Analisis Pengaruh Pola Penggunaan Gadget Terhadap Computer Vision Syndrome Menggunakan Algoritma Machine Learning

This research aims to analyze the impact of gadget usage on eye health using Decision Tree, Random Forest, and Naive Bayes algorithms. The increasing use of gadgets in society potentially causes eye health disorders, specifically Computer Vision Syndrome (CVS) symptoms that require in-depth investigation. Data was collected through a survey questionnaire about gadget usage habits and respondents' eye conditions. The OSEMN method was used to process and analyze data by applying three classification algorithms. Research findings showed the Random Forest algorithm provided the best performance with 73 % accuracy, followed by Naive Bayes at 65 %, and Decision Tree at 64 %. The study provides insights into the impact of gadget usage on eye health and recommendations for maintaining usage balance to prevent health disruptions.