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All Journal MATHunesa: Jurnal Ilmiah Matematika Jurnal Olahraga Kebugaran dan Rehabilitasi (JOKER)
Ilham Pratama
Universitas Negeri Medan
Mathematics

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Penerapan Small side Game pada Atlet Cabang Olahraga Futsal Junius Daniel; Nimrot Manalu; Bastian Lois; Fauzia Hany; Ilham Pratama
Jurnal Olahraga Kebugaran dan Rehabilitasi (JOKER) Vol 4 No 2 (2024): Jurnal Olahraga Kebugaran dan Rehabilitasi (JOKER)
Publisher : Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/joker.v4i2.12515

Abstract

Penerapan Latihan Small side game terhadap atlet cabang olahraga Futsal. Penelitian ini bertujuan untuk mengkaji pengaruh penerapan latihan tersebut terhadap atlet cabang olahraga futsal dalam hal mengoper, kinerja, dan kemampuan terhadap latihan. Penelitian menggunakan metode kualitatif dengan pendekatan studi kasus. Data dikumpulkan melalui informasi mendalam dan observasi terhadap beberapa atlet di klub futsal yang menerapkan small site game. Hasil penelitian menunjukkan bahwa adanya pengaruh penerapan small side game terhadap peningkatan mengoper ,kinerja atlet. Namun, penurunan pengawasan dan arahan pelatih dapat berdampak negatif pada konsistensi latihan dan kemampuan atlet dalam mempertahankan kinerja yang presisi.
KLASIFIKASI LAJU PERTUMBUHAN PENDUDUK ANTAR PROVINSI DI INDONESIA MENGGUNAKAN METODE K-NEAREST NEIGHBOR Agung Atra Perkasa; Emon Sejahtera Zendrato; Ilham Pratama; Hizkia Simamora
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p271-278

Abstract

Population data plays a crucial role in development planning, yet its utilization has not yet been optimized to generate truly informative insights. This study aims to classify population growth rates across provinces in Indonesia into low, medium, and high categories using the K-Nearest Neighbor (KNN) algorithm. The research process includes data collection and preprocessing, category labeling using the quantile method, data normalization via Min-Max Scaling, and splitting the data into training and test sets with a 70:30 ratio. The KNN model was built using parameter values of k, namely 3, 5, and 7, and the best value of k was selected based on the model evaluation results. Evaluation was performed using a confusion matrix by calculating the accuracy value. The test results showed that the best model was obtained at k = 5 with an accuracy of 75%. These findings indicate that KNN can identify similarities in demographic characteristics across provinces quite well, although there are still classification errors in classes with closely related characteristics. Therefore, the KNN method can serve as a simple and effective approach for population data analysis and has the potential to support data-driven decision-making.
Co-Authors Agung Atra Perkasa Bastian Lois Emon Sejahtera Zendrato Fauzia Hany Hizkia Simamora Junius Daniel Nimrot Manalu