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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) PROtek : Jurnal Ilmiah Teknik Elektro JIKO (Jurnal Informatika dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Muhamad Dwisnanto Putro
Sam Ratulangi University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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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.
Lightweight Enhancement Module for Efficient Sea Turtle Species Detection under Illumination Variation Yuliana Mose; Alex Copernikus Andaria; Hebron Prasetya; Muhamad Dwisnanto Putro
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.7399

Abstract

Sea turtle monitoring is crucial for maintaining ecosystem balance, although traditional methods are costly and inefficient. Object detection provides a foundation for autonomous monitoring by localizing sea turtles in their natural habitat. At the same time, recent deep learning models can operate in real-time. On the other hand, underwater detection remains challenging due to extreme illumination, underwater blur, and underwater distortions, which obscure key features from the background. To address these issues, this work proposes a new sea turtle detector, based on a modified YOLOv12-nano model that incorporates a lightweight enhancement module to improve robustness under diverse illumination. The network introduced Efficient Spatial Convolution (ESC) at the end of the backbone to improve the representation of high-level features. It involves Dual Spatial Convolution (DSC) to improve backbone feature extraction with a large kernel size while preserving efficiency. The Lite module is designed to enrich feature diversity and selectively enhance the quality of high-level features. These modifications significantly enhance detection performance under diverse underwater lighting conditions. The proposed network achieves higher precision than the original YOLOv12-nano while maintaining efficiency, making it well-suited for deployment on low-cost devices.
YOLOv12n-RL-MSCAM: Enhancing Lightweight Trash Detection with Reinforced Local Multi-Scale Channel Attention Module Robby Moody Lintong; Florensce Sumarauw; Febriyanti Ludja; Sary Diane Ekawati Paturusi; Muhamad Dwisnanto Putro
JURNAL INFORMATIKA DAN KOMPUTER Vol 10, No 2 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v10i2.2700

Abstract

The growing demand for efficient environmental monitoring has led to the development of computer vision based trash detection models for real world urban deployment. These systems must recognize diverse waste types under challenging conditions, such as variations in appearance, scale, and complex backgrounds, which can significantly reduce detection accuracy. Moreover practical applications require lightweight models capable of maintaining real-time performance on CPU devices. This study enhances the YOLOv12n architecture for trash detection by integrating a Reinforced Local Multi-Scale Channel Attention Module (RL-MSCAM). The proposed module improves feature representation by highlighting informative channels and reducing irrelevant background noise, leading to better extraction of discriminative spatial features of waste objects. The lightweight design ensures these improvements are achieved without significant computational overhead, making the model suitable for real-time applications. Experimental results show that the proposed model achieves strong detection performance, with 60.6 % mAP@50 and 50.9% mAP@50:95. Despite its lightweight design, the model maintains practical efficiency, reaching an inference speed of 16.12 frames per second (FPS). These findings indicate that integrating RL-MSCAM into the YOLOv12n framework improves trash detection performance while preserving computational efficiency, making it suitable for real-time deployment on CPU devices.
NiteTron: a novel nighttime bunaken sea turtle species detection Muhamad Dwisnanto Putro; Jane Litouw; Abdul Haris Ontowirjo; Sherwin Reinaldo U. Sompie; Hebron Prasetya
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27678

Abstract

Automated monitoring is essential for sea turtle conservation, and vision-based algorithms offer a non-intrusive approach to observing turtles without disturb ing their natural habitat. However, nighttime conditions introduce significant challenges, as low illumination severely reduces the visibility of discriminative features. This limitation requires robust feature extraction to capture the lim ited important features. Recent advances in computer vision, such as you only look once (YOLO) 11-nano, enable real-time detection and allow optimization under challenging conditions. In this study, we propose NiteTron, a nighttime sea turtle species detector that uses a lightweight architecture with enhanced feature refinement. The network modifies YOLO11-nano to create a more ef ficient variant, YOLO11-pico. To improve detection in low-light scenarios, we introduce a strong feature modulation (SFM) module. This module enhances attention to relevant features by expanding the receptive field in the final back bone stage. We also incorporate a sparse separated convolution (S2C) module to extract key information efficiently while balancing accuracy and computational cost. The proposed model achieves high precision with fewer parameters and lower giga floating-point operations per second (GFLOPs) than YOLO11-nano. Experiments demonstrate superior performance, achieving 0.682 mean average precision (mAP)@0.5:0.95 for nighttime sea turtle detection. Inference tests also confirm efficient central processing unit (CPU) deployment, reaching 21.18 frames per second (FPS) on a desktop and 2.65 FPS on an edge device.
A Fast and Efficient Ship Detector Via HDR-CoT Module Jane Litouw; Marsel Marhaen Wungow; Dayen Manoppo; Ni Made S. Mustikayani; Hoang Van Thanh; Muhamad Dwisnanto Putro
Journal of Applied Informatics and Computing Vol. 10 No. 5 (2026): October 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i5.13947

Abstract

Reliable vessel surveillance requires robust computer vision for accurate ship detection. Although YOLO-FAST is computationally efficient, its lightweight architecture has limited capability to extract discriminative features from small objects. To address this limitation, this study proposes an enhanced ship detection framework by integrating a novel Hadamard-Dilated Residual Contextual Transformer (HDR-CoT) module into the YOLO-FAST architecture. The proposed HDR-CoT module integrates Hadamard multiplication with parallel dilated depthwise convolutions. In addition, residual connections enhance feature representation while maintaining low computational overhead. Experimental results demonstrate the effectiveness of the proposed model. It achieves an mAP50 of 0.952 and an mAP50:95 of 0.689 while requiring only 1.73 million parameters and 6.3 GFLOPs. The proposed network achieves a favorable balance between detection performance and efficiency, improving mAP50:95 by 2.99% while reducing the parameters by 24.93%. It also achieves competitive performance compared with other lightweight architectures. CPU-based evaluation further shows that the model operates at 20.54 FPS. This configuration achieves an optimal trade-off between near-real-time inference speed and detection reliability compared with YOLO-FAST for resource-constrained maritime applications.
Co-Authors Abdul Haris Ontowirjo Alex Copernikus Andaria Alwin Melkie Sambul Dayen Manoppo Dedi Sorongan, Yuliana Mose, Reonaldy A Berikang Febriyanti Ludja Febriyanti Ludja Florensce Sumarauw Florensce Sumarauw Hebron Prasetya Hoang Van Thanh Jane Litouw Jane Litouw Marsel Marhaen Wungow Ni Made S. Mustikayani Robby Moody Lintong Sary Diane Ekawati Paturusi Sherwin Reinaldo U. Sompie