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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
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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 : 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.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.
Kinerja Optical Flow dalam Estimasi Kecepatan Terbang SUAV Menggunakan Metode Farneback Aziz Fathurrahman; Ony Arifianto; Yazdi Ibrahim Jenie; Hari Muhammad
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 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.v14i1.15001

Abstract

This paper evaluates the performance of the Farneback optical flow method for estimating the flight speed of a small unmanned aerial vehicle (SUAV) in a simulated 3D World MATLAB-Unreal Engine environment. Optical flow offers a promising solution for velocity estimation, which is crucial for autonomous navigation. A downward-facing monocular camera model was simulated on an SUAV during steady state, straight flight at 100 m altitude and 25 m/s airspeed. Three simulated flight scenes—forest, city block, and water—representing poor, moderate, and rich textures were used to assess the method’s performance. Results demonstrated that using the median estimate of the optical flow field yielded accurate velocity estimations in moderate to rich texture scenes. Over the city block and forest scenes, mean velocity estimation accuracy was 0.6 m/s (σ = 0.2 m/s) and 0.3 m/s (σ = 0.4 m/s), respectively. The impact of camera tilt angle and altitude variations on estimation accuracy was also investigated. Both factors introduced bias, with accuracy decreasing to 1.7 m/s (σ = 0.2 m/s) and 1.9 m/s (σ = 0.2 m/s) for +10° and -10° camera tilt, respectively. Similarly, altitude differences of +10m and -10m resulted in reduced accuracy of 1.9 m/s (σ = 0.2 m/s) and 4.3 m/s (σ = 0.1 m/s), respectively. This study demonstrates the potential of the Farneback method for determining flight speed under steady, straight flight conditions with acceptable accuracy.
Perbandingan Kinerja Algoritma KNN dan SVM Menggunakan SMOTE untuk Klasifikasi Penyakit Diabetes Asri Mulyani; Sarah Khoerunisa; Dede Kurniadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 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.v14i1.15198

Abstract

Diabetes frequently goes undetected or is diagnosed too late. Consequently, it may lead to a range of serious complications, such as organ damage, stroke, and heart disease. The International Diabetes Federation (IDF) reports that 10.5% of the adult population aged 20 to 79 are diagnosed with diabetes, and almost half are unaware of the condition. Hence, the number of people with diabetes has increased by fourfold compared to the prior period. One essential step for preventing complications in patients with diabetes is early detection, one of which is by utilizing artificial intelligence (AI) technology, namely data mining. Therefore, knowledge about effective algorithms used to detect diabetes is needed. This study aimed to compare two algorithms, namely k-nearest neighbor (KNN) and support vector machine (SVM), for diabetes classification using the synthetic minority oversampling technique (SMOTE). In this study, both algorithm performance was measured using the machine learning life cycle method. The results showed they had good performance in detecting diabetes; yet, there were significant performance differences between the two. The SVM algorithm with radial basis function (RBF) kernel achieved 81.67% accuracy, 85.91% precision, 79.01% recall, and 82.32% F1 score. Meanwhile, the KNN algorithm with k = 3 found through cross-validation achieved 83.33% accuracy, 85.00% precision, 83.95% recall, and 84.47% F1 score. Based on confusion matrix evaluation, KNN showed superior performance compared to SVM in terms of accuracy and other evaluation metrics. These results indicate that KNN is more effective in detecting diabetes in the dataset used in this study.
Perbandingan Model U-Net dan ELU-Net untuk Segmentasi Semantik Citra Medis Kanker Pankreas Algi Fari Ramdhani; Yudi Widhiyasana; Setiadi Rachmat
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 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.v14i1.15262

Abstract

Medical image analysis for semantic segmentation using deep learning technology has been extensively developed. One of the notable architectures is U-NET, which has demonstrated high accuracy in segmentation tasks. Further advancements have led to the development of ELU-NET, which aims to enhance model efficiency. ELU-NET achieves relatively good accuracy; however, further comparative analysis of both models is necessary. The comparison between these models is based on accuracy, storage usage, and processing time in performing semantic segmentation of pancreatic cancer images. The pancreatic cancer images utilized in this study are sourced from the PAIP 2023 Challenge, consisting of hematoxylin and eosin (H&E)-stained images. Experiments were conducted by varying the number of filters and model depth for both architectures. The evaluation was performed using a dataset of 57 pancreatic cancer images. The experimental results indicated that U-NET achieved the highest accuracy at 92.8%, slightly outperforming ELU-NET, which attained 89.7%. However, ELU-NET is significantly more efficient in terms of storage usage (8.1 MB for ELU-NET compared to 93.31 MB for U-NET) and processing time (4.0 s for ELU-NET and 5.3 s for U-NET). Although ELU-NET exhibited slightly lower accuracy than U-NET, it surpassed U-NET considerably in terms of storage efficiency (by 85.21 MB) and processing speed (by 1.3 s). These findings suggest that ELU-NET is not superior to U-NET in accuracy. However, given the storage size ratio of 1:11.51 and the processing time ratio of 1:1.325 between ELU-NET and U-NET, the 3.1% accuracy difference represents a reasonable trade-off.
Model Klasifikasi Multilabel pada Publikasi Penelitian SDG dengan Pendekatan Multilevel dan Hierarki Berliana Sugiarti Putri; Lya Hulliyyatus Suadaa; Efri Diah Utami
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 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.v14i1.16265

