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PENCARIAN BAKAT (TALENT SCOUTINGI) RENANG PADA ANAK MENGGUNAKAN ALGORITMA K-NEAREST NEIGBORS

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Swimming requires an objective and efficient talent identification system. This study develops a machine learning-based system using the K-Nearest Neighbors (KNN) algorithm to predict young athletes' potential based on anthropometric and motor skill data. The dataset consists of 50 normalized samples evaluated through K-Fold Cross Validation (K=5 and 10). Test results show the optimal configuration at K=2 with 60% accuracy for both validation methods. Although the overall accuracy remains below 65%, this system can serve as a preliminary tool for talent scouting, particularly for short and long distance categories. These findings indicate the need for further development in terms of data quantity and more optimal classification methods.

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Detail Information

Series Title

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Call Number

T1844382025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiv, IV-2 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Kecerdasan Buatan
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

SEW

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