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YOLOv11n-Based Oil Palm Health Classification Using Orthomosaic Drone Imagery Maudy Hellena Harlyn; Ritna Wahyuni; Andi Prayogi
Teknika Vol. 15 No. 2 (2026): July 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i2.1494

Monitoring oil palm plantation conditions across large-scale areas remains challenging because manual inspection is time-consuming, costly, and prone to observational errors. This study aims to develop a GIS-based monitoring system for oil palm health detection using the YOLOv11n algorithm and orthomosaic imagery acquired from UAV mapping. The study employed the Software Development Life Cycle (SDLC) Waterfall model and Unified Modeling Language (UML) for system development and design. The research stages included orthomosaic image acquisition, image tiling, dataset annotation, data augmentation, YOLOv11n model training, system implementation, and functional testing. The dataset was collected from oil palm plantation areas owned by PT Bakrie Sumatera Plantations and classified into three categories: healthy, unhealthy, and dead trees. The evaluation results demonstrated high detection performance with 0.99 precision, 0.99 recall, 0.99 mAP50, and 0.88 mAP50-95. The developed GEOPALM system was capable of generating centroid-based visualizations, plantation condition distribution graphs, and spatial outputs for plantation monitoring purposes. Overall, the proposed system can support faster, more efficient, and spatially structured oil palm plantation monitoring.

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 Mandiri

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

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.

Palm Oil Quality Based on Free Fatty Acid Using SVM Andi Prayogi; Moustafa H. Aly; Ali Ikhwan; Muhammad Akbar Syahbana Pane; Ratu Mutiara Siregar
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24797

Background: Palm oil is one of the key commodities in both the food and non-food industries, with its quality largely influenced by the level of Free Fatty Acid (FFA). Obejctive: High FFA content can reduce the stability and market value of the oil. Classify palm oil quality based on FFA levels using the Support Vector Machine (SVM) algorithm. Methods: FFA levels were measured across multiple samples with varying usage frequencies (0, 5, 7, and 9 cycles) using the alkalimetric titration method. The measured data was categorized as "Suitable" if FFA ≤ 0.3% and "Unsuitable" if it exceeded this threshold. The developed SVM model was trained using 70% of the data and tested with the remaining 30%. Results: Evaluation results indicate that the model achieved an accuracy of 95%, a precision of 92%, and a recall of 94%, demonstrating SVM's effectiveness in classifying data. Additionally, hyperplane visualization using PCA provided a clearer distinction between oil categories based on FFA levels. Conclusion: This study highlights that SVM can serve as an effective alternative for FFA-based palm oil quality classification. The implementation of this model is expected to enhance efficiency in the palm oil industry, particularly.

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

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.

Development of an ESP32-Based Motor-Balancing Prototype for Fresh Fruit Bunch Transportation Using Crawler and Loader Wheel Modes Ridho Agustiawan Rangkuti; Andi Prayogi; Raden Aris Sugianto
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1477

Purpose – This study aimed to develop and evaluate an ESP32-based motor-balancing prototype for Fresh Fruit Bunch (FFB) transportation using crawler and loader wheel modes to address inefficiencies in manual FFB transportation on uneven plantation terrains.Methods – A prototype development approach was employed, consisting of problem identification, system requirement analysis, mechanical and electronic design, component testing, prototype assembly, and performance evaluation. The prototype integrated an ESP32 microcontroller, HC-12 wireless communication modules, an L298N motor driver, DC motors, a power supply system, and a substitute load container. Performance evaluation included basic movement testing, HC-12 wireless communication testing, terrain adaptability testing on simulated muddy and rocky surfaces, and load-carrying testing using substitute loads ranging from 500 to 2000 g.Findings – The developed prototype successfully executed forward, backward, left turn, right turn, and stop commands with a 100% success rate during repeated laboratory testing. Stable wireless communication was maintained up to 20 m using HC-12 modules, while the crawler and loader wheel configurations demonstrated stable mobility on simulated muddy and rocky terrains. The prototype successfully transported substitute loads of up to 1500 g, whereas a 2000 g load exceeded the available motor torque.Research implications – The prototype was evaluated only under laboratory conditions using simulated terrains and substitute loads. Therefore, further validation under actual plantation environments and with real Fresh Fruit Bunches is required before practical deployment.Originality – This study presents a laboratory-scale ESP32-based motor-balancing transportation prototype that integrates HC-12 wireless communication and interchangeable crawler-loader wheel modes into a single platform specifically designed for Fresh Fruit Bunch transportation in oil palm plantations.

