Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal EMITTER International Journal of Engineering Technology Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Agum Try Wardhana
Politeknik Negeri Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
SMART LAB SAFETY : BUDAYA KESEHATAN DAN KESELAMATAN KERJA (K3) SEJAK DINI DALAM PEMBELAJARAN KIMIA DI SMA Agum Try Wardhana; Dilia Puspa; Iriani Reka Septiana; Apri Mujiyanti; Metta Wijayanti; Melantina Oktriyanti
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 3 No. 6 (2025): Desember
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v3i6.3376

Abstract

Pengabdian ini bertujuan untuk menanamkan budaya Keselamatan dan Kesehatan Kerja (K3) sejak dini dalam pembelajaran kimia di SMA Al-Amalul Khair Palembang dan meningkatkan kesadaran guru serta siswa terhadap keselamatan laboratorium. Metode pengabdian yang digunakan meliputi analisis situasi dan survei lapangan, sosialisasi prinsip K3 melalui presentasi interaktif, diskusi dan tanya jawab, simulasi praktik penggunaan Alat Pelindung Diri (APD), prosedur evakuasi, tanggap darurat, serta evaluasi dan tindak lanjut. Hasil pengabdian menunjukkan peningkatan partisipasi dan pemahaman guru maupun siswa terhadap potensi bahaya di laboratorium, penggunaan APD, prosedur tanggap darurat, dan penerapan budaya K3 secara keseluruhan. Simulasi praktik terbukti paling efektif dalam meningkatkan keterlibatan peserta. Simpulan dari kegiatan pengabdian ini adalah bahwa penerapan budaya K3 sejak dini dapat membangun kesadaran dan kebiasaan aman di laboratorium, yang diharapkan berlanjut tidak hanya selama pembelajaran kimia, tetapi juga pada aktivitas laboratorium lainnya di sekolah.
Exploring YOLO-Based Deep Learning Approaches for Fish Detection in Intelligent Aquatic Monitoring Systems Tresna Dewi; Riyo Irawan; Agum Try Wardhana; Muhammad Amri Yahya; Lukman Nul Hakim; Dini Septiyani AR
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.1007

Abstract

The Advancements in precision aquaculture demand robust visual monitoring systems capable of accurate, real-time fish detection in complex underwater environments characterized by turbidity, occlusion, and dynamic illumination. While YOLO (You Only Look Once) architectures have demonstrated high efficiency in object detection tasks, their comparative performance for underwater fish detection remains underexplored, particularly across recent variants such as YOLOv5, YOLOv8, and YOLOv11. This study presents a systematic evaluation of three state-of-the-art YOLO models using a curated GlowFish dataset consisting of 533 annotated images across three fluorescent species. Data were acquired under controlled but visually diverse conditions using multi-angle imaging and standardized illumination. A uniform training pipeline, consistent annotation using the COCO format, and identical hyperparameters were applied across models to ensure fair benchmarking. Key evaluation metrics include precision, recall, mAP@0.5, and mAP@0.5:0.95. Experimental results reveal that YOLOv5 achieved the highest precision (0.963) and mAP@0.5 (0.967), while YOLOv8 delivered superior recall (0.930) and more balanced detection across species classes. YOLOv11 demonstrated architectural potential but showed greater sensitivity to class imbalance and reduced confidence stability. Visual analysis and confusion matrices further confirmed model-specific trade-offs in classification reliability and localization precision. This work contributes critical empirical insights into the selection of YOLO architectures for intelligent aquaculture systems, offering practical guidance for real-time aquatic monitoring deployments. Future research will extend this framework to multi-species, multi-environment datasets, integrate spatiotemporal behavioral tracking, and investigate deployment on resource-constrained edge-AI platforms, advancing the field toward interpretable and autonomous aquatic monitoring solutions.
Co-Authors Apri Mujiyanti Dilia Puspa Dini Septiyani AR Iriani Reka Septiana Lukman Nul Hakim Melantina Oktriyanti Metta Wijayanti Muhammad Amri Yahya Riyo Irawan Tresna Dewi