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INDONESIA
Jurnal Penelitian Kesehatan Suara Forikes
Published by Forum Ilmiah Kesehatan
ISSN : 20863098     EISSN : 25027778     DOI : -
Core Subject : Health,
Health Professions Medicine & Pharmacology Nursing Public Health
Journal of Health Research "Forikes Voice" is a medium for the publication of articles on research and review of the literature. We accept articles in the areas of health such as public health, medicine, nursing, midwifery, nutrition, pharmaceutical, environmental health, health technology, clinical laboratories, health education, and health popular.
Arjuna Subject : -
Articles 22 Documents
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Search results for , issue "2026" : 22 Documents clear
F: Pendekatan Metode Predictive Analytics Untuk Deteksi Dini Potensi Kecelakaan Kerja : Tinjauan Literatur Sistematis Abbadfitrah, Roid; Ramdhan, Doni Hikmat
Jurnal Penelitian Kesehatan SUARA FORIKES Vol 17, No 2 (2026): February 2026
Publisher : FORIKES

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

K3 adalah aspek penting dalam melindungi pekerja dan menjaga kelangsungan operasional perusahaan, terutama di sektor berisiko tinggi seperti energi, konstruksi, dan manufaktur. Pendekatan K3 yang reaktif sering kali tidak cukup untuk mencegah kecelakaan. Transformasi digital memungkinkan penerapan analitik prediktif sebagai strategi proaktif, menggunakan data historis dan algoritma pembelajaran mesin untuk mendeteksi risiko sebelum insiden terjadi. Penelitian ini melakukan tinjauan literatur sistematis tentang penggunaan analitik prediktif dalam deteksi awal kecelakaan kerja, memetakan metode, algoritma, dan hasil implementasi di berbagai industri. Studi ini menggunakan pendekatan scoping review dengan kerangka PRISMA-ScR, pencarian artikel dilakukan di basis data IEEE Xplore, ScienceDirect, dan PubMed dari 2015–2025. Dari 264 artikel, diperoleh 9 studi empiris yang relevan. Berdasarkan hasil tinjauan literatur menunjukkan algoritma yang umum digunakan seperti Random Forest, Support Vector Machine, dan lainnya, mampu memprediksi kecelakaan, risiko ergonomi, dan kepatuhan pekerja dengan akurasi tinggi (≥90%). Penerapan analitik prediktif dapat mengurangi kecelakaan hingga 25% dan meningkatkan manajemen keselamatan melalui sistem peringatan dini. Kajian literatur sistematis menegaskan pentingnya analitik prediktif dalam mengubah manajemen K3 dari reaktif ke proaktif. Integrasi big data, IoT, dan AI menciptakan sistem berbasis data yang efektif untuk mencegah kecelakaan kerja, memberikan rekomendasi bagi peneliti dan praktisi K3 untuk mengadopsi model prediktif dalam strategi pencegahan yang berkelanjutan.
Green Betel Leaf Compress as an Effective Therapy for Reducing Breast Engorgement Pain During Lactation Jannah, Isnaini Nurul; Amalia, Linda; Puspita, Asih Purwandari Wahyoe
Jurnal Penelitian Kesehatan SUARA FORIKES Vol 17, No 1 (2026): January 2026
Publisher : FORIKES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33846/sf170108

Abstract

Breast engorgement is a common condition among lactating mothers, characterized by breast fullness, tightness, and pain. This study aimed to compare the effectiveness of green betel leaf compresses and cabbage leaf compresses in reducing breast pain associated with engorgement. A quasi-experimental pretest–posttest design with two groups was employed, involving 70 breastfeeding mothers who were randomly assigned to either the green betel leaf compress group (n = 35) or the cabbage leaf compress group (n = 35). The intervention was administered twice daily for three consecutive days, with each session lasting 15 minutes. Pain intensity was measured using the Visual Analog Scale before and after the intervention, and participant satisfaction was assessed using a structured questionnaire. The results demonstrated that the mean pain score in the green betel leaf compress group decreased from 5.66 to 0.49, whereas in the cabbage leaf compress group it decreased from 5.54 to 4.31. The Wilcoxon test indicated a statistically significant reduction in pain in both groups (p < 0.001), and the Mann–Whitney test revealed a significant difference in effectiveness between the two interventions (p < 0.001). The mean satisfaction score was higher in the green betel leaf compress group (90.69) compared with the cabbage leaf compress group (59.51). In conclusion, both compress types were effective in reducing breast engorgement pain; however, green betel leaf compresses demonstrated more consistent pain reduction and higher levels of participant satisfaction.Keywords: breast engorgement; green betel leaf compress; cabbage leaf compress; breast pain; breastfeeding mothers

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