Abstract

The growing number of research publications complicates the identification of the implementation of research publications, especially related to sustainable development goals (SDGs). The research publication categorization into SDG levels has not been conducted. The Center for Research and Community Service (Pusat Penelitian dan Pengabdian Masyarakat, PPPM) Politeknik Statistika (Polstat) STIS needs this to monitor lecturers in implementing SDGs. This study aimed to implement and evaluate problem transformation methods and machine learning classification algorithms with a multilevel and hierarchical approach to categorize research publications into SDG levels. Problem transformation methods used were binary relevance, label powerset (LP), and classifier chains. Machine learning classification algorithms used were logistic regression (LR) and support vector machine (SVM). The inputs included titles, abstracts, and titles and abstracts. The best filter model that classified data into SDGs-non-SDGs was the model with titles and SVM, with an accuracy of 0.8634. The best level model for classifying data to SDG level was the model using titles, LP, and SVM with multilevel approaches. The level model classified data into four pillars, goals, targets, and indicators of SDGs, with an accuracy of 0.8067, 0.7501, 0.6792, and 0.6194, respectively. In comparison to other inputs with more comprehensive information, the results showed that title inputs yielded the best accuracy due to the simultaneous use of English and Indonesian. Future research can modify the model to utilize a single language input to optimize the term frequency-inverse document frequency (TF-IDF) process, hence, the word meanings from each language are not considered different important words.
Pendekatan PLS-SEM pada Persepsi Percaya dan Penggunaan Informasi untuk Situs Web Informasional Umi Proboyekti; Ridi Ferdiana; P. Insap Santosa
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 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.v14i1.16326

Abstract

Trust is described in various contexts, such as e-commerce, e-government, reviews, and online health information. Credibility and information quality are fundamental to building trust in those contexts. This study aimed to develop trust perception (TP) and information use (IU) indicators in an information evaluation context. Indicators were developed through three processes: searching, grouping, and construction. Relevant indicators were grouped based on similarities to construct statements, which were validated for face and content validity by three experts. The validated TP and IU were then tested using the partial least squares structural equation modeling (PLS)-SEM. The data used for measurement obtained from 110 participants comprising 55 Indonesian academic librarians and 55 university students. Participants responded to indicator statements after evaluating information from four prepared informational websites. This study yielded five TP indicators and a single IU indicator, where TP significantly predicted IU. The five indicators described TP as make-sense information relevant to needs, provided by trusted authors and providers, and accompanied by accessible author information, provider information, and reference sources. IU was described as the information used for its credibility. The measurement demonstrated distinct participant behaviors. Differences in needs influenced assessments, while author and provider trustworthiness showed no bias toward participant type. Trust perception significantly predicted IU, with moderate model fit and varying predictive strengths across the websites. Tested as reliable, valid, and a significant predictor of IU, TP serves as a tool for examining factors that potentially influence trust in online information.
Perbandingan Penggunaan Optimizer dalam Klasifikasi Sel Darah Putih Menggunakan Convolutional Neural Network Dede Kurniadi; Rifky Muhammad Shidiq; Asri Mulyani
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 1: Februari 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.v14i1.17162

Abstract

White blood cells are crucial components of the immune system responsible for combating infections and diseases. The classification and counting of white blood cells are typically performed manually by experienced operators or via automated cell analysis systems. The manual method is inefficient, time-consuming, and labor-intensive, while automated analysis machines are often expensive and require stringent sample preparation. This study aimed to compare the performance of three optimizers—root mean square propagation (RMSProp), stochastic gradient descent (SGD), and adaptive moment estimation (Adam)—in a white blood cell classification model using a convolutional neural network (CNN) algorithm. The dataset consisted of 12,392 images spanning four white blood cell classes: eosinophils, neutrophils, lymphocytes, and monocytes. The results indicate that the Adam optimizer achieved the best performance, with a training accuracy of 98.65% and an evaluation accuracy of 97.73%. Adam also outperformed the other optimizers in key metrics, including recall (97.43%), precision (97.42%), F1-score (97.42%), and specificity (99.11%). The AUC values for all classes exceeded 90%, demonstrating the model’s exceptional ability to distinguish between different cell types. The RMSProp optimizer yielded a training accuracy of 98.63%, whereas SGD achieved a lower training accuracy of 83.46%. This study highlights the significant impact of optimizer selection on CNN performance in white blood cell image classification, providing a foundational step toward the development of more accurate medical classification systems.
Deteksi Duplikasi Data pada Sistem Pemantauan Kualitas Udara Berbasis IoT Dwi Ilham Maulana; Asep Andang; Ifkar Usrah; Agus Purnomo
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.v14i2.16272

