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All Journal Jurnal Olahraga Kebugaran dan Rehabilitasi (JOKER) Intelmatics
Muhammad Akhdan
STMIK Amikom Yogyakarta
Computer Science & IT Education Health Professions Public Health Other

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Penerapan Strength Endurance pada anaerobik terhadap Atlet Cabang Olahraga Kick Boxing Krisna, Oswald; Manalu, Nimrot; Octaloren, Maykel; Pangihutan, Orlando; Akhdan, Muhammad
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.v4i2.12320

Abstract

Penerapan Strength Endurance pada anaerobik terhadap atlet cabang olahraga kickboxing. Penelitian ini bertujuan untuk mengkaji pengaruh penerapan strenght endurance pada anaerobik terhadap atlet cabang olahraga kickboxing dalam hal kekuatan, kinerja, dan kemampuan terhadapt latihan. Penelitian menggunakan metode kualitatif dengan pendekatan studi kasus. Data dikumpulkan melalui informasi mendalam dan observasi terhadap beberapa atlet di camp kickboxing yang menerapkan strenght endurance. Hasil penelitian menunjukkan bahwaadanya pengaruh penerapan strenght endurance terhadap peningkatan daya tahan pada anaerobik. Namun, penurunan pengawasan dan arahan pelatih dapat berdampak negatif pada konsistensi latihan dan kemampuan atlet dalam mempertahankan daya tahan yang presisi. Penelitian ini menyarankan adanya keseimbangan antara latihan daya tahan yang diberikan dengan pengawasan minimal untuk memaksimalkan hasil pelatihan.
Development of Osteoporosis Prediction System on Femur and Tibia Bones with Convolutional Neural Network Akhdan, Muhammad; Pratiwi, Dian; Rochman, Abdul
Intelmatics Vol. 5 No. 2 (2025): July-December
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i2.23237

Abstract

Osteoporosis and osteopenia are conditions that commonly affect bone health significantly, this is characterized by decreased bone density causing the risk of fractures especially in the femur and tibia. The prevalence rate of these diseases is calculated from 103,334,579 people between the ages of 15 and 105 years, with an overall prevalence of 18.3%. Fast and accurate detection is needed for the first line of defense for osteoporosis patients and potential patients. This study provides the development of a Convolutional Neural network (CNN) model trained to predict osteoporosis and osteopenia from x-ray radiographs of femur and tibia bones. The proposed model has satisfactory performance on all metrics namely average accuracy 90%, average recall 90%, average F1 score 90%. From these performance results, alternative detection methods using CNN can be considered by medical parties or parties who can utilize the first diagnosis of osteopenia to osteoporosis bone disease handling compared to conventional methods.
Co-Authors Abdul Rochman Dian Pratiwi Krisna, Oswald Manalu, Nimrot Octaloren, Maykel Pangihutan, Orlando