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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri
Rifai Chai
Swinburne University of Technology
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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DCT based feature extraction and support vector machine classification for musical instruments tone recognition Linggo Sumarno; Rifai Chai
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3158

Abstract

The conducted research proposes a feature extraction and classification combination method that is used in a tone recognition system for musical instruments. It is expected that by implementing this combination, the tone recognition system will require fewer feature extraction coefficients than those previously investigated. The proposed combination comprises of feature extraction using discrete cosine transform (DCT) and classification using support vector machine (SVM). Bellyra, clarinet, and pianica tones were used in the experiment, with each indicating a tone with one, several, or many major local peaks in the transform domain. Based on the results of the tests, the proposed combination is efficient enough to be used in a tone recognition system for musical instruments. This is indicated in recognizing a tone, it only needs at least eight feature extraction coefficients.
Peningkatan Produktivitas pada Proses Belajar Mengajar di Ruang Kelas dengan Menggunakan Stimulasi Cahaya dan Suara untuk Meningkatkan Fokus dan Kenyamanan Peserta Ajar Henry Candra; Endah Setyaningsih; Jeanny Pragantha; Rifai Chai
Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri 2019: Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Prduktivitas proses belajar mengajar di ruang kelas sangat dipengaruhi oleh tingkat fokus dan kenyamanan dari para peserta ajar. Dengan menggunakan stimulasi berupa cahaya dan suara level fokus dari para peserta di dalam ruang kelas dapat ditingkatkan. Stimulasi cahaya dilakukan dengan melakukan penyesuaian iluminasi pencahayaan yang sesuai standar dan pemilihan temperatur warna cahaya. Sedangkan stimulasi suara dilakukan dengan membangkitkan gelombang suara yang memiliki frekuensi yang beresonansi dengan frekuensi gelombang otak. Pada penelitian ini dilakukan investigasi untuk mengetahui pengaruh dari kombinasi stimulasi cahaya dan suara di ruang kelas pada peserta yang sedang mengikuti suatu pelajaran di kelas tersebut. Percobaan dilakukan dengan mempersiapkan suatu ruang kelas yang dilengkapi dengan penstimulasi cahaya dan suara. Pemantauan level fokus dan kenyamanan dari para peserta diukur dengan menggunakan kuesioner, di mana para peserta diminta untuk menilai tingkat kenyamanan dan fokus mereka dengan memilih temperatur warna cahaya yang paling sesuai untuk masing-masing peserta. Beberapa peserta juga direkam pola gelombang otaknya dengan menggunakan electroencephalography. Hasil analisis dari kuesioner dan pola gelombang otak menunjukkan bahwa pengaturan iluminasi dan pemilihan temperatur warna cahaya yang dikombinasikan dengan stimulasi suara dapat meningkatkan level fokus dan kenyamanan dari para peserta ajar.
Brain-computer interface-based hand exoskeleton with bidirectional long short-term memory methods Osmalina Nur Rahma; Khusnul Ain; Alfian Pramudita Putra; Riries Rulaningtyas; Khouliya Zalda; Nita Lutfiyah; Nafisa Rahmatul Laili Alami; Rifai Chai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp173-185

Abstract

It takes at least 3 months to restore hand and arm function to 70% of its original value. This condition certainly reduces the quality of life for stroke survivors. The effectiveness in restoring the motor function of stroke survivors can be improved through rehabilitation. Currently, rehabilitation methods for post-stroke patients focus on repetitive movements of the affected hand, but it is often stalled due to the lack of professional rehabilitation personnel. This research aims to design a brain-computer interface (BCI)-based exoskeleton hand motion control for rehabilitation devices. The Bidirectional long short-term memory (Bi-LSTM) method performs motion classification for the ESP32 microcontroller to control the movement of the DC motor on the exoskeleton hand in real-time. The statistical features, such as mean and standard deviation from the sliding windows process of electroencephalograph (EEG) signals, are used as the input for Bi-LSTM. The highest accuracy at the validation stage was obtained in the combination of mean and standard deviation features, with the highest accuracy of 91% at the offline testing stage and reaching an average of 90% in real-time (80%-100%). Overall, the control system design that has been made runs well to perform movements on the hand exoskeleton based on the classification of opening and grasping movements.
Co-Authors Alfian Pramudita Putra Henry Candra Jeanny Pragantha Khouliya Zalda Khusnul Ain Linggo Sumarno Nafisa Rahmatul Laili Alami Nita Lutfiyah Osmalina Nur Rahma Riries Rulaningtyas Setyaningsih, Endah