Klasifikasi Jenis Buah Mangga Menggunakan Convolutional Neural Network (CNN) Berbasis Citra Digital
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Published:
Dec 31, 2025
Keywords:
Mango classification, Convolutional Neural Network, Deep learning, Image dataset, Google Colab
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Abstract
Sektor pertanian dan perdagangan Indonesia sangat dibantu oleh klasifikasi jenis mangga. Studi ini menerapkan Convolutional Neural Network (CNN) untuk mengklasifikasikan sembilan jenis mangga menggunakan gambar digital. Dataset dikumpulkan secara langsung melalui kamera smartphone, yang menghasilkan gambar awal untuk setiap kelas mangga. Sebelum proses pra-pengolahan, resize dan normalisasi dilakukan, dan model CNN dilatih untuk mengekstraksi fitur dan klasifikasi dengan menggunakan lapisan konvolusi dan dense. Hasil evaluasi menunjukkan bahwa model memiliki akurasi sekitar 90% dalam mengidentifikasi jenis mangga. Di masa depan, sistem ini diharapkan dapat membantu perkembangan pertanian cerdas dan membantu klasifikasi buah secara otomatis.
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Aulia, R., & Husna, W. (2025). Klasifikasi Jenis Buah Mangga Menggunakan Convolutional Neural Network (CNN) Berbasis Citra Digital. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 6(4), 417 - 421. https://doi.org/10.62527/jitsi.6.4.473
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References
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[4] S.-L. Wu, H.-Y. Tung, dan Y.-L. Hsu, “Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights,” arXiv preprint, arXiv:2011.11378, 2020.
[5] Z. G. Mursalin, A. Taqwa, dan I. Salamah, “Design and Development of Mango Ripeness Classification Tool using CNN Android-based Platform,” SISTEMASI, vol. 12, no. 1, 2023.
[6] A. K. Pathak, P. Saikia, dan S. Dutta, “Development of a Robust CNN Model for Mango Leaf Disease Detection and Classification: A Precision Agriculture Approach,” ACS Agricultural Science & Technology, vol. 4, no. 8, pp. 806–817, 2024.
[7] Hidra Amnur, Vadreas, A. K., & Ridwan, M. (2024). Aplikasi Pendeteksi Kematangan Tanaman Menggunakan Metode Transformasi Ruang Warna HSI (Hue, Saturation, Intensity) dan K-NN (K- Nearest Neighbor). JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 5(4), 161 -167. https://doi.org/10.62527/jitsi.5.4.318
[8] M. Rahat, et al., “Deep CNN-Based Mango Insect Classification,” in Computer Vision and Machine Learning in Agriculture, Springer, 2021.
[9] N. M. Trieu dan N. T. Thinh, “Mango Panicle Stage Classification Using YOLOv3-Rotated, MangoYOLO, and R²CNN,” Agronomy, vol. 10, no. 1, pp. 143, 2021.
[10] S. Srivastava et al., “Mango Varieties Classification-Based Optimization with Transfer Learning and Deep Learning Approaches,” dalam Advances in Machine Learning and Data Science, Springer, pp. 33–44, 2022.
[11] Hidayat, R., Nuryana, Z., Erianda, A., Alanda, A., Rasyidah, Amnur, H. (2026). Visual Analysis and Scientometric Mapping of Deep Learning Techniques for Grapevine Disease Classification. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2025. Lecture Notes in Computer Science, vol 16215. Springer, Singapore. https://doi.org/10.1007/978-981-95-3808-9_18
[12] F. Handayani, R. Cahyani, dan E. Prasetyo, “Analisis CNN LeNet‑5 dalam Klasifikasi Daun Mangga,” Jurnal CoSciTech, vol. 6, no. 2, 2024.
[13] S. Ali, M. A. Raza, dan H. Zafar, “Computer Vision System for Mango Fruit Defect Detection Using Deep CNN,” Foods, vol. 11, no. 21, pp. 3483, 2021.
[14] V. A. Gurumurthy, R. Kestur, dan O. Narasipura, “Mango Tree Net – Semantic Segmentation of Mango Trees,” arXiv preprint, arXiv:1907.06915, 2019.
[15] P. Borianne, F. Borne, dan J. Sarron, “Deep Mangoes: Detection and Cultivar Identification with Faster R‑CNN,” arXiv preprint, arXiv:1909.10939, 2019.
[16] K. Simonyan dan A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint, arXiv:1409.1556, 2015.
[17] A. Krizhevsky, I. Sutskever, dan G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 25, 2012