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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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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 13 Documents
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Search results for , issue "Vol 9 No 2: Mei 2020" : 13 Documents clear
Asesmen ECG-Apnea Satu Sadapan untuk Peningkatan Akurasi Klasifikasi Gangguan Tidur Berdasarkan AdaBoost Iman Fahruzi; I Ketut Eddy Purnama; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1548.913 KB) | DOI: 10.22146/jnteti.v9i2.159

Abstract

Sleep disorder is a disturbed breathing flow (collapse) during sleep. The symptoms are generally undiagnosed and untreated properly so that repeated respiratory interruptions have the potential for severe sleep disorders. Electrocardiogram (ECG) recordings are practical tools used to examine the existence of sleep disorders in the heart rhythm. The ECG represents heart electrical activity in the form of P, QRS, and T waves. The number of ECG sensors is uncomfortable for the patient to record the data, increasing the recording complexity, slowing the computation, causing misinterpretation and loss of clinical information. Therefore, an early warning system is needed as a medical aid that can be diagnosed using single-lead ECG. In conducting this study, the system consists of five stages, which include the acquisition of ECG records, pre-processing, extraction of features, selection of features, and the classification process. ECG-record feature sets consist of time-domain, frequency-domain, and non-linear analysis. The AdaBoost method confirms that the model had the highest performance than the SVM, k-NN and NN. The results of the experiments thus measure the outperformed of method performance and achieved 90.1% classification accuracy for the AdaBoost classification method. Moreover, the F1 score, precision, recall, sensitivity, and specificity was reported as 90.1%, 90.3%, 90.1%, 86.9%, and 93.3%, respectively.
Kombinasi Fitur Multispektrum Hilbert dan Cochleagram untuk Identifikasi Emosi Wicara Agustinus Bimo Gumelar; Eko Mulyanto Yuniarno; Wiwik Anggraeni; Indar Sugiarto; Andreas Agung Kristanto; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1364.227 KB) | DOI: 10.22146/jnteti.v9i2.166

Abstract

In social behavior of human interaction, human voice becomes one of the means of channeling mental states' emotional expression. Human voice is a vocal-processesed speech, arranged with word sequences, producing the speech pattern which able to channel the speakers' psychological condition. This pattern provides special characteristics that can be developed along with biometric identification process. Spectrum image visualization techniques are employed to sufficiently represent speech signal. This study aims to identify the emotion types in the human voice using a feature combination multi-spectrum Hilbert and cochleagram. The Hilbert spectrum represents the Hilbert-Huang Transformation(HHT)results for processing a non-linear, non-stationary instantaneous speech emotional signals with intrinsic mode functions. Through imitating the functions of the outer and middle ear elements, emotional speech impulses are broken down into frequencies that typically vary from the effects of their expression in the form of the cochlea continuum. The two inputs in the form of speech spectrum are processed using Convolutional Neural Networks(CNN) which best known for recognizing image data because it represents the mechanism of human retina and also Long Short-Term Memory(LSTM)method. Based on the results of this experiments using three public datasets of speech emotions, which each of them has similar eight emotional classes, this experiment obtained an accuracy of 90.97% with CNN and 80.62% with LSTM.
Integrasi Sistem Pengawasan Kesehatan Jembatan dengan Sistem Pengawasan Lalu Lintas Muhammad Satria Wibawa; Achmad Irjik Ubay; Seno Adi Putra; Alvi Syahrina
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1804.641 KB) | DOI: 10.22146/jnteti.v9i2.197

Abstract

Bridge health monitoring system is a real-time data collection system that consists of components, responses, changes, and the construction process of bridge structures. This system aims to help assessing the structural security risks that could damage to the bridge structure by utilizing Wireless Sensor Network (WSN) as data retrieval. This system has a focus on the structure parameters of the bridge,but does not pay attention to vehicle factors,such as vehicle weight, vehicle patterns,and types of vehicles that pass on the bridge,whichcan also be factors of the bridge's health assessment. Anothersystem,namelya traffic control system,measuresvehicle weight using Weight in Motion (WIM).It can be used to obtainvehicle factors,especially vehicle weight,that also helps in assessing bridge health. This paper proposes the integrationof these two systems in order to create a system that can measure health of bridge structures,as well asmonitor the weight of the vehicle.Through this integration, it is possible to manage WSN sleep and wake time which helps saving energy.Experimental result shows that the system can measure natural frequencies close to the value of Finite Element Analysis (FEA),showing the validity of the developed system.

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