik tenaga uap, PLTU) become the mainstay with capacities reaching 437.5 MW. To support this scenario, nine new transmission lines are needed, along with the expansion of 25 existing lines to accommodate increased power flow within the interconnection system.
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.
Explainable Artificial Intelligence Model for Pneumonia Detection: A Hybrid CNN-ViT and 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, such as diabetic ulcers, requires an innovative approach, where electrical stimulation (ES) has proven effective but is still dominated by an open-loop system that is less adaptive. This study aims to develop a closed-loop electrical stimulation system based on fuzzy control that adjusts the stimulation duration dynamically. The system is designed by integrating an electrical stimulator, fuzzy control, and a wound area reduction model. The Atmega32 microcontroller is used to regulate stimulation with fuzzy control. Preclinical testing on experimental animals to compare the effectiveness of the control method (without therapy), open-loop, and closed-loop. The test results show that the electrical stimulator circuit works according to specifications, with a signal frequency of 20 Hz, a pulse width of 250 µs, and a boost converter output voltage of 50V. The error in the maximum stimulation duration is 2.5%, which is still within the safe limit for wound therapy. Fuzzy control is proven to be effective in adjusting the stimulation duration based on wound development, with an estimation error of only 0.3%. Preclinical testing showed that the fuzzy-controlled closed-loop system accelerated wound healing with a 64–67% reduction in wound area in seven days, higher than open-loop (44–50%) and no therapy (37.5%). Closed-loop also produced the highest tissue density (75–100%) compared to open-loop (50%) and no therapy (25–50%), proving its effectiveness in accelerating tissue regeneration. Fuzzy-controlled closed-loop electrical stimulation was able to accelerate wound healing up to 1.5 times faster than the open-loop method and almost twice as fast as no therapy. Fuzzy control adjusted the stimulation duration in real-time, avoiding over- and understimulation. This system is more effective than conventional methods and has the potential to increase the acceleration of wound healing.
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.
Implementation of Programmable Logic Controller control system with IoT technology-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

Problems in control systems such as difficult access or long distances to give commands can be overcome by using Supervisory Control and Data Acquisition (SCADA). SCADA systems enable real-time monitoring and control of industrial processes, allowing operators to make informed decisions from remote locations. By integrating various data sources, SCADA improves operational efficiency and increases response time to potential problems. SCADA systems allow users to monitor production processes directly without having to be on site. It also increases data transparency, so that any failures can be monitored and identified by engineers or by analysis. This technology also poses, the challenge of high implementation costs, the need for skilled personnel to manage and maintain the system. Therefore, it is very important for the industry to consider the advantages for the adoption of SCADA technology. Therefore, this research aims to build a SCADA system that can be accessed by small and medium industries. This system uses a Programmable Logic Controller (PLC) as the controller and Human Machine Interface (HMI) as the interface on the Remote Terminal Unit (RTU). RTU sends data to the Master Terminal Unit (MTU) which is developed independently to be monitored and controlled remotely using Industrial IoT technology with Message Queueing Telemetry Transport (MQTT) protocol. The variables used in this system are temperature in RTU 1 and rotational speed in RTU 2, with the aim of measuring communication duration and data accuracy. The results showed that sensor accuracy reached 98.6% on RTU 1 and 100% on RTU 2. Communication accuracy between layers reached 100%, with real-time communication duration between layers of 0.324 seconds, making it very efficient in terms of cost. From the test results, the SCADA system based on IoT technology is reliable to be applied in small to medium industrial environments, easily replicated, and reconfigured.

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