Research Article
Analysis Of Down Passing Movement Using The Kinovea Application On Extraculicular Students Of State Junior High School 6 Percut Sei Tuan
@INPROCEEDINGS{10.4108/eai.28-10-2022.2327514, author={Benny Aprial M and Liliana Puspa Sari and Alan Alfiansyah Putra Karo-karo and Hardodi Sihombing and Eka Abdurrahman and Ibrahim Ibrahim and Fery Adrian}, title={Analysis Of Down Passing Movement Using The Kinovea Application On Extraculicular Students Of State Junior High School 6 Percut Sei Tuan}, proceedings={Proceedings of the 8th ACPES (ASEAN Council of Physical Education and Sport) International Conference, ACPES 2022, October 28th -- 30th, 2022, Medan, North Sumatera, Indonesia}, publisher={EAI}, proceedings_a={ACPES}, year={2023}, month={6}, keywords={analysis of down passing motion volleyball}, doi={10.4108/eai.28-10-2022.2327514} }- Benny Aprial M
Liliana Puspa Sari
Alan Alfiansyah Putra Karo-karo
Hardodi Sihombing
Eka Abdurrahman
Ibrahim Ibrahim
Fery Adrian
Year: 2023
Analysis Of Down Passing Movement Using The Kinovea Application On Extraculicular Students Of State Junior High School 6 Percut Sei Tuan
ACPES
EAI
DOI: 10.4108/eai.28-10-2022.2327514
Abstract
The method combines quantitative methods and qualitative methods. This research method aims to aim to obtain data that is more comprehensive, valid, reliable, and objective. The combined method (mixed methods) combines quantitative and qualitative research with the number of athletes being 2 people as samples in this study. The data in this study used Kinovea software to make professional athletes as comparison athletes and to obtain motion analysis as an assessment of the correctness of motion. Based on the motion analysis carried out, the researcher makes assessment indicators to obtain data by making sections of the initial position, contact with the ball, and follow-up movements using five camera angles from the right, left, top, front and rear. The results of the kinovea software analysis for the prefix position are very good (15%), good (12%), moderate (4%) poor (4%), very poor (65%). the position of implementation in the categories is very good (37.5%), good (37.5%), moderate (6%), poor (6%), very poor (13%). the position of the category ending is very good (8%), good (23%), moderate (2%), less (2%), very less (65%). The results of the analysis using category prefix position experts are very good (16.66%), good (66.66%), poor (16.66%), very poor (65%). very good (5%), good (5%), poor (33%), very poor (57.%) categories. the position of the category ending is very good (0%), good (75%), less (25%), very less (0%).