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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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Ensemble Voting Classifier Berbasis Multi-Algoritma dan Metode SMOTE untuk Klasifikasi Penyakit Jantung Dede Kurniadi; Asri Indah Pertiwi; Asri Mulyani
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
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.v14i2.17157

Abstract

The heart is a vital organ responsible for pumping blood throughout the body. Hence, impairments can disrupt blood circulation and are the leading causes of mortality worldwide. World Health Organization (WHO) reported that, in 2021, the mortality rate attributed to heart disease reached a significant number. In Indonesia, the prevalence of heart disease attained 1.5%. Consequently, it is essential to prevent and detect heart disease at an early stage utilizing machine learning technologies. This study aims to develop a heart disease classification model using the naïve Bayes and random forest algorithms through the ensemble voting classifier approach. The data were obtained from Kaggle, comprising 1,000 records with 14 variables, including one classification target. Imbalanced data were handled using the synthetic minority oversampling technique (SMOTE), while feature selection was conducted in consultation with cardiologists to ensure clinical relevance. The model was trained using the naïve Bayes algorithm, random forest, and integration of both through the ensemble voting classifier method, in contrast to previous studies that only compared several algorithms to determine the highest accuracy. The test results showed that the model trained with the ensemble voting classifier yielded the best performance, with an accuracy, precision, recall, and F1 score of 98.28%, 98.41%, 98.41%, and 98.41%, respectively. This study demonstrates that the ensemble voting classifier method provides better accuracy than the individual algorithms. This model falls within the excellent classification category and is expected to contribute to the medical field and support the development of decision-support systems for diagnosing heart disease.
Studi Komparasi Kinerja Object-Relational Mapping Berdasarkan Implementasi Data Source Architectural Pattern Muhammad Rezy Anshari; Redi Ratiandi Yacoub; Herry Sujaini; Bomo Wibowo Sanjaya; Eva Faja Ripanti
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
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.v14i2.17315

Abstract

Object-relational mapping (ORM) is a technique that maps in-memory objects and tables in the database, implementing data source architectural patterns (DSAP), namely Data Mapper and Active Record. These patterns require comparison due to performance difference indications and their significant roles in a system’s business processes. This study aims to compare and analyze execution duration and memory consumption quantitatively, and functions influencing them in the ORM, utilizing Data Mapper and Active Record. The objects were Doctrine (Data Mapper) and Eloquent (Active Record). The performance profiling in the ORM was conducted as a library rather than a framework. This profiling encompassed create, read, update, and delete (CRUD) and lookup operations based on specified measurement metrics, conducted using variations in the number of database records. The profiling process was automated using a script, leveraging a combination of Xdebug and Apache Benchmark. The analysis employed using Kcachegrind and big O notation, resulting in performance graphics, relative percentage differences, and functions’ contributions to the performance. Results showed that memory consumption outperformed Data Mapper. Data Mapper was superior in execution duration in most operation combinations and metrics. Function groups of database transactions, object serialization, and retrieval records were the primary contributors to the performance. Object and database synchronizations became additional contributors to Active Record. The complexity of the largest contributor functions in Data Mapper was higher than that of Active Record. Future studies can utilize automation concepts in the profiling process and substitute Xdebug according to the requirements of the programming languages used by the ORM.
Deteksi Transaksi Penipuan pada Sektor Perbankan Menggunakan Ruled-Based Model dan Pembelajaran Mesin Cut Dinda Rizki Amirillah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
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.v14i2.17410

Abstract

This research aims to develop an effective fraud detection model in banking transactions using the rule-based model (RBM) approach and the isolation forest (IF) machine learning algorithm. Based on data from the Ministry of Communication and Information Technology, there were more than 405,000 online fraud cases during the 2019–2022 period, indicating the need for a reliable fraud detection system to protect customers. The research method involves collecting banking transaction data for four months through channels such as ATM, internet banking, and mobile banking. The RBM model was used as an initial approach, detecting suspicious transaction patterns based on defined rules. However, it has limitations in detecting transactions that are not defined in the rules. To complement this shortcoming, this research implemented IF, an effective unsupervised learning model for detecting anomalies using the isolation tree (iTree) technique to identify suspicious transactions. The results showed that the IF model could detect anomalous patterns not covered by RBM, thereby improving the accuracy of fraud transaction identification. The precision data of 99% indicates that the model’s predictions of anomalies are indeed anomalies, while a recall value of 1.0 shows that the model successfully identified all anomalies in the dataset. In conclusion, the combination of RBM and IF provides a comprehensive approach to fraud detection in the banking sector. IF’s ability to detect anomalies more dynamically and accurately can reduce fraud losses in the industry.
Memajukan Transisi Energi Indonesia: Studi Simulasi Coal to Nuclear (C2N) Irfan Eko Budiyanto; Sinta Uri El Hakim
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
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.v14i2.17426

