TELKOMNIKA Telecommunication Computing Electronics and Control Vol. 22, No. 5, October 2024, pp. 1178~1186 ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v22i5.26092  1178 Journal homepage: http://telkomnika.uad.ac.id Leveraging of recurrent neural networks architectures and SMOTE for dyslexia prediction optimization in children Yuri Pamungkas1, Muhammad Rifqi Nur Ramadani2 1Department of Medical Technology, Faculty of Medicine and Health (MEDICS), Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia 2Department of Biology, Faculty of Science and Data Analytics (SCIENTICS), Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia Article Info ABSTRACT Article history: Received Feb 18, 2024 Revised Jun 22, 2024 Accepted Jul 12, 2024 Dyslexia in children are serious problems that need to be addressed early. Many previous studies have focused on the detection/prediction of dyslexia. However, in the prediction process, there is often an imbalance in the dataset used (between patients with dyslexia and non-dyslexia). Therefore, we are trying to build a system using recurrent neural networks architectures that can quickly and accurately predict the possibility of a child having dyslexia. To overcome the data imbalance between dyslexics and non-dyslexics, we also apply the synthetic minority oversampling technique (SMOTE) method to the dataset. SMOTE will synthesize dyslexic data to balance the numbers with non-dyslexic data. This study used a dataset of 3640 participants (392 dyslexic and 3248 non-dyslexics). For the process of predicting dyslexia, several algorithms such as simple recurrent neural networks (RNN), long short term- memory (LSTM), and gate recurrent units (GRU) are used. As a result, there is an increase in prediction accuracy when SMOTE is applied (compared to without SMOTE) in the dyslexia forecasting process using RNN (92.68% for training and 91.16% for testing), LSTM (94.81% for training and 93.16% for testing), and GRU (96.43% for training and 92.24% for testing). Using SMOTE+RNN architecture in this research increased the accuracy of dyslexia prediction by up to 5% compared to without SMOTE. Keywords: Dyslexia Gate recurrent unit Long short term-memory Recurrent neural networks Synthetic minority oversampling technique This is an open access article under the CC BY-SA license. Corresponding Author: Yuri Pamungkas Department of Medical Technology, Faculty of Medicine and Health (MEDICS) Institut Teknologi Sepuluh Nopember 60111 Sukolilo, Surabaya, Indonesia Email: yuri@its.ac.id 1. INTRODUCTION Dyslexia (learning disorder) has become a problem that needs serious attention today. According to the International Dyslexia Association, the number of people with dyslexia ranges from 15-20% of the world’s population [1]. In Europe alone, people with dyslexia range from 5-12% (37.3-89.5 million people) of the entire European population [2], while in the United States, it is estimated that more than 35 million people have dyslexia even though only 2 million people are detected [3]. The Indonesian Dyslexia Association also states that around 5 million Indonesian children are indicated to have dyslexia [4]. The signs of someone with dyslexia have started to be seen since childhood [5]. It is characterized by reading abilities much lower than their age, difficulty processing and understanding what they hear, difficulty remembering the sequence of events and finding the right words or sentences to answer questions [6]. If dyslexia in children is not paid attention to, these habits will carry over into adulthood and cannot be changed. Therefore, early detection and treatment