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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal Zetroem
Angger Abdul Razak
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Identification of BSR Disease in Oil Palm Using UAV Imagery through CNN and SCNN Approaches Zakia Azzahro; Rahmadwati; Angger Abdul Razak; Amrul Faruq
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2546

Abstract

Basal Stem Rot (BSR) disease caused by Ganoderma boninense is a major threat to oil palm productivity due to its destructive nature and the challenges associated with early-stage detection. To support sustainable production and mitigate significant yield losses, a system capable of classifying oil palm trees into healthy and infected categories is required. In this study, two deep learning approaches, namely CNN and SCNN, are applied to identify oil palm conditions based on UAV-derived imagery. While CNN is widely used for image-based detection tasks due to its capability to extract relevant visual representations, it is prone to overfitting during training. Therefore, SCNN is employed to address this issue by leveraging image similarity comparison mechanisms. Experimental results show that both methods achieve high classification accuracy, with SCNN outperforming CNN by achieving an accuracy of 96.48% compared to 95.644% for CNN. The superior performance of SCNN indicates its sensitivity to subtle visual differences between healthy and early-stage infected oil palm trees, enabling more reliable classification performance. Thus, SCNN is considered more effective for oil palm condition detection and contributes to reducing overfitting, resulting in improved model stability.
Real-Time Vehicle Detection and Counting Using YOLOv8 and ByteTrack Multi-Object Tracking on Surveillance Cameras Filantropi Yusuf Aji Cahyono; Raden Arief Setiawan; Angger Abdul Razak
ZETROEM Vol 8 No 2 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i2.8313

Abstract

Vehicle counting in tourist area parking facilities is typically performed manually, leading to counting errors and the inability to provide real-time capacity updates. This study proposes an automated real-time vehicle detection and counting system integrating YOLOv8s as the object detector and ByteTrack as the Multi-Object Tracking algorithm on surveillance camera footage to support parking capacity management. The dataset consists of 1,377 images across two vehicle classes, cars and motorcycles, prepared through a data-leakage-free pipeline with a 70/20/10 training-validation-test split followed by horizontal flip augmentation applied exclusively to the training subset. The model was trained for 50 epochs on an NVIDIA GeForce RTX 4050 GPU. A virtual counting line positioned at 70% of the frame height, combined with ByteTrack's persistent unique ID mechanism, enables precise vehicle entry and exit counting while preventing double counting. Evaluation results show that the YOLOv8s model achieved precision of 0.904, recall of 0.961, F1-score of 0.930, and mAP@0.5 of 0.969. Vehicle counting evaluation over a 30-minute test video yielded a counting accuracy of 96.875%, MAE of 2.25, and MAPE of 3.125%. The system operated at an average processing speed of 37.82 FPS, exceeding the real-time threshold of 25–30 FPS. These results indicate that the proposed system has the potential to serve as an alternative solution for automated parking capacity management at tourist area facilities.
Co-Authors Amrul Faruq Filantropi Yusuf Aji Cahyono Raden Arief Setiawan Rahmadwati Zakia Azzahro