[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120393-id":3,"doc-seo-120393-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},120393,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",54,"Penelitian & Laporan","Deteksi Tingkat Kelelahan Pekerja Berbasis Smartwatch dengan Pendekatan Machine Learning - Mixed method study","Kelelahan fisik merupakan bahaya kerja yang umum dan berdampak pada kesehatan serta keselamatan pekerja lintas industri. Penelitian ini bertujuan mendeteksi tingkat kelelahan pekerja berbasis smartwatch menggunakan pendekatan machine learning pada perusahaan manufaktur di Kabupaten Karawang. Metode yang digunakan adalah mixed explanatory sequential dengan pengukuran hemodinamik melalui smartwatch serta Fatigue Severity Scale (FSS). Hasil melibatkan 42 partisipan, menghasilkan temuan tingkat kelelahan tinggi, uji korelasi HR–skor, dan 9 tema pengalaman kelelahan.","INFORMASI ARTIKEL Received: January, 23, 2025  \nRevised: February, 24, 2025  \nAvailable online: February, 26, 2025  \nat : [https://e-jurnal.iphorr.com/index.php/hjk](https://e-jurnal.iphorr.com/index.php/hjk)  \nDeteksi tingkat kelelahan pekerja berbasis smartwatch dengan pendekatan machine learning pada perusahaan manufaktur di Kabupaten Karawang: Mixed method study  \nSudiono*, Henny Lilyanti  \nFakultas Ilmu Kesehatan, Universitas Horizon Indonesia  \nKorespondensi penulis: Sudiono. *Email: [sudiono.horizon.krw@horizon.ac.id](sudiono.horizon.krw@horizon.ac.id)  \nAbstract  \nBackground: Physical fatigue is one of the most significant and common occupational hazards across various industries. Numerous detection tools, both subjective and objective, have been developed to measure workrelated fatigue. Smartwatches are one such tool that can objectively detect physical fatigue by assessing hemodynamic indicators.  \nPurpose: To detect the level of worker fatigue based on smartwatches with a machine learning approach in manufacturing companies in Karawang Regency.  \nMethod: Mixed explanatory sequential method with fatigue measurement using smartwatch machine learning and Fatigue Severity Scale (FSS) .  \nResults: A total of 42 participants (63%) experienced high levels of fatigue with fatigue scores >42 and an average HR value above 100 times per minute. The Pearson Correlations test produced a correlation coefficient value of 0 .039, indicating a strong relationship between HR values and worker fatigue scores. From the results of interviews on worker fatigue levels, 9 themes of worker fatigue experiences were obtained.  \nConclusion: Fatigue detection with machine learning that combines HR values and fatigue scores is very effective in preventing work-related fatigue.  \nSuggestion: Further research is expected to explore the data more deeply. The use of mixed methods can be maintained because it produces varied data, but the addition of the number of participants and research locations needs to be reconsidered. This is to avoid bias levels and produce algorithms from large amounts of data.  \nKeywords: Detection; Fatigue; Machine Learning Approached; Smartwatch; Workers.  \nPendahuluan: Kelelahan fisik adalah salah satu bahaya kerja yang paling penting dan umum terjadi di berbagai industry. Berbagai macam alat telah digunakan untuk mendeteksi/mengukur kelelahan akibat kerja, baikdeteksi/pengukuran secara subjektif maupun objektif. Smartwatch adalah salah satu alat yang dapat digunakanuntuk mendeteksi tingkat kelelahan fisik secara objektif dengan melihat dari hemodinamik.  \nTujuan: Untuk mendeteksi tingkat kelelahan pekerja berbasis smartwatch dengan pendekatan machine learning pada perusahaan manufaktur di Kabupaten Karawang.  \nMetode: Mixed method explanatory sequential dengan pengukuran kelelahan menggunakan machine learning smartwatch dan Fatigue Severity Scale (FSS) .  \nHasil: Sebanyak 42 partisipan (63%) mengalami kelelahan tingkat tinggi dengan hasil skor kelelahan >42 dan rata- rata nilai HR diatas 100 kali per menit. Uji Pearson Correlations menghasilkan nilai correlation coefficient sebesar 0.039, menunjukkan ada hubungan kuat antara Nilai HR dengan skor kelelahan pekerja. Dari hasil wawancara tingkat kelelahan pekerja menghasilkan 9 tema pengalaman kelelahan pekerja.  \nSimpulan: Deteksi kelelahan dengan machine learning yang mengkombinasikan nilai HR dan skor kelelahan  \nsangat efektif dilakukan untuk mencegah terjadinya kelelahan akibat kerja.  \nSaran: Penelitian selanjutnya diharapkan dapat melakukan eksplorasi data. Penggunaan mixed method dapat dipertahankan karena menghasilkan data yang bervariasi, namun penambahan jumlah partisipan dan lokasi penelitian harus dipertimbangkan ulang. Hal ini untuk menghindari tingkat bias dan menghasilkan algoritma dari jumlah data yang besar.  \nKata Kunci: Deteksi; Kelelahan; Pekerja; Pendekatan Machine Learning; Smartwatch.  \nPENDAHULUAN  \nKelelahan akibat kerja merupakan masalahutama di ","cbCaibnRbeXYgfHV","https://ap.wps.com/l/cbCaibnRbeXYgfHV","pdf",499943,4,1,10,"Indonesian","id",113,"# Abstrak\n## Latar Belakang\n## Tujuan\n## Metode\n## Hasil\n## Simpulan dan Saran\n# Pendahuluan\n## Latar belakang kelelahan akibat kerja\n## Gambaran konteks Kabupaten Karawang\n# Metode Penelitian\n## Mixed method explanatory sequential","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Menentukan tingkat kelelahan pekerja berbasis smartwatch dengan pendekatan machine learning pada perusahaan manufaktur di Kabupaten Karawang.\"},{\"question\":\"Bagaimana metode pengukuran kelelahan dilakukan?\",\"answer\":\"Menggunakan mixed explanatory sequential, dengan pengukuran kelelahan melalui smartwatch berbasis machine learning dan Fatigue Severity Scale (FSS).\"},{\"question\":\"Apa temuan utama terkait tingkat kelelahan?\",\"answer\":\"Sebanyak 42 partisipan (63%) mengalami kelelahan tingkat tinggi, dengan skor kelelahan \\u003e42 dan nilai HR rata-rata di atas 100 kali per menit.\"}]","Deteksi Tingkat Kelelahan Pekerja Berbasis Smartwatch dengan Pendekatan Machine Learning - Mixed method study | PDF",1785729799,15,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"detecting-worker-fatigue-levels-based-on-smartwatch-with-a-machine-learning-approach-mixed-method-study","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/id/document/detecting-worker-fatigue-levels-based-on-smartwatch-with-a-machine-learning-approach-mixed-method-study/120393/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Apa tujuan penelitian ini?","Question",{"text":76,"@type":77},"Menentukan tingkat kelelahan pekerja berbasis smartwatch dengan pendekatan machine learning pada perusahaan manufaktur di Kabupaten Karawang.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana metode pengukuran kelelahan dilakukan?",{"text":81,"@type":77},"Menggunakan mixed explanatory sequential, dengan pengukuran kelelahan melalui smartwatch berbasis machine learning dan Fatigue Severity Scale (FSS).",{"name":83,"@type":74,"acceptedAnswer":84},"Apa temuan utama terkait tingkat kelelahan?",{"text":85,"@type":77},"Sebanyak 42 partisipan (63%) mengalami kelelahan tingkat tinggi, dengan skor kelelahan >42 dan nilai HR rata-rata di atas 100 kali per menit.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]