Abstract

The increasing volume of data on the Internet of things (IoT)-based systems has driven the need for efficiency in data management, particularly in air quality monitoring systems. One approach to address this challenge is data duplication detection, which works to eliminate redundant data to reduce storage requirements and power consumption. This study aims to develop an IoT-based air quality monitoring system incorporating a data duplication detection method as part of an effort to support the green IoT concept. The methodology involved a comparative analysis between systems with and without the implementation of data duplication detection, accompanied by a comprehensive evaluation of system performance. The data tested included the size of transmitted data and device power consumption during the transmission process. Testing was conducted under real operational conditions over a 24-hour period. The results indicate that the implementation of data duplication detection successfully reduced the size of transmitted data from 56 bytes to 11–44 bytes, depending on the level of data redundancy. Power consumption was reduced by 1.59% to 3.84% compared to the system without data duplication detection. This method was also proven not to affect the accuracy of the displayed data, thereby maintaining the system’s functional requirements. In conclusion, the implementation of the data duplication detection method in an IoT-based air quality monitoring system not only optimizes data transmission processes but also supports energy efficiency in line with the principles of green IoT. This research provides a significant contribution to the development of more sustainable and energy-efficient IoT systems.
Perencanaan Jaringan 5G Menggunakan Teknologi Macrocell dan Picocell Rivan Achmad Nugroho; Redy Ratiandi Yacoub; Herry Sujaini; Dedy Suryadi; 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.v14i2.16830

Abstract

The city of Pontianak is projected to experience substantial growth in network demand, driven by the expansion of commercial hubs, educational institutions, tourism destinations, and essential public services. Under the current status quo, Pontianak lacks 5G network coverage, underscoring the necessity of implementing a comprehensive 5G network plan to support its urban development. This research conducted a detailed analysis of 5G network coverage and capacity planning, utilizing macrocell and picocell technologies to address the connectivity demands of an urban environment. Operating within the 3.5 GHz frequency band with a 100 MHz bandwidth, this research examined network requirements in the medium band spectrum. The results revealed that macrocell technology required 18 uplink and 23 downlink sites to cover an area of 107.8 km², while picocell technology demanded a denser infrastructure, comprising 351 uplink and 364 downlink sites across 90.72 km². Based on a five-year capacity projection for a population of 673,400, the macrocell technology will require 10 uplink and 22 downlink sites. On the contrary, picocell technology, which is more suitable for densely populated areas, will require 261 uplink and 263 downlink sites to serve a population of 423,881. Simulation results indicated that synchronization signal reference signal received power (SS-RSRP) and secondary synchronization signal received power (SS-SINR) values met or surpassed the established key performance indicators (KPI) for both technologies. This 5G network plan aligns with Pontianak’s smart city vision by enhancing connectivity, optimizing coverage, and delivering seamless user experiences, highlighting the adaptability of macrocell and picocell solutions in varied urban settings.
Evaluasi Pengukuran Semantik Sinonim KBBI Menggunakan Pendekatan Word Embedding Muhammad Rafli Aditya H.; Muhammad Ilham; Dewi Fatmarani Surianto; Abdul Muis Mappalotteng
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.v14i2.17117

Abstract

Kamus Besar Bahasa Indonesia (KBBI) is a primary resource for data in research on determining word-meaning similarity in Indonesian. This study investigates the effectiveness of word embedding methods and the term frequency–inverse document frequency (TF-IDF) weighting technique in assessing the semantic similarity of synonym pairs. The objective is to measure the similarity of synonym word pairs listed in KBBI by applying cosine similarity, leveraging TF-IDF weighting, various word embedding models, and latent semantic analysis (LSA). The methodology involved data collection, followed by a text preprocessing stage consisting of case folding, stopword removal, stemming, and tokenization. The processed data were transformed into vector representations using word embedding models, including Word2Vec, fastText, GloVe, and sentence-bidirectional encoder representations from transformers (S-BERT), and TF-IDF. LSA was employed for dimensionality reduction of the vectors before similarity testing using cosine similarity, with final evaluation of the results. The findings revealed that fastText significantly improved the similarity scores between synonym pairs, achieving an average similarity score of 0.901 for 30 synonym pairs. Evaluation results indicated an accuracy of 0.88, a recall of 1.00, a precision of 0.81, and an F1 score of 0.90. These results suggest that fastText is more effective in enhancing the accuracy of synonym meaning similarity measurements. Future research is encouraged to expand the corpus and further explore the use of word embedding for semantic similarity tasks. This study contributes to the natural language processing advancement and provides a potential foundation for more accurate language-based applications that assess word meaning similarity in KBBI.

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