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Risanuri Hidayat
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Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
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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
 57   58   59   60   61 
Entity dan Relation Linking untuk Knowledge Graph Question Answering Menggunakan Pencarian Berjenjang Adila Alfa Krisnadhi; Mohammad Yani; Indra Budi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
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.v13i2.9184

Abstract

Knowledge graph question answering (KGQA) systems have an important role in retrieving data from a knowledge graph (KG). With the system, regular users can access data from a KG without the need to construct a formal SPARQL query. KGQA systems receive a natural language question (NLQ) and translate it into a SPARQL query through three main tasks, namely, entity and relation detection, entity and relation linking, and query construction. However, the translation is not trivial due to lexical gaps and entity ambiguity that may occur during entity or relation linking. This research proposed an approach based on multiclass classification of NLQ whose entity occurrences are detected into categories based on KG relations to address the lexical gap challenge. Next, to solve the entity ambiguity challenge, this research proposed a three-stage searching procedure to determine appropriate KG entities associated with the NLQ entities, given the correspondence between the NLQ and a particular KG relation. This three-stage searching consisted of text-based searching, vector-based searching, and entity and relation pairing. The proposed approach was evaluated on the SimpleQuestions and LC-QuAD 2.0 datasets. The experiments demonstrated that the proposed approach outperformed the state-of-the-art baseline. For the relation linking task, the proposed approach reached 89.87% and 74.83% recall for the SimpleQuestions and LC-QuAD 2.0, respectively. This approach also achieved 91.74% and 61.96% recall on the entity linking tasks for the SimpleQuestions and LC-QuAD 2.0, respectively.
GSA dengan Skrining Faktor untuk Evaluasi Kinerja Relai Proteksi Saluran Nanang Rohadi; Bambang Mukti Wibawa; Nendi Suhendi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 3: Agustus 2024
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.v13i3.9422

Abstract

This paper presents a global sensitivity analysis with factor screening to efficiently test conventional distance relay algorithm models used as transmission line protection devices with series compensators. Various system indeterminacy parameters (factors) may affect the functional performance of the fault impedance measurement algorithm model of intelligent electronic devices, specifically the SEL-421 type distance relays. The purpose of global sensitivity testing is to determine the influence strength of individual and interacting factors on the output of the fault impedance measurement algorithm. Global sensitivity analysis, conducted through variance analysis using quasi-Monte Carlo methods, aims to compute the error in fault impedance measurement results. As an initial step, the Morris method was employed to filter out factors that did not predominantly affect relay performance, thereby reducing the computational burden of the global sensitivity analysis. Several simulated transmission line faults with series compensators and multiple factors were modeled using DIgSILENT PowerFactory. Automatic fault simulations, both before and after compensators, were developed using DIgSILENT Programming Language. The sensitivity of the relay algorithm output was tested for each simulation based on read-out voltage, fault current signals, and the values of sampled factors using both Morris and Sobol methods. The variance of the algorithm output model influenced by several factors was calculated using SIMLAB software. Fault resistance emerged as the dominant factor affecting algorithm performance, with sensitivity indices exceeding 0.9 and 0.7 for faults before and after the compensator, respectively. This technique has effectively tested the SEL-421 distance relay algorithm.
Pengaruh Pembobotan Desain Codebook terhadap Kinerja Sistem Sparse Code Multiple Access Shilvy Fatma Fitria Rachmawati Fatma; Linda Meylani; Vinsensius Sigit Widhi Prabowo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
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.v13i2.9430

Abstract

Sparse code multiple access (SCMA) can support the system when overloading on the receiving side, thereby improving the system’s spectral efficiency by designing mapping symbols appropriately. The performance of SCMA is assessed through the utilization of a sparse codebook, wherein bits are directly mapped to multidimensional codewords influenced by both the energy diversity and the minimum Euclidean distance of the multidimensional constellation (MC). The codebook design simulation was conducted using both Latin and non-Latin generators with phases of 60° and 45°, incorporating weighting values of w1 = 0.6; w2 = 0.3; w3 = 0.1. The simulation also included line constellation with additive white Gaussian noise (AWGN) channel, Rayleigh fading, and Rician channel. This study presented the optimal results across three channels: Latin 60° with BER 10−3 in the AWGN channel, non-Latin 60° with BER 10−3 in the Rayleigh fading, and non-Latin 45° with BER 10−3 in the Rician channel. Then, the results on the codebook design weighting were as follows: Latin 60° with BER 10−1 in the AWGN channel, non-Latin 45° with BER 10−1 in the Rayleigh fading, and Latin 45° with BER 10−3 in the Rician channel. The simulation results state the effect of weighting on each channel. It was found that Latin generators could improve BER performance by suppressing overlap at constellation points and eliminating errors occurred in SCMA codebooks. However, this improvement was observed only in AWGN channels and not for non-Latin generators.
Pemodelan Isu Perubahan Iklim di Indonesia Berdasarkan Headline Media Anang Kunaefi; Aris Fanani
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
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.v13i2.9464

