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YOLOv8-Based Detection of Convective Storm Clouds for Cumulonimbus Classification Rafsyam, Yenniwarti; Nurjihan, Shita Fitria; Rinaldi, Arief
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1854

Abstract

Cumulonimbus (CB) clouds are vertically developed convective systems that are capable of producing severe weather phenomena, including turbulence, heavy rainfall, and lightning. These phenomena pose a significant threat to aviation safety. This paper considers an automated CB cloud detection approach using the deep learning algorithm You Only Look Once version 8 on NOAA-19 satellite imagery. The images of 640 × 640 pixels each were labeled into two classes: CB and non-CB. In general, rotation, flip, and random brightening are performed to develop a more robust model. After 100 training epochs, the proposed model produced reliable detection performance, as evidenced by 1,694 TP (true positives), 438 FP (false positives), and 304 FN (false negatives) cases, with a precision of 0.79, recall of 0.84, and an F1-score of 0.81. Validation using METAR reports from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG) confirmed the consistency of the model with observed weather conditions. The results demonstrated that YOLOv8 could provide a rapid and reliable framework for real-time detection and classification of CB clouds, thereby enhancing situational awareness for aviation operations and facilitating the effectiveness of satellite-based early warning systems in convectively active tropical regions.
HUBUNGAN POSISI DUDUK DAN LAMA DUDUK PADA NYERI PUNGGUNG BAWAH Birman, Yuliza; Rinaldi, Arief; Luqmanul Insan Fadhil, Aulia
Nusantara Hasana Journal Vol. 5 No. 8 (2026): Nusantara Hasana Journal, January 2026
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i8.1821

Abstract

Lower Back Pain (LBP) is a common musculoskeletal problem experienced by many people, including students who have the habit of sitting for long periods. This condition can interfere with daily activities and the quality of students' lives. The high global prevalence of LBP highlights the urgency to understand the risk factors of LBP. Sitting habits with non-ergonomic positions and prolonged sitting durations have been identified as potential contributors to the development of LBP in students, which can negatively impact academic activities. Objective: To determine the relationship between sitting position and duration with complaints of lower back pain among students of the Faculty of Medicine at Baiturrahmah University. Methods: This study used an analytical method with a cross-sectional study design. The study respondents consisted of 91 medical students at Baiturrahmah University, selected using consecutive sampling technique. Data collection was conducted using the RULA questionnaire instrument and The Pain and Distress Scale. The data analysis used was the chi-square test. Results: From 91 student samples from the Faculty of Medicine at Baiturrahmah University, the majority of respondents were 21 years old (61.5%), with females being the most common gender (56%), and having a normal BMI (50.5%). Most had a very high-risk sitting posture (47.3%) and sat for more than 4 hours/day (83.5%). The majority of those experiencing lower back pain were in the mild category (86.8%). Statistical analysis showed a significant relationship between sitting posture and lower back pain (p-value=0.015) and between prolonged sitting duration and lower back pain (p-value=0.025). Conclusion: Based on the analysis above, it can be concluded that there is a relationship between sitting posture and prolonged sitting duration with lower back pain in students of the Faculty of Medicine at Baiturrahmah University.