Abstract

Indonesia’s ambitious net-zero emissions (NZE) target for 2060 necessitates a transformative shift from coal-dependent power systems to nuclear energy sources. Although previous studies have explored various aspects of energy transitions, limited attention has focused on the possibility of replacing existing coal-fired boilers with nuclear reactors while maintaining the input and output parameters of the current infrastructure. This paper presents an approach to facilitating Indonesia’s nuclear energy transition by a coal-to-nuclear (C2N) simulation study. Specifically, the focus is on the 400 MW coal-fired power plant (CFPP), which relies on coal combustion. This study was designed to model the conversion process of the coal combustion system to a modular nuclear reactor setup while preserving the existing steam turbine and generator infrastructure. The foundation of this study was that the replacement of the nuclear reactor-based heat source must meet the requirements of the pre-existing water and steam cycle design. Various configurations for substituting the current boiler with a nuclear reactor were analyzed in this study, by considering engineering, operational, and modeling aspects. Results from model simulations for nine different operating conditions showed a deviation of the main-steam temperature of about 3% of the design value, starting at 120 MWe and above. Nevertheless, all other parameters of the conversion simulation results demonstrated a very small deviation. The deviation was close to the actual existing operational conditions of the previous CFPP. This paper highlights how the simulation demonstrated a promising integration of legacy infrastructure with emerging nuclear technology.
Pengembangan Sistem Deteksi Kantuk Menggunakan YOLOv9 untuk Keselamatan dalam Berkendara Fernando Candra Yulianto; Wiwit Agus Triyanto; Syafiul Muzid
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
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.v14i2.18701

Abstract

Drowsiness detection systems play a crucial role in safe driving, considering the high rate of traffic accidents caused mainly by drowsiness. Several drowsiness detection systems built using the eye aspect ratio (EAR), percentage of eyelid closure (PERCLOS), and convolutional neural network (CNN) methods still have limitations in terms of accuracy and response time. This study aimed to overcome these problems by applying the You Only Look Once version 9 (YOLOv9). This method has advantages in terms of speed and accuracy because it can detect objects in real-time in one processing stage. The dataset was collected independently from several sources in a real environment inside the vehicle with various lighting and viewing angles; then, labeling, preprocessing, and modeling processes were conducted. The model performance was evaluated based on precision, recall, F1 score, and mean average precision (mAP) metrics. The best model was optimized using several optimization techniques to determine the most optimal results. The results indicate that the YOLOv9 model trained using Nesterov-accelerated adaptive moment estimation (Nadam) optimization has a better image processing speed than other models. This model yielded a precision, recall, F1 score, mAP@50, mAP@50, mAP@50-95, and processing speed of 99.4%, 99.6%, 99.5%, 99.5%, 85.5%, and 52.08 FPS, respectively. The developed model can detect drivers’ drowsiness signs, such as closed eyes, yawning, abnormal head positions, and unnatural hand movements, in real time. However, this model still has limitations in detecting drivers wearing sunglasses, so further development is needed to improve its performance in these conditions.
Analisis Perbandingan Kinerja Pola MVVM dan MVP pada Sistem Dasbor Android Fajar Pradana; Raziqa Izza Langundi; Djoko Pramono; Nur Ida Iriani
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 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.v14i2.18985

Abstract

The rapid growth of the Android market in various developing countries has driven the demand for higher-quality applications. Developing Android-based applications presents specific challenges, such as the need for responsive designs and optimization for devices with diverse specifications. Design patterns like model-view-controller (MVC), model-view-presenter (MVP), and model-view-viewmodel (MVVM) have become popular approaches to address these issues. However, studies on the performance of design patterns in Android applications, especially in modern programming languages like Kotlin, remain limited. This research aims to compare the performance of the MVP and MVVM design patterns in an Android-based boarding house management application, KosGX. This application utilized Kotlin and featured an interactive dashboard requiring significant device resources. Testing was conducted by measuring performance across three key aspects: central processing unit (CPU) usage, memory usage, and system response time. The results of the study showed that MVVM outperformed in CPU efficiency, with an average usage of 8.92% compared to 11.15% for MVP. In terms of memory usage, MVVM was also slightly more efficient, with an average usage of 121.48 MB compared to 121.55 MB for MVP. However, MVP excelled in response time, averaging 236.88 ms, whereas MVVM reached 252.68 ms. This study underscores that the choice of design pattern affects application performance. MVVM is more efficient in CPU and memory usage, while MVP offers better response times. These findings provide valuable insights for developers in selecting the optimal design pattern based on the specific needs of their applications.
Deteksi Berita Hoaks Berbahasa Indonesia Menggunakan One-Dimensional Convolutional Neural Network Muhammad Zuama Al Amin; Muhammad Ariful Furqon; Dwi Wijonarko
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
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.v14i2.19050

