Level of Long and Short Service Skills in Badminton Students at Public High School 1 West Telukjambe with Machine Learning Nana Suryana Nasution, Ardawi Sumarno, Dian Budhi Santoso, Reni Rahmadewi, Ulinnuha Latifa
Universitas Singaperbangsa Karawang
Abstract
This study expects to decide the capacity level of short serve and long serve in badminton for understudies of Sman 1 Telukjambe Barat. This^s a quantitative engaging review utilizing test and estimation methods. This study^s participants were all SMAN 1 Telukjambe Barat students. Twenty students make up the sample of this study^s data. New technologies such as artificial intelligence are key tools to take data analysis to a higher level. One popular is Machine Learning. ML is able to provide better visualization of athlete performance and evaluate. Therefore, this study aims to assess the influence of a ML-based learning model on the basic techniques of teenage badminton players.
The findings of this study, students at Sman 1 Telukjambe Barat have varying levels of short serve and long serve skills
Both short serve and long serve test methods were used for sampling. Statistical methods were the used to analyze the research.
For the short serve athletes, there are 3 very good athletes (15%), 2 very good athletes (10%), 10 very good athletes. enough, 5 in the less category (25%), 0 in the very less category (0%), and enough (50%)
For the long serve variable, the long serve ability level, understudies is in the generally excellent class upwards of 2 individuals (10%), 2 individuals in the great class (10%), 10 individuals in the Enough class (half), 6 individuals in the unfortunate class (30%), and 0 individuals in the extremely less class (0%)
Keywords: Ability Level- Long and Short Serve, machine Learning
