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All Journal Jurnal Ilmiah Teknologi dan Rekayasa JURNAL PERTANIAN PRESISI (JOURNAL OF PRECISION AGRICULTURE)
Genoveva, Zahwa
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Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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MODEL MACHINE LEARNING UNTUK DETEKSI TINGKAT KEMATANGAN TANDAN BUAH SEGAR KELAPA SAWIT MENGGUNAKAN METODE YOLOV8 Genoveva, Zahwa; Syah, Rama Dian
Jurnal Pertanian Presisi (Journal of Precision Agriculture) Vol 8, No 2 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/jpp.2024.v8i2.11848

Abstract

Kemajuan teknologi informasi membawa banyak perubahan dibidang pertanian. Pemanfaatan teknologi dapat dilakukan pada kelapa sawit untuk mendukung pertaniandi Indonesia. Penelitian ini bertujuan untuk membuat model machine learning untuk deteksi tingkat kematangan Tandan Buah Segar (TBS) kelapa sawit menggunakan model YOLOv8. Model machine learning ini dirancang untuk meningkatkan akurasi dan efisiensi penentuan kematangan buah kelapa sawit, yang sangat penting bagi industri kelapa sawit. Dataset yang digunakan dalam penelitian ini terdiri dari 6592 citra yang dikumpulkan dari platform Roboflow, yang mencakup berbagai tingkat kematangan buah kelapa sawit. Metodologi penelitian yang diterapkan adalah Cross Industry Standard Process for Data Mining (CRISP-DM), yang meliputi tahap pemahaman bisnis, pemahaman data, persiapan data, pemodelan, dan evaluasi. Proses pelatihan model machine learning berlangsung selama 3107 jam dengan nilai precision mencapai 0.945, nilai recall mencapai 0.947, dan nilai mean Average Precision (mAP) mencapai 0.98. Model deteksi ini mampu mendeteksi tingkat kematangan kelapa sawit dengan baik yang dibuktikan oleh evaluasi model dengan nilai kurva f1-confidence mencapai 95% serta nilai kurva precision-recall mencapai 98%.
Optimalisasi Deteksi Tingkat Kematangan Tanda Buah Segar Kelapa Sawit Menggunakan YOLOV8 Dengan Platform Web Mardhiyah, Iffatul; Sari, Dyan Prawita; Genoveva, Zahwa; Kosasih, Rifki; Irawati, Dyah Cita
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 30 No. 3 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2025.v30i3.67

Abstract

Oil palm represents one of Indonesia’s principal commodities. Traditionally, farmers manually monitor the ripeness level of palm oil, but this method is neither effective nor efficient for large-scale harvests. Therefore, a system that can automatically detect the ripeness level of fresh fruit bunches (FFB) is needed. In this study, the YOLOv8 algorithm was used which was integrated into a web-based application. The system is designed to improve accuracy and efficiency in the grading process of oil palm fruits, which directly impacts the quality of processed products and palm oil production. The dataset used consists of 6.592 images obtained through the Roboflow platform, covering various ripeness categories. The system development follows the CRISP-DM approach, consisting of business understanding, data understanding, data preparation, modeling, evaluation and deployment. The model training process approximately 3,1 hours, with evaluation results showing a precision of 94,5%, recall of 94,7%, and a mean Average Precision (mAP) of 98%. The model’s performance is further supported by an F1-confidence curve of 95% and a precision-recall curve of 98%, indicating stable and accurate classification capabilities. The model is deployed through a Streamlit-based web interface, allowing users to perform real-time detection from images or videos without requiring additional installations.
Co-Authors Irawati, Dyah Cita Mardhiyah, Iffatul Rama Dian Syah Rifki Kosasih Sari, Dyan Prawita