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All Journal Kitektro InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Green Intelligent Systems and Applications Jurnal INFOTEL Jurnal Polimesin
Yunidar Yunidar
Department Of Elect Rical Engineering And Computer, Engineering Faculty, University Of Syiah Kuala, Banda Aceh, Indonesia
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : Green Intelligent Systems and Applications

 1 
Finite Impulse Response Filter for Electroencephalogram Waves Detection Melinda Melinda; Syahrial; Yunidar; Al Bahri; Muhammad Irhamsyah
Green Intelligent Systems and Applications Vol. 2 Iss. 1 (2022)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.572 KB) | DOI: 10.53623/gisa.v2i1.65

Abstract

Electroencephalographic data signals consist of electrical signal activity with several characteristics, such as non-periodic patterns and small voltage amplitudes that can mix with noise making it difficult to recognize. This study uses several types of EEG wave signals, namely Delta, Alpha, Beta, and Gamma. The method we use in this study is the application of an impulse response filter to replace the noise obtained before and after the FIR filter is applied. In addition, we also analyzed the quality of several types of electroencephalographic signal waves by looking at the addition of the signal to noise ratio value. In the end, the results we get after applying the filter, the noise that occurs in some types of waves shows better results.
Application of Convolutional Neural Network (CNN) Method in Fluctuations Pattern Melinda Melinda; Yunidar Yunidar; Nur Afny Catur Andryani
Green Intelligent Systems and Applications Volume 3 - Issue 2 - 2023
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i2.270

Abstract

In the acquisition of amplitude data, the inaccuracy of a signal with the occurrence of an unstable peak value of the amplitude in the data is called a fluctuation. This study uses High-High Fluctuation (HHF) signal data from the acquisition of Multi-Spectral Capacitive Sensors (MSCS) with Hydrogen Dioxide (H2O) and Hydrogen Dioxide (H2O) objects mixed with Sodium Hydroxide (NaOH) that have been organized into a matrix form. The data acquisition results in previous studies have several parts that are difficult to distinguish with the naked eye. The method used in this study applies the CNN method for image recognition of signal fluctuations of type HHF from H2O and H2O mixed with NaOH, using the architecture known as AlexNet. Then, the H2O and H2O data groups mixed with NaOH were grouped into training data groups of 280 image data for each data type, and 70 image data for test data for both groups. During the training stage, the number of epochs used is 20. However, by the time the number of epochs reaches 15, the accuracy rate is already high, reaching 98%. Furthermore, at the testing stage, the CNN program can correctly recognize the entire 70 image data for both materials, achieving perfect recognition for the total amount of the two materials.
IoT-based Heart Signal Processing System for Driver Drowsiness Detection Yunidar Yunidar; Melinda Melinda; Khairani Khairani; Muhammad Irhamsyah; Nurlida Basir
Green Intelligent Systems and Applications Volume 3 - Issue 2 - 2023
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i2.323

Abstract

Traffic accidents often result in loss of life and significant economic losses. Indonesia's high number of traffic accidents indicates the need for effective solutions to overcome this problem. Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The drowsiness detection tool was developed using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor. Testing was carried out on 25 subjects under two conditions: 'Drowsy' and 'Normal.' The driver's level of drowsiness is determined based on the heart rate measured by the detection device. The Blynk application is used as a visual interface to provide notifications via smartphone if the driver is drowsy. The accuracy of the drowsiness detection tool was compared with the results obtained from the Pulse Oximeter. This research shows that the drowsiness detection tool using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor has an accuracy of around 98% when compared with the pulse oximeter. The Blynk application successfully sends notifications precisely when the driver is drowsy. This study highlights the potential of drowsiness detection devices to improve traffic safety and reduce accidents caused by drowsy drivers.
Co-Authors Achmi Yuliani Al Bahri Alfatirta Mufti Elizar Elizar I Ketut Agung Enriko Iskandar Hasanuddin Khairani Khairani M Fahrur Rozi Melinda Melinda Melinda Melinda Mohd. Syaryadhi Muhammad Furqan Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Muhammad Irhamsyah Nur Afny Catur Andryani Nurlida Basir Nurlida Basir Nurrizqa Nurrizqa Riska Sufina Rizal Munadi Shaquille Rizki Ramadhan Na Syahrial Syahrial Syahrial Yudha Nurdin Yuwaldi Away