Abstract

Climate change has become a global issue affecting all countries in the last decades. This phenomenon poses a concern to Indonesia as it is one of the climate change’s epicenters. Various studies have shown that climate change can harm multiple community activities, such as unstable agricultural production, decreased people’s health, and global warming. This study tried to model and analyze climate change topics discussed in the media. Finding hidden topics from texts can provide clues and information regarding public conversation surrounding climate change, such as public thoughts, perceptions, and readiness to mitigate the possible adverse effects of climate change. In order to identify hidden subjects from the corpus, this work modeled climate change issues in Indonesia using the latent Dirichlet allocation (LDA) algorithm to analyze texts from Indonesian media headlines. As many as 7,000 headline data from five online media were collected from 2017 to 2021 using web scraping techniques. The proposed approach produced eight topics related to climate change, which were determined by the highest coherence value of 0.560. Those topics were renewable energy, carbon emissions, environmental management, development economics, international cooperation, policy/regulation, rehabilitation, and disaster. Based on the results, the model could sufficiently describe the theme of discussion in society and photograph public thoughts and the government’s readiness in the form of policies and regulations in dealing with the climate change phenomenon.
Analisis Laju Pembelajaran untuk Pengenalan Nyeri Melalui Metode Viola-Jones dan Pembelajaran Mendalam Raihan Islamadina; Khairun Saddami; Fitri Arnia; Taufik Fuadi Abidin; Rusdha Muharar; Muhammad Irwandi; Aulia Syarif Aziz
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
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.v13i2.9466

Abstract

Deep learning is growing and widely used in various fields of life. One of which is the recognition of pain through facial expressions for patients with communication difficulties. Viola-Jones is a simple algorithm that has real-time detection capabilities with relatively high accuracy and low computational power requirements. The learning rate is a significant number that has an impact on the deep learning result. This study recognized pain using the Viola-Jones and deep learning methods. The dataset used was a thermal image from the Multimodal Intensity Pain (MIntPAIN) database. The steps taken consisted of segmentation, training, and testing. Segmentation was conducted using the Viola-Jones method to get the significant area of the face image. The training process was carried out using four deep learning benchmarks model, which were DenseNet201, MobileNetV2, ResNet101, and EfficientNetb0. Besides that, deep learning has a very important number to determine that is learning rate, which impact the deep learning results. There were five learning rates, which were 10-1, 10-2, 10-3, 10-4, and 10-5. Learning rate values were then compared with four deep models learning to obtain high accuracy results in a short time and simple algorithm. Finally, the testing process was carried out on test data using a deep learning benchmark model in accordance with the training process. The research results showed that a learning rate of 10-2 from the MobileNetV2 method produced an optimal performance with a training validation accuracy of 99.60% within a time of 312 min and 28 s.
Optimasi Algoritma K-Nearest Neighbors Berdasarkan Perbandingan Analisis Outlier (Berbasis Jarak, Kepadatan, LOF) Fitri Ayuning Tyas; Mahda Nurayuni; Hidayatur Rakhmawati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
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.v13i2.9579

Abstract

The current data growth affects data analysis in various fields, such as astronomy, business, medicine, education, and finance. The collected and stored data contain extreme values or observation values different from most other observation value results. These extreme values are called outliers. Outliers on some data often hold valuable information, necessitating thorough examination to determine whether to retain or discard them prior to data mining application. Outlier detection can be performed as a part of data preprocessing using outlier analysis techniques. Commonly utilized outlier analysis techniques encompass distance-based methods, density-based methods, and the local outlier factor (LOF) method. k-nearest neighbors (KNN) are a data mining algorithm susceptible to outliers due to its reliance on the value of k. Hence, having an appropriate handling mechanism is essential when employing KNN on datasets that contain outliers. The experimental method was selected to apply the proposed approach, aiming to optimize the KNN algorithm through a comparison of outlier analysis methods (KNN-distance, KNN-density, and KNN-LOF). The results revealed that KNN-density outperformed the others significantly: achieving an average accuracy of 99.34% at k=3 and k=5 for Wisconsin Breast Cancer, 85.25% at k=7 for Glass, and 85.45% at k=5 for Lymphography. Moreover, both the Friedman and Nemenyi tests validate a notable distinction between KNN-density and KNN-LOF.
Teknik Konfusi dan Difusi untuk Proses Enkripsi Citra Berbasis Sistem Chaos Magfirawaty Magfirawaty; Ariska Allamanda; Malika Ayunasari; Muhammad Nadhif Zulfikar
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
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.v13i2.9623

