Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal PROtek : Jurnal Ilmiah Teknik Elektro Jiko (Jurnal Informatika dan komputer) The International Journal of Informatics, Data, and Emerging Applications (IDEA International Journal)
Florensce Sumarauw
Sam Ratulangi University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
REAL-TIME DOLPHIN DETECTION IN AQUATIC ENVIRONMENTS USING YOLO11-NANO Febriyanti Ludja; Florensce Sumarauw; Robby Moody Lintong; Steven R. Sentinuwo; Alwin M. Sambul; Muhamad Dwisnanto Putro
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 1 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i1.11256

Abstract

Dolphin monitoring plays a crucial role in maintaining the balance of marine ecosystems and supporting the ecotourism sector. However, in practice, automated dolphin monitoring still faces significant challenges, particularly when deployed in real-time applications within dynamic underwater environments. Previous research on computer vision-based dolphin detection generally uses models with high computational complexity. This condition has resulted in increased resource requirements and long inference times, making it difficult to apply to underwater device-based monitoring systems with limited computing power. Therefore, it is necessary to develop more efficient detection models and algorithms so that the system can operate reliably under real-world monitoring scenarios in resource-limited environments. Moreover, the adoption of the latest-generation lightweight detection architectures in aquatic scenarios remains limited. To address these challenges, this study proposes the application of YOLOv11-Nano as a lightweight detection architecture designed for low-latency dolphin monitoring on resource-constrained devices. The proposed model is optimized to strike a balance between inference speed and detection accuracy, enabling competitive performance under challenging underwater conditions. Experimental results show that YOLOv11-Nano achieves a computational complexity of 6.4 GFLOPs with 2.59 million parameters, while attaining 65.0% mAP@50, 43.1% mAP@50:95, and an inference speed of 18.34 FPS. These results show that YOLOv11-Nano is capable of delivering stable and efficient performance with relatively low computational requirements and high inference speed, demonstrating strong potential for application in real-time monitoring systems based on devices with limited resources to support automatic dolphin detection as part of marine ecosystem conservation efforts.
An Efficient Fall Detector Using Improvement of the YOLOv12n Via ONA-Net Florensce Sumarauw; Febriyanti Ludja; Robby Moody Lintong; Alwin Melkie Sambul; Muhamad Dwisnanto Putro
PROtek : Jurnal Ilmiah Teknik Elektro Vol 13 No 2 (2026): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v13i2.11653

Abstract

Meningkatnya permintaan akan sistem deteksi jatuh yang andal dalam pemantauan perawatan pasien sekaligus membantu lingkungan kehidupan mendorong pengembangan model visi komputer yang mampu mengidentifikasi kejadian jatuh secara akurat dalam kondisi dunia nyata. Sistem ini harus mendeteksi perubahan postur tubuh manusia di berbagai posisi sambil mempertahankan ketahanan terhadap latar belakang yang kompleks, yang seringkali mengurangi akurasi deteksi. Selain itu, penerapan di dunia nyata membutuhkan model yang beroperasi secara efisien pada perangkat berbiaya rendah dan memproses aliran video langsung secara real-time. Studi ini menganalisis efektivitas peningkatan versi nano dari arsitektur YOLOv12 untuk deteksi jatuh dengan mengintegrasikan mekanisme ONA-Net. Modul yang diusulkan memungkinkan jaringan untuk fokus pada beberapa respons penting yang terkait dengan postur tubuh manusia, memungkinkan model untuk menangkap isyarat spasial yang terkait dengan kejadian jatuh dengan lebih baik. Desain yang ringan mengurangi beban komputasi sambil mempertahankan ekstraksi fitur yang efektif untuk deteksi yang akurat. Temuan eksperimental menunjukkan YOLOv12-ONA-Net sebagai model yang diperkenalkan memperoleh kinerja deteksi yang kuat, dengan memperoleh 92,3% mAP@50 dan 59,7% mAP@50:95. Meskipun arsitekturnya ringan, model ini tetap mempertahankan efisiensi praktis dengan mencapai kecepatan inferensi 13,13 frame per detik (FPS). Hasil ini menunjukkan bahwa penggabungan ONA-Net ke dalam jaringan YOLOv12n meningkatkan kemampuan deteksi jatuh sekaligus mempertahankan penggunaan komputasi yang sesuai untuk penggunaan pemantauan waktu nyata pada perangkat dengan kapasitas komputasi terbatas.
A Deep Learning Approach To Human Fall Detection With YOLOv12 Versi Nano Florensce Sumarauw; Febriyanti Ludja; Robby Moody Lintong
International Journal of Informatics, Data, and Emerging Applications Vol. 1 No. 2 (2026): International Journal of Informatics, Data, and Emerging Application
Publisher : Faculty of Engineering, Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35793/nrcmbb88

Abstract

The growing elderly population faces a heightened risk of falls. Current monitoring solutions, such as in-person observation by caregivers or wearable sensors, suffer from several drawbacks. These include low accuracy, user discomfort, and a constant need for human oversight. To overcome these issues, automated approaches using Convolutional Neural Networks (CNNs), especially the YOLO algorithm, have been applied for fast and real-time fall detection. In this study, the original YOLOv12-nano model, which is known for its lightweight architecture, low latency, and efficiency on edge devices, was implemented on the Le2i dataset. The model achieved an mAP50 of 0.866 and an mAP50-95 of 0.555, with only 2.52 M parameters and 6.0 GFLOPs. These provide a strong foundation for further improvement in elderly fall detection systems
Co-Authors Alwin M. Sambul Alwin Melkie Sambul Febriyanti Ludja Febriyanti Ludja Febriyanti Ludja Muhamad Dwisnanto Putro Muhamad Dwisnanto Putro Robby Moody Lintong Robby Moody Lintong Steven Ray Sentinuwo