Abstract

The rapid advancement of information technology has enabled global information dissemination and led to a surge in hoax news, particularly in Indonesia. Hoax news poses a significant risk of spreading disinformation, potentially influencing public opinion, social stability, and security. Therefore, an effective technology-based solution is required to detect and identify hoaxes. This study aims to develop and optimize a one-dimensional convolutional neural network (1D-CNN) model to detect hoax news with high accuracy. The dataset comprised 12,151 articles, including 5,276 valid news items and 6,875 hoax news items, collected from reliable sources and anti-hoax platforms. The text preprocessing stages included data cleaning, case folding, punctuation removal, number removal, and stopword removal. The textual data were processed through tokenization and padding stages for model training preparation. The proposed 1D-CNN architecture integrated embedding, Conv1D, batch normalization, globalmaxpooling1d, dense, and dropout layers to enhance generalization capabilities and reduce the risk of overfitting. The model was trained using the Adam optimizer and its performance was evaluated using 10-fold cross-validation. Experimental results showed that the model achieved an average accuracy, precision, recall, and F1 score of 97.74%, 97.75%, 97.74%, and 97.73%, respectively. The developed model outperformed previous methods, namely the convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM), gated recurrent unit (GRU), and conventional methods such as naïve Bayes or support vector machine (SVM), in terms of accuracy and training efficiency. This study demonstrates that the model has a reliable capability in identifying hoax news, both in terms of detection accuracy and performance consistency.
Klasifikasi Penyakit Padi Menggunakan Convolutional Neural Network (CNN) Berbasis Citra Daun Moh. Heri Susanto; Irwan Budi Santoso; Suhartono; Ahmad Fahmi Karami
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.v14i3.18791

Abstract

Rice diseases significantly impact agricultural productivity, making classification models essential for accurately distinguishing rice leaf diseases. Various classification models have been proposed for image-based rice disease classification; however, further performance improvement is still required. This study proposes the use of a convolutional neural network (CNN) to classify rice diseases based on leaf images. The dataset used in this study included leaf images categorized into four conditions: leaf blight, blast, tungro, and healthy. In the initial stage, data preprocessing was conducted, including resizing, augmentation, and normalization. Following preprocessing, a custom CNN architecture was developed, consisting of four convolutional layers, four pooling layers, and three fully connected layers. Each convolutional layer employed a 3 × 3 kernel with a stride of 1 and ReLU activation, while the pooling layers used max pooling with a 3 × 3 kernel and a stride of 2. Using a batch size of 32 and the Adam optimizer, the best test performance was achieved with 100 training epochs and a learning rate of 0.0002, resulting in a training accuracy of 0.9930, a loss of 0.0221, and a test accuracy of 0.9647. Model evaluation demonstrated a balanced performance across precision, recall, and F1 score, each achieving 0.9647, indicating highly effective classification without bias toward any specific class. These findings suggest that the simplified CNN model can deliver competitive classification performance without the need for complex architectures or additional enhancement techniques. The proposed CNN model outperformed existing CNN architectures, such as Inception-ResNet-V2, VGG-16, VGG-19, and Xception.
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.
Deteksi Serangan pada Jaringan IoT Menggunakan Seleksi Fitur Gabungan dan Optimasi Bayesian Samsudiat; Kalamullah Ramli
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.v14i3.19764

Abstract

Machine learning (ML)-based attack detection is a promising alternative for addressing cybersecurity threats in Internet of things (IoT) networks. This approach can handle various emerging attack types. However, the growing volume of data and the reliance on default parameter values in ML algorithms have led to performance degradation. This study proposed a hybrid feature selection method combined with Bayesian optimization to improve the effectiveness and efficiency of attack detection models. The hybrid feature selection method integrated correlation-based filtering, which aimed to rapidly remove highly correlated features, and feature importance, which aimed to select the most influential features for the model. In addition, Bayesian optimization was employed to efficiently identify the optimal parameter values for lightweight and robust ML algorithms suitable for IoT networks, namely decision tree and random forest. The constructed model was then evaluated using the latest attack dataset, CICIoT2023, which consists of seven types of attacks: DDoS, DoS, Mirai, spoofing, reconnaissance, web-based attacks, and brute force. The evaluation results showed that the hybrid feature selection technique produced a more efficient model compared to several single feature selection methods by selecting 5 out of 46 features. Furthermore, Bayesian optimization successfully identified the optimal parameter values, improving model performance in terms of accuracy, precision, recall, and F1 score up to 99.74%, while reducing computational time by as much as 97.41%. Based on these findings, the proposed attack detection model using hybrid feature selection and Bayesian optimization can serve as a reference for implementing cybersecurity solutions in IoT networks.

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