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All Journal Sisfo: Jurnal Ilmiah Sistem Informasi
Bayu Ath Thariq Syams
Institut Teknologi Sawit Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering

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Classification of Plant Pests Using the Real-Time Detection Transformer (RT-DETR) Algorithm in Oil Palm Plants Bayu Ath Thariq Syams; Ratu Mutiara Siregar; Muhammad Akbar Syahbana Pane
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27029

Abstract

Plant Pests (PP) are one of the main factors causing a decline in oil palm productivity. This study focuses on the classification of PP, limited to the three main pest species: the rhinoceros beetle (Oryctes rhinoceros), the fireworm (Setora nitens), and the Tioman rat (Rattus tiomanicus). The dataset consists of digital images representing these three pest types. The method used is the Real-Time Detection Transformer (RT-DETR), which is capable of real-time, end-to-end object detection. The research stages include data collection, preprocessing, model training, and evaluation using confusion matrix, precision, recall, and F1-score metrics. The research results are expected to produce an accurate and efficient crop pest classification system to support decision-making in pest management in oil palm plantations.
Co-Authors Muhammad Akbar Syahbana Pane Ratu Mutiara Siregar