Intervensi Bugar (body, soul, good, and relax) untuk menurunkan stres dan kelelahan pada pekerja industri manufaktur
DOI: https://doi.org/10.33024/hjk.v19i10.1657
Intervensi Bugar Kelelahan Industri Manufaktur Pekerja Stres
Abstract
Background: Physical fatigue is one of the most important and common occupational hazards across various industries. Various tools have been used to detect/measure work-related fatigue, both subjectively and objectively. Smartwatches are one such tool that can be used to objectively detect physical fatigue levels by observing hemodynamics.
Purpose: To apply a machine learning approach based on data collected from smartwatches to develop a fatigue detection model for workers in manufacturing environments.
Method: This research employed a mixed explanatory sequential method, measuring fatigue using machine learning-based smartwatches and the Fatigue Severity Scale (FSS).
Results: A correlation coefficient of 0.039 was obtained, indicating a strong relationship between HR values and participants' fatigue scores. Interviews on participants' fatigue levels yielded nine themes of workers' fatigue experiences.
Conclusion: Fatigue detection using machine learning, which combines HR values and fatigue values, is highly effective in preventing work-related fatigue.
Suggestion: Further research is expected to further explore data with more participants involved, the use of Android and iOS-based applications can be considered to expand the reach of manufacturing workers who can be involved in future research.
Keywords: Bugar Intervention; Fatigue; Industry; Manufacturing; Stress; Workers.
Pendahuluan: Kelelahan fisik adalah salah satu bahaya kerja yang paling penting dan umum terjadi di berbagai industri. Berbagai macam alat telah digunakan untuk mendeteksi/mengukur kelelahan akibat kerja, baik deteksi/pengukuran secara subjektif maupun objektif. Smartwatch adalah salah satu alat yang dapat digunakan untuk mendeteksi tingkat kelelahan fisik secara objektif dengan melihat dari hemodinamik.
Tujuan: Untuk menerapkan pendekatan pembelajaran mesin berdasarkan data yang dikumpulkan dari smartwatch untuk mengembangkan model deteksi kelelahan bagi pekerja di lingkungan manufaktur.
Metode: Penelitian mixed metode explanatory sequential dengan pengukuran kelelahan menggunakan machine learning smartwach dan fatigue severity scale (FSS).
Hasil: Diperoleh nilai correlation coefficient sebesar 0.039, menunjukkan ada hubungan kuat antara nilai HR dengan skor kelelahan partisipan. Dari hasil wawancara tingkat kelelahan partisipan menghasilkan 9 tema pengalaman kelelahan pekerja.
Simpulan: Deteksi kelelahan dengan machine learning yang mengkobinasikan nilai HR dan nilai kelelahan kerja sangat efektif dilakukan untuk mencegah terjadinya kelelahan akibat kerja.
Saran: Penelitian selanjutnya diharapkan lebih melakukan ekspolarasi data dengan lebih banyak lagi partisipan yang dilibatkan, penggunaan aplikasi berbasis android maupun iOS dapat dipertimbangkan untuk memperluas jangkauan pekerja manufaktur yang bisa dilibatkan dalam penelitian yang akan datang.
Kata Kunci: Intervensi Bugar; Kelelahan; Industri; Manufaktur; Pekerja; Stres.
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