Development of an Android-Based QR Code Information System for Offline-First Palm Oil Harvest Recording in Plantations Bintang Permata Hati Simanjuntak; Raden Aris Sugianto; Andi Prayogi
Journal of Digital Technology and Computer Science Vol. 3 No. 3 (2026): August 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i3.791

Purpose – Manual palm oil harvest recording at remote plantations often causes transcription errors, duplicate records, delayed reporting, and difficulties in implementing fully online data collection because of unstable Internet connectivity. This study aimed to design and develop an Android-based palm oil harvest recording information system using Quick Response (QR) Code identification and an offline-first data architecture. Methods – This study employed a Research and Development approach using the Rapid Application Development model. Data were collected through field observations, interviews with five foremen and one administrative staff member, documentation reviews, and functional system testing. The application was developed using Android Studio and Flutter, with SQLite for local offline storage and Firebase Cloud Firestore for online data synchronization. Findings – The developed system enables QR Code-based worker identification, harvest data input, offline data storage, automatic synchronization, digital signature validation, activity-log monitoring, and report export. Black-box testing of seven functional scenarios showed that all tested features operated correctly in both offline and online conditions. Research implications – The findings indicate that an offline-first mobile architecture can support harvest recording in plantation areas with limited connectivity and low-cost devices. However, the system was tested in one plantation environment; therefore, broader implementation requires further multisite evaluation. Originality – This study contributes by integrating QR Code identification, SQLite-based offline persistence, Firebase synchronization, and Android-based field reporting into a single harvest recording system for remote palm oil plantation operations.

Sosialisasi Dan Pelatihan Smart Plantation Berbasis Internet Of Things (IOT) Menggunakan ESP32 Untuk Monitoring Kelembaban Tanah Pada Kelompok Tani Kelapa Sawit Raden Aris Sugianto; Ritna Wahyuni; Ratu Mutiara Siregar; Andi Prayogi; Sri Lestari Rahayu
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 2 (2026): JURMAS BANGSA
Publisher : Riset Sinergi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Perkebunan kelapa sawit merupakan salah satu sektor strategis yang berperan penting dalam mendukung perekonomian nasional. Namun, pengelolaan lahan oleh kelompok tani masih menghadapi berbagai kendala, salah satunya adalah keterbatasan dalam memantau kondisi kelembaban tanah secara berkala sehingga penyiraman dan pengelolaan tanaman belum dilakukan secara optimal. Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan pengetahuan dan keterampilan kelompok tani kelapa sawit dalam memanfaatkan teknologi Smart Plantation berbasis Internet of Things (IoT) menggunakan mikrokontroler ESP32 sebagai sistem monitoring kelembaban tanah secara real-time. Metode pelaksanaan meliputi tahap identifikasi kebutuhan mitra, sosialisasi konsep Smart Plantation dan IoT, pelatihan perakitan perangkat ESP32 yang terintegrasi dengan sensor kelembaban tanah, praktik penggunaan sistem monitoring, serta evaluasi pemahaman peserta melalui diskusi dan demonstrasi. Hasil kegiatan menunjukkan bahwa peserta mampu memahami konsep dasar IoT, mengoperasikan sistem monitoring kelembaban tanah berbasis ESP32, serta menyadari pentingnya penerapan teknologi digital dalam mendukung pengelolaan perkebunan kelapa sawit yang lebih efektif, efisien, dan berkelanjutan. Kegiatan ini diharapkan dapat mendorong transformasi digital pada sektor perkebunan melalui penerapan teknologi Smart Plantation yang sesuai dengan kebutuhan kelompok tani.

Sosialisasi Dan Pelatihan Smart Plantation Berbasis Internet Of Things (IOT) Menggunakan ESP32 Untuk Monitoring Kelembaban Tanah Pada Kelompok Tani Kelapa Sawit Raden Aris Sugianto; Ritna Wahyuni; Ratu Mutiara Siregar; Andi Prayogi; Sri Lestari Rahayu
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 2 (2026): JURMAS BANGSA
Publisher : Riset Sinergi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Perkebunan kelapa sawit merupakan salah satu sektor strategis yang berperan penting dalam mendukung perekonomian nasional. Namun, pengelolaan lahan oleh kelompok tani masih menghadapi berbagai kendala, salah satunya adalah keterbatasan dalam memantau kondisi kelembaban tanah secara berkala sehingga penyiraman dan pengelolaan tanaman belum dilakukan secara optimal. Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan pengetahuan dan keterampilan kelompok tani kelapa sawit dalam memanfaatkan teknologi Smart Plantation berbasis Internet of Things (IoT) menggunakan mikrokontroler ESP32 sebagai sistem monitoring kelembaban tanah secara real-time. Metode pelaksanaan meliputi tahap identifikasi kebutuhan mitra, sosialisasi konsep Smart Plantation dan IoT, pelatihan perakitan perangkat ESP32 yang terintegrasi dengan sensor kelembaban tanah, praktik penggunaan sistem monitoring, serta evaluasi pemahaman peserta melalui diskusi dan demonstrasi. Hasil kegiatan menunjukkan bahwa peserta mampu memahami konsep dasar IoT, mengoperasikan sistem monitoring kelembaban tanah berbasis ESP32, serta menyadari pentingnya penerapan teknologi digital dalam mendukung pengelolaan perkebunan kelapa sawit yang lebih efektif, efisien, dan berkelanjutan. Kegiatan ini diharapkan dapat mendorong transformasi digital pada sektor perkebunan melalui penerapan teknologi Smart Plantation yang sesuai dengan kebutuhan kelompok tani.

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