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All Journal Indonesian Journal of Tropical and Infectious Disease Jurnal Pengabdian Magister Pendidikan IPA Indonesian Physical Review Indonesian Journal of Electrical Engineering and Computer Science Indonesian Applied Physics Letters ABSYARA: Jurnal Pengabdian Pada Masyarakat Jurnal Info Sains : Informatika dan Sains
Alfian Pramudita Putra
Bachelor Of Biomedical Engineering Study Program, Faculty Of Science & Technology, Universitas Airlangga, Surabaya
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Other

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Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children Rahma, S.T., M.Si., Osmalina Nur; Harahap, Akila Firdausi; Rahmatillah, Akif; Putra, Alfian Pramudita; Rulaningtyas, Riries; Thifal, Quinolina; Sumanang, Delfina Amarissa; Ittaqillah, Sayyidul Istighfar
Indonesian Applied Physics Letters Vol. 4 No. 1 (2023): Indonesian Applied Physics Letters - June 2023
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 Agustiyanto, Frans R Alvie Aditya, Shabrina Ama, Fadli Aminatun Dita Ayu Mayasari, Dita Ayu Eko Budi Kuntjoro, Eko Budi Fatkhunisa Rahmawati, Fatkhunisa Fitriyatul Qulub Franky Chandra Satria Arisgraha Harahap, Akila Firdausi Hatta, Agus Muhamad I Made Mas Dwiyana Prasetya Wibawa Ittaqillah, Sayyidul Istighfar Katherin Indriawati Katherine Katherine Kezia Sarahsophia Immanuel Ryadi Khouliya Zalda Khusnul Ain Nafisa Rahmatul Laili Alami Nita Lutfiyah Osmalina Nur Rahma Parastuti, Frazna Perkasa, Sigit Dani Prihartini Widiyanti Rahmatillah, Akif Rifai Chai Riries Rulaningtyas Satrio Adhitioso Solly Aryza Sumanang, Delfina Amarissa Thifal, Quinolina Usamah, Shofi Afghania Wardhani, Inten Firdhausi Yonanda, Yusril Putra