EARLY DETECTION OF ASD (AUTISM SPECTRUM DISORDER) IN TODDLER WITH A WEB APPLICATION

• Main Author: Jonathan Vickeldo, Farrel
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/83381
• Institution: Institut Teknologi Bandung

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that significantly impacts an individual's social and communication skills, as well as affecting their family life. ASD affects around 168 million people worldwide, with the latest data showing that Indonesia has 242,541 children with autism in 2022, making it the country with the sixth largest population of children with autism in the world. If examined by professional medical personnel, ASD symptoms can be recognized from the age of 12-24 months. However, various factors, including parents' lack of knowledge about ASD in toddlers, have led to an increase in the number of ASD cases that are detected too late. Research shows that early detection of ASD can significantly reduce the severity of symptoms in the long term. Children diagnosed early showed a significant reduction in the severity of ASD social symptoms within 1–2 years. ASD screening is an important tool to expedite treatment, especially because many toddlers do not receive an official diagnosis until age five or older. Children with ASD tend to experience delays in language development and have restricted and repetitive behavioral patterns, which negatively impacts their social lives. Apart from that, less visible early symptoms can also be an obstacle to detecting ASD for parents so that the number of ASD patients who are taken to professional medical personnel for further diagnosis is still low. The symptoms of ASD often become more apparent as children enter elementary school, which means diagnosis is often delayed. Currently, ASD screening is carried out by medical professionals based on parental concerns, but this process takes a long time and involves many questions and complex assessments. Preschool teachers, such as kindergarten and PAUD teachers, have the potential to play a strategic role in detecting abnormal development in toddlers because they often meet and understand children's development. However, preschool teachers are currently not directly involved in ASD detection due to a lack of training and available screening tools. Research shows that the main barrier for preschool teachers to early detection of ASD is a lack of knowledge and adequate screening tools. Various questionnaire-based ASD screening methods have been developed. However, this method cannot yet be used by the general public due to the complexity of the assessment guide which requires an in-depth understanding of certain cases. Preschool teachers need socialization and basic knowledge regarding ASD, symptoms, the impact of delayed treatment, and how to use these screening methods. Research shows that there are significant differences between the development of academic competence in children with ASD compared to typically developing children. Children with ASD have lower achievement scores in language, mathematics, and world orientation at the end of elementary education. Delayed diagnosis impacts a child's social life, which can lead to anxiety and depression in adolescence. Questionnaire-based screening applications can be an effective solution for early detection of ASD. Currently, several applications such as ASDTracker from Australia and Autism Fingerprint from Oman already exist, but do not yet have Indonesian translations. Applications can be a means to increase preschool teachers' knowledge and sensitivity towards ASD. The Tiny Detector system is designed to help preschool teachers conduct early screening for ASD using the medically approved M-CHAT questionnaire. This system consists of three main components: a Flutter-based front-end service, a Node.js-based back-end service, and a PostgreSQL-based Database Management System (DBMS). The system is deployed using Hostinger. The system is designed to meet ISO/TS 82304-2 standards and has been tested for ease of use, with results showing an average usability score of 64.83%, average efficiency of 80.77%, average effectiveness of 78.21%, average satisfaction of 73.69% and a rating of 'good', average learning ability of 26.40%, average error occurrence of 25%, and average cognitive load of 45.08. Implementation of the Tiny Detector system shows that preschool teachers can increase their confidence and ability in identifying ASD risk in toddlers. Testing of the system shows that the system is easy to use and effective in early detection of ASD risk. With this system, it is hoped that preschool teachers can play an important role in early detection of ASD, so that children with ASD can immediately receive the necessary treatment before it is too late. This system not only increases preschool teachers' awareness and knowledge regarding ASD, but also provides a practical and efficient tool for conducting screening. Self-efficacy testing shows a correlation between increased knowledge and sensitivity to increased teacher self-confidence and ability to identify ASD. This is expected to reduce the burden on medical professionals by providing early detection that can lead to quicker intervention. Thus, the implementation of Tiny Detector is expected to bring significant changes in the detection and treatment of ASD in Indonesia.