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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) ILKOM Jurnal Ilmiah Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Journal of Information Systems and Technology Research Journal of Computers and Digital Business Wahana Internet of Things and Artificial Intelligence Journal Servire: Jurnal Pengabdian Kepada Masyarakat Jurnal Agro Fabrica Journal of Technology Informatics and Engineering The Indonesian Journal of Computer Science Jurnal Edukasi Citra Olahraga Journal of Digital Technology and Computer Science
Ratu Mutiara Siregar
Institut Teknologi Sawit Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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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.
Perbandingan Model Spasial Kesesuaian Lahan Kelapa Sawit di Pulau Sumatera Menggunakan Algoritma Machine Learning Ferdy Hardiansyah; Ratu Mutiara Siregar; Muhammad Akbar Syahbana Pane; Andi Prayogi
Journal of Computers and Digital Business Vol. 5 No. 2 (2026)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i2.987

Abstract

Pulau Sumatera merupakan salah satu wilayah utama pengembangan kelapa sawit di Indonesia dengan karakteristik biofisik yang kompleks. Pemanfaatan lahan yang tidak mempertimbangkan kesesuaian biofisik berpotensi menurunkan produktivitas dan meningkatkan degradasi lingkungan. Penelitian ini bertujuan mengintegrasikan pendekatan berbasis aturan FAO dengan metode machine learning untuk memodelkan kesesuaian lahan kelapa sawit secara lebih interpretatif. Algoritma Decision Tree digunakan untuk mempelajari pola klasifikasi dari kriteria FAO dan dibandingkan dengan K-Nearest Neighbor (KNN). Variabel penelitian meliputi kemiringan lereng, curah hujan, suhu udara, pH tanah, tekstur tanah, kedalaman tanah, dan tutupan lahan. Dataset diperoleh dari ekstraksi data raster ke format tabular dengan pembagian data latih dan uji sebesar 80:20. Hasil penelitian menunjukkan kelas S2 mendominasi wilayah penelitian sebesar 61,06%, diikuti S3 sebesar 18,46%, S1 sebesar 14,26%, dan N sebesar 6,22%. Evaluasi cross-validation menunjukkan akurasi Decision Tree sebesar 88,94% dan KNN sebesar 87,18%. Decision Tree memiliki performa lebih stabil dan mudah diinterpretasikan. Penelitian ini menunjukkan integrasi FAO dan machine learning dapat mendukung perencanaan penggunaan lahan yang lebih objektif, transparan, dan berkelanjutan.
Crude Palm Oil (CPO) Production Prediction Information System Using A Linear Regression Algorithm Erin Triani Sipayung; Ritna Wahyuni; Ratu Mutiara Siregar
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/dtcs.v3i2.679

Abstract

Purpose – Crude Palm Oil (CPO) production fluctuates with Fresh Fruit Bunches (FFB) supply and operational conditions, making production planning difficult for palm oil mills. This study develops a CPO production prediction information system that integrates a simple linear regression model into a Progressive Web Application (PWA) to provide an accessible decision-support tool. Methods – The study used 39 monthly production records from PTPN IV Regional II Adolina Palm Oil Mill, covering January 2023 to March 2026. FFB was used as the independent variable and CPO production as the dependent variable. The model was developed using simple linear regression, evaluated through an 80/20 train-test split, MAE, RMSE, and R², and implemented in a PWA-based system using PHP and MySQL. Findings – The regression model produced the equation Y = -101.869 + 0.238X. The model achieved R² = 0.872, MAE = 279.80 tons, and RMSE = 335.72 tons. With average monthly CPO production of 2,000–3,000 tons, the MAE represents an approximate error rate of 10–14%, indicating moderate predictive performance. Research implications – The findings are useful for preliminary production planning, but generalization is limited by the use of one predictor, 39 observations, one palm oil mill, and the absence of k-fold cross-validation. Originality – This study contributes by combining an interpretable linear regression model with a PWA-based system for real-time CPO prediction and visualization.
Co-Authors Abdussalam Al Akbar Alex Rizky Saputra Ali Ikhwan Alimuddin Yasin Alisarjuni Padang Aly, Moustafa H. Andi Prayogi Andi Prayogi Annisa Annisa Ardila Asnita Yani Awal, Elsa Elvira Balqis Nurmauli Damanik Busrizal Faisal Elfitra Erin Triani Sipayung Fadilah, Sandi Fajar Maulana . Ferdy Hardiansyah Friska Anggraini Barus Guntoro, Guntoro Kan, Phak Len Al Eh Khairul Saleh Liga Mayola Mahyuddin K. M Nasution Maisarah Maisarah Maisarah, Maisarah Makhrani Sari Ginting Maudy Hellena Harlyn Muhammad Akbar Syahbana Pane Muhammad Yusuf Dibisono Mulyara, Budi Navea, Roy Francis Nurliana Phaklen Ehkan Prayogi, Andi Rahmad Dian Rahmad Dian Rahmad Dian Rahmamnsyah, Eka Patma Ritna Wahyuni Ritna Wahyuni Rizky Delianngi safira, silky Sari, Yessi Ratna Sepriano Sepriano Simbolon, Hasanal Fachri Satia Sri Wahyuna Saragih Tuty Ningsih Wisnu Ananta Kusuma Yasir, Amru