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All Journal International Journal of Electrical and Computer Engineering Jurnal Pengabdian Magister Pendidikan IPA Indonesian Journal of Electrical Engineering and Computer Science Indonesian Applied Physics Letters ABSYARA: Jurnal Pengabdian Pada Masyarakat Indonesian Community Journal
Osmalina Nur Rahma
Proram Studi S1 Teknik Biomedis, Departemen Fisika, Fakultas Sains Dan Teknologi, Universitas Airlangga, Surabaya, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Journal : Indonesian Applied Physics Letters

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Speech Synthesis Based on EEG Signal for Speech Impaired Patients by Using bLSTM Recurrent Neural Network Abdufattah Yurianta; Anaqi Syaddad Ihsan; Arijal Ibnu Jati; Osmalina Nur Rahma; Aji Sapta Pramulen
Indonesian Applied Physics Letters Vol. 3 No. 1 (2022): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v3i1.40257

Abstract

The disability rate in Indonesia is still relatively high and is one of the main health problems which reaches 30.38 million people or 14.2% of the Indonesian population. One of these types of disabilities is speech impairment. There are several possible causes for speech impairment, including the focal disturbance. This situation occurs because of disturbances in the vocal cords caused by injuries due to accidents and other conditions, such as throat cancer, which of course will reduce the productivity of the sufferer. Sign language can be used to communicate, but it still has limitations for normal individuals. In addition, speech synthesis using brain computer interface (BCI) based on electrocorticography (ECoG) has been developed. However, this method still has a weakness, namely invasive and allows the emergence of large enough scar tissue, so that it can reduce the quality of brain biopotential to be recorded. Therefore, a non-invasive EEG-based speech synthesis method was initiated. This method uses bLSTM as one of the components of the RNN model, so that it can construct syllables into words. This system consists of datasets, data filter programs, data segmentation programs, feature extraction programs, ANN and RNN deep learning model training programs, and text-to-speech programs. ANN and RNN form a 2-level deep learning. The testing accuracy and accuracy of the ANN are 26.04% and 20.83%, while the accuracy of the RNN is 81.25%. To improve these results, in the future, researchers can improve the data collection process and increase the number of the data, use the correct extraction feature, and compare several machine learning architectures, to produce optimal accuracy.
Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children Sayyidul Istighfar Ittaqillah; Delfina Amarissa Sumanang; Quinolina Thifal; Akila Firdausi Harahap; Akif Rahmatillah; Alfian Pramudita Putra; Riries Rulaningtyas; Osmalina Nur Rahma, S.T., M.Si.
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/iapl.v4i1.48153

Abstract

Concentration is the main asset for students and serves as an indicator of successful learning implementation. One of the abnormal disturbances that can occur in a child's concentration development is attention deficit hyperactivity disorder (ADHD). The prevalence of ADHD in Indonesia in 2014 reached 12.81 million people due to delayed management in addressing ADHD. Therefore, early detection of ADHD is necessary for prevention. ADHD detection can be done by testing the level of concentration using a concentration grid. However, a method is needed that can be applied to uncooperative young children who are not familiar with numbers. Therefore, research was conducted with an innovative approach using a combination of EEG-ECG to classify concentration levels. The data used in this study were primary data from 4 participants with 5 repetitions. The data were processed in the preprocessing stage, which involved noise filtering and Butterworth filtering. The features used in this study were BPM (beats per minute), alpha, theta, and beta EEG signals, which would later become inputs for the Adaptive Neuro-Fuzzy Inference System (ANFIS). The output shows that the combination of EEG-ECG has the potential to predict concentration test results. Using BPM, alpha, theta, and beta signals can serve as parameters for predicting the concentration grid test values using ANFIS effectively. In the ANFIS model with 4 features, an accuracy of 99.997% was obtained for the training data and 80.2142% for the testing data. This result could be developed for early detection of ADHD based on concentration levels so the learning implementation could be more effective.
Co-Authors Abdufattah Yurianta Agathon, Daniel Akila Firdausi Harahap Alfian Pramudita Putra Anaqi Syaddad Ihsan Andiani, Dhiela Huriyah Anny Setijo Rahaju, Anny Setijo Arfensia, Danny Sanjaya Arijal Ibnu Jati Chai, Rifai Delfina Amarissa Sumanang Dewi, Rosa Amalia Fitriyatul Qulub Franky Chandra Satria Arisgraha Habibullah, Alamah Ulzanati I Made Mas Dwiyana Prasetya Wibawa Ittaqilah, Sayyidul Istighfar Janice, Theresia Katherine Katherine Katherine Kho Kezia Sarahsophia Immanuel Ryadi Kharisman H, Fourdes Khouliya Zalda Khusnul Ain Khusnul Ain Nafisa Rahmatul Laili Alami Nita Lutfiyah Nizam, Abiyyu PN, M. Rifqi Aditya Pramulen, Aji Sapta Prihartini Widiyanti Quinolina Thifal Rahmatillah, Akif Rifai Chai Riries Rulaningtyas Sayyidul Istighfar Ittaqillah Solly Aryza Sosialita, Tiara Dyah Thevan Y, Georgeous Ummi Hanifah Varyan, Richa Wijaya, Verlita Wiratama, Dyo Yael RBS, Jeanny