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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Technology Informatics and Engineering Sevaka: Hasil Kegiatan Layanan Masyarakat
Andi Prayogi
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Engineering Health Professions

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Mobile Robot Detects Pests And Diseases In Palm Oil Nurseries Raden Aris Sugianto; Andi Prayogi; Hasanal Fachri Satia Simbolon
Journal of Technology Informatics and Engineering Vol. 3 No. 1 (2024): April : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i1.159

Abstract

The palm oil industry is an important economic pillar in various countries, making a significant contribution to global vegetable oil production. Despite its vital role, oil palm growth is often faced with serious challenges such as pest and disease attacks that can threaten crop yields and plant health. Oil palm seeding, as the initial stage in the plant growth cycle, is key in ensuring optimal quality and productivity in the future. The successful application of mobile robot technology in detecting pests and diseases in oil palm nurseries will not only increase production efficiency, but also support the principles of sustainability and wise resource management.
Penyuluhan Internet Sehat Dan Aman Untuk Masyarakat Kelurahan Sentang, Kecamatan Kisaran Timur, Kabupaten Asahan Khairul Saleh; Muhammad Akbar Syahbana Pane; Novialdi Ashari; Andi Prayogi
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 3 No. 4 (2025): November : Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v3i4.588

Abstract

This community service program aims to improve digital literacy among residents of Sentang Urban Village, Kisaran Timur Subdistrict, Asahan Regency. The main issues addressed include low public awareness of safe and healthy internet usage and the risks of data misuse, hoaxes, and cyberbullying. A participatory counseling method was employed, involving residents directly in discussions, simulations, and digital security practices. The results show a significant increase in participants’ understanding of digital ethics, cybersecurity, and wise internet usage. Additionally, the activity led to the formation of a new social institution called the Internet Safety Awareness Group (KWSIS), which serves as a community-based educational forum. These findings indicate that participatory approaches are effective in fostering digital literacy-based social change.
Performance Evaluation of YOLOv9, YOLOv10, and YOLOv11 for Real-Time Early Detection of Ganoderma Boninense in Oil Palm Rizky Delianngi; Ratu Mutiara Siregar; Nurliana; Muhammad Akbar Syahbana Pane; Phaklen Ehkan; Andi Prayogi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7479

Abstract

Early detection of Ganoderma boninense infection is essential to reduce yield losses in oil palm plantations. This study aims to evaluate the performance of three recent YOLO architectures, namely YOLOv9, YOLOv10, and YOLOv11, for real-time detection of early infection symptoms under natural field conditions. A dataset of 2,000 annotated RGB images was used with a 70:20:10 split for training, validation, and testing. Model performance was evaluated using precision, recall, F1-score, mean average precision (mAP50 and mAP50–95), and inference speed. The results show that YOLOv9 achieved the highest detection accuracy with an mAP50 of 0.989 and F1-score of 0.968. Meanwhile, YOLOv11 demonstrated the best computational efficiency with an inference speed of 35 FPS and processing time of 28.5 ms per frame. These findings indicate a trade-off between accuracy and speed, where YOLOv9 is suitable for accuracy-oriented applications, while YOLOv11 is more appropriate for real-time deployment in precision agriculture.
Co-Authors Ashari, Novialdi Hasanal Fachri Satia Simbolon Khairul Saleh Muhammad Akbar Syahbana Pane Nurliana Phaklen Ehkan Ratu Mutiara Siregar Rizky Delianngi