Abstract

Face recognition uses biometric technologies to identify humans based on their facial characteristics. This method is commonly used to restrict information access control. The benefits of face recognition systems encompass their ease of use and security. The human face recognition process consists of face detection, face tracking, and face recognition. The process uses some algorithms: the Viola-Jones for face detection, the Kanade-Lucas-Tomasi (KLT) for face tracking, and the principal component analysis (PCA) for face recognition. Furthermore, this research proposed face recognition with an encryption process to protect the data stored in a database. The encryption process consists of two main processes: confusion and diffusion. The confusion process is randomizing the position of the original image pixels. This research utilized the Arnold’s cat map (ACM) for the confusion process, and the diffusion process was performed using the XOR operation with the key generated from the 1D chaos system. Three different 1D chaos systems, namely logistic map, Bernoulli map, and tent map, were compared to see which chaos system had the best encryption results. Tests were conducted by comparing various parameters on the three proposed 1D chaos systems, including correlation coefficient, histogram analysis, entropy value, number of pixel rate changes (NPCR), and unified average change intensity (UACI). Based on testing the image encryption results, the diffusion process utilizing the tent map produced the best image encryption compared to other chaotic systems.
Pengembangan Web Cerdas untuk Social e-Learning di Pedesaan Seno Adi Putra; Timmie Siswandi; Dessy Yussela; Rinez Asprinola; Erin Karina; Mega Candra Dewi; Santi Al-arif
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 3: Agustus 2024
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.v13i3.9872

Abstract

Social media technology affects the learning paradigm change towards social media-based learning, known as social e-learning. Social e-learning regards a person as a center of learning, dubbed people-centered learning. Here, people are encouraged to interact or communicate with others and produce their learning content. This work attempted to provide a solution model for rural e-learning social learning empowered with intelligent web technology. The proposed social e-learning includes several modules for development, such as personal space, collaboration space, and communication space modules. It also leverages intelligent web technologies currently implemented in today’s social media applications, such as article search, article recommendations, friendship recommendations, and document classification. In the searching module, the PageRank method was used to calculate the relevance score to determine the rating of the documents or articles. The similarity-based element calculation method was utilized to create articles’ suggestions and recommendations. The naïve Bayes algorithm, decision tree, and neural network were compared to find the best solution for article classification in agriculture, fisheries, animal husbandry, and plantations. When comparing these three algorithms, the result showed that the neural network was the most accurate classification, reaching 95.2% accuracy. A clustering algorithm, namely robust clustering using links (ROCK), was utilized for rural friendship recommendation. Thus, these algorithms (the PageRank, the similarity-based element, neural network, and the ROCK) were suitable and recommended for supporting intelligent web paradigms in social e-learning applications.
Analisis Faktor Berbagi Informasi Pribadi di Media Sosial: Studi Skala Kecil Belia Rida Syifa Fauzia; Lukman Yudokusumo; Yova Ruldeviyani
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 3: Agustus 2024
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.v13i3.9908

Abstract

In the contemporary social landscape, the widespread use of social media, such as platforms like TikTok, Instagram, and YouTube, has become a prominent trend in various circles of society, especially in Indonesia. As the number of users on these platforms increases, concerns regarding user security and privacy also increase. Data breaches in 2021 affecting 235 million users on Instagram, TikTok, and YouTube underscored the importance of researching the multifaceted dynamics around privacy concerns, levels of trust, risk awareness, and user behavior patterns related to sharing personal information on social media platforms. This research aimed to address this critical issue by introducing a research model developed based on relevant hypotheses from previous research. The sample used in this research consisted of social media users in Indonesia. Methodologically, this research used sophisticated structural equation modeling (SEM) tools for hypothesis testing and confirmatory factor analysis (CFA) to validate the efficacy of existing research models. These findings indicated that users’ trust, awareness, privacy concerns, and behavioral intentions significantly and positively influence the tendency to share personal data on social media platforms. This research provides valuable insights into the complex interactions between factors influencing user behavior in social media privacy, thereby offering implications for academia and practical applications.
Propulsi Kapal Listrik dengan Motor BLDC IPM: Analisis Kinerja dan Efisiensi Dewi Rianti Mandasari; Budi Sudiarto; Lia Amelia; Cuk Supriyadi Ali Nandar
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 2: Mei 2024
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.v13i2.10131

Abstract

Air pollution, particularly the presence of PM2.5 particles, remains a global health concern. While Indonesia exhibits lower PM2.5 levels than the global average, vehicular emissions significantly contribute to air pollution. In light of environmental and health considerations, adopting eco-friendly electric motors, mainly interior permanent magnet brushless direct current (IPM BLDC) motors, represents a promising solution for cleaner and more efficient boat propulsion systems, benefiting both the environment and the livelihoods of fishermen. This study thoroughly examines the efficiency and performance of IPM BLDC motors in boat propulsion, utilizing finite element analysis (FEA) through ANSYS Maxwell. The FEA simulations in ANSYS Maxwell were tailored to focus on crucial design variables such as motor torque, speed, and thermal management. It aimed to ensure that the motor specifications meet electric boats’ operational needs in fishing and search operations. Notably, at the desired speed of 5,000 rpm, the motor achieved a torque of 15 Nm with a cogging torque of just 7% and maintained an average efficiency of 89%. Significantly, it operated at a safe temperature without requiring additional cooling systems. Furthermore, simulation outcomes suggested that the motor could effectively function at higher speeds, specifically 6,300 rpm, presenting an exciting opportunity to enhance boat propulsion systems through increased motor speed.

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