Level of Long and Short Service Skills in Badminton Students at Public High School 1 West Telukjambe with Machine Learning

DOI

10.2991/978-2-38476-273-6_27How to use a DOI?

Keywords

Ability Level; Long and Short Serve; Machine Learning

Abstract

This study investigates the skill level in short and long serves amongst young badminton players from SMAN 1 Telukjambe Barat. Focusing on 20 student athletes, the researchers employed a quantitative approach that involved testing and data analysis. They see potential in leveraging new technologies like Artificial Intelligence (AI), particularly Machine Learning (ML), to gain deeper insights from badminton performance data. Machine Learning’s ability to categorize athletes based on various factors allows coaches to tailor training plans to individual needs. This study specifically explored the impact of such a learning model on the basic techniques of these teenage athletes. The analysis revealed a range of skill levels in both short and long serves. Descriptive statistics were used to examine the data, showing a distribution of skill levels for short serves: very good (15%), good (10%), and satisfactory (50%). Long serves displayed a similar distribution with athletes categorized as very good (10%), good (10%), satisfactory (50%), and unsatisfactory (30%). These findings suggest that the badminton players at SMAN 1 Telukjambe Barat possess varying skill levels in these fundamental techniques, highlighting the potential benefits of implementing personalized training strategies informed by data analysis.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nana Suryana Nasution
AU  - Ardawi Sumarno
AU  - Dian Budhi Santoso
AU  - Reni Rahmadewi
PY  - 2024
DA  - 2024/08/02
TI  - Level of Long and Short Service Skills in Badminton Students at Public High School 1 West Telukjambe with Machine Learning
BT  - Proceedings of 5th Borobudur International Symposium on Humanities and Social Science 2023
PB  - Atlantis Press
SP  - 226
EP  - 234
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-273-6_27
DO  - 10.2991/978-2-38476-273-6_27
ID  - Nasution2024
ER  -