<![CDATA[General Background: Automated athlete measurement systems are increasingly required in combat sports to support accurate classification, efficient data management, and competition validation processes. Specific Background: Conventional weighing procedures in pencak silat competitions still rely on manual measurements and independent weighing devices without integrated classification or web-based recording systems, creating risks of athlete misclassification and administrative difficulties. Knowledge Gap: Previous studies primarily focused on nutritional assessment systems using rule-based or z-score methods, while limited research has integrated automatic athlete classification, Body Mass Index (BMI) analysis, website integration, and decision tree algorithms in pencak silat competitions. Aims: This study aims to design and develop an automatic body weighing system for pencak silat athlete class classification using the decision tree method and integrated website monitoring. Results: The system utilized load cell sensors for body weight measurement and Time of Flight (ToF) sensors for height detection, while the ESP32 microcontroller processed classification and BMI calculations. Experimental results demonstrated an average error rate of 0.81% and success rate of 99.19% for body weight measurements, while height measurements achieved an average error rate of 1.52% and success rate of 98.48%. The decision tree classification results were consistent with manual calculations across athlete categories from pre-teen to adult levels. Novelty: The study integrates automatic athlete classification, BMI evaluation, sensor-based measurements, and website-based monitoring within a single decision tree framework. Implications: The proposed system supports accurate athlete verification, digital sports data management, and automated classification processes for pencak silat competitions.]]>
