Klasifikasi Tingkat Kematangan Buah Kelapa Sawit Menggunakan EfficientNet-B7
Abstract
Determining the ripeness level of oil palm fresh fruit bunches (FFB) is a crucial factor affecting oil yield and quality; however, field assessment is still largely performed manually and is prone to subjectivity and errors. This study aims to develop an image-based classification system for oil palm fruit ripeness using a Convolutional Neural Network (CNN) with the EfficientNet-B7 architecture. The proposed method applies transfer learning and fine-tuning on the public dataset “An Ordinal Dataset for Ripeness Level Classification in Oil Palm Fruit Quality Grading,” which contains 4,728 images across five ripeness classes. The methodology includes image preprocessing, normalization, and data augmentation techniques such as rotation, flipping, and zooming. The model is trained using the Adam optimizer and evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the proposed model achieves an accuracy of 93.23% with stable performance across all classes. These findings indicate that EfficientNet-B7 is effective for oil palm fruit ripeness classification and has strong potential to be implemented as a decision-support system for more objective and consistent harvest timing.
Keywords: EfficientNet-B7; Convolutional Neural Network; Ripeness classification
Abstrak
Penentuan tingkat kematangan tandan buah segar (TBS) kelapa sawit merupakan faktor penting yang memengaruhi rendemen dan kualitas minyak sawit, namun proses penilaiannya di lapangan masih dilakukan secara manual sehingga rentan terhadap subjektivitas dan kesalahan. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi tingkat kematangan buah kelapa sawit berbasis citra digital menggunakan algoritma Convolutional Neural Network (CNN) dengan arsitektur EfficientNet-B7. Metode yang digunakan meliputi transfer learning dan fine-tuning pada dataset publik “An Ordinal Dataset for Ripeness Level Classification in Oil Palm Fruit Quality Grading” yang terdiri dari 4.728 citra dalam lima kelas kematangan. Tahapan penelitian mencakup preprocessing citra, normalisasi, serta augmentasi data berupa rotasi, flip, dan zoom. Model dilatih menggunakan optimizer Adam dan dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model mencapai accuracy sebesar 93,23% dengan performa klasifikasi yang stabil pada seluruh kelas. Berdasarkan hasil tersebut, EfficientNet-B7 terbukti efektif untuk klasifikasi tingkat kematangan buah sawit dan berpotensi diterapkan sebagai sistem pendukung keputusan dalam penentuan waktu panen yang lebih objektif.
Keywords
References
E. F. Himmah, M. Widyaningsih, and M. Maysaroh, “Identifikasi Kematangan Buah Kelapa Sawit Berdasarkan Warna RGB Dan HSV Menggunakan Metode K-Means Clustering,” J. Sains dan Inform., vol. 6, no. 2, pp. 193–202, 2020, doi: 10.34128/jsi.v6i2.242.
GAPKI, “Kinerja Industri Minyak Sawit Tahun 2023 & Prospek Tahun 2024.” [Online]. Available: https://gapki.id/news/2024/02/27/kinerja-industri-minyak-sawit-tahun-2023-prospek-tahun-2024/
F. Murgianto, Edyson, A. Ardiyanto, S. K. Putra, and L. Prabowo, “Potensi Kandungan Minyak Kelapa Sawit dengan Berbagai Tingkat Berondolan Lepas di Piringan ( Potential Content of Palm Oil at Various Levels of Loose Fruit in Oil Palm Circle ),” Agro Ind. Perkeb., vol. 9, no. 2, pp. 91–98, 2021.
D. Bayu, Priyambada, and G. Supriyanto, “Analisis Rendemen Minyak Kelapa Sawit (CPO) berdasarkan Tingkat Kematangan Buah di PT . Bumitama Gunajaya Agro,” J. Agroforetech, vol. 1, no. 03, pp. 2051–2060, 2023.
W. E. Sari, M. Muslimin, A. Franz, and P. Sugiartawan, “Deteksi Tingkat Kematangan Tandan Buah Segar Kelapa Sawit dengan Algoritme K-Means,” SINTECH (Science Inf. Technol. J., vol. 5, no. 2, pp. 154–164, 2022, doi: 10.31598/sintechjournal.v5i2.1146.
S. Yuliany, Aradea, and Andi Nur Rachman, “Implementasi Deep Learning pada Sistem Klasifikasi Hama Tanaman Padi Menggunakan Metode Convolutional Neural Network (CNN),” J. Buana Inform., vol. 13, no. 1, pp. 54–65, 2022, doi: 10.24002/jbi.v13i1.5022.
S. V. Darshan, “Automated Diagnosis and Cataloguing of Foliar Disease in Apple Trees using Ensemble of Deep Neural Networks,” Int. Res. J. Eng. Technol., no. May, pp. 4230–4237, 2020, [Online]. Available: www.irjet.net
M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 10691–10700, 2019.
A. Rahmawati, M. Akbar, and D. Nurdiansyah, “Klasifikasi Tingkat Kematangan Pada Buah Kelapa Sawit Berbasis Deep Learning,” vol. 4, pp. 1064–1073, 2025.
I. Afandy, B. N. Ahmad, and M. R. Amartharizqi, “Klasifikasi Kematangan Sawit Menggunakan Metode Convolutional Neural Network (CNN) Dengan Transfer Learning Xception,” J. Ilm. Teknol. Inf. dan Robot., vol. 6, no. 2, pp. 88–98, 2024, doi: 10.33005/jifti.v6i2.158.
R. Triyogi, R. Magdalena, and B. Hidayat, “Mendeteksi Kematangan Buah Kelapa Sawit Menggunakan Convolutional Neural Network Deep Learning,” J. Nas. SAINS dan Tek., vol. 1, no. 1, pp. 22–27, 2023, doi: 10.25124/jnst.v1i1.6732.
M. S. Muna, Y. Setiyo, I. P. S. Wirawan, M. Syarovy, and G. H. Jaya, “Klasifikasi Kematangan Buah Kelapa Sawit Menggunakan Model Yolov8 Berbasis Deep Learning,” J. Agric. Biosyst. Eng. Res., vol. 6, no. 1, p. 50, 2025, doi: 10.20884/1.jaber.2025.6.1.15953.
A. chandra Saputra and E. D. Oktaviyani, “Rancang Bangun Sistem Deteksi Kematangan Buah Kelapa Sawit Berdasarkan Deteksi Warna Menggunakan Algoritma K-Nn,” J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 7, no. 2, pp. 222–229, 2023, doi: 10.47111/jti.v7i2.9232.
I. M. Kamal, S. Mochammad, L. Nurahmi, A. Natawijaya, and M. Kalili, “An Ordinal Dataset for Ripeness Level Classification in Oil Palm Fruit Quality Grading.” [Online]. Available: https://data.mendeley.com/datasets/424y96m6sw/1
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.
Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
S. K. Parinduri, R. Rosnelly, A. Purnama, A. Sihotang, M. Chintya, and U. P. Utama, “Perbandingan Algoritma Support Vector Machine Dan K-Nearest Neighbors Pada Kematangan Buah Sawit,” vol. 13, no. 2, pp. 147–155, 2023.
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