Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer

Laila Marifatul Azizah, Sitti Fadillah Umayah, Febriyana Fajar


Mangosteen is one of Indonesian potential export fruits. Nevertheless, mangosteens quality is compulsary. A good quality fruit surface is needed in export fruit. This is the reason of this research to detect the flaw in rind surface, particularly mangosteen. Some researcher has been done many type of image processing for fruit detection. However, there aren’t any research for mangosteen rind detection especially used Deep Learning. This research used CNN (Convolutional Neural Network) as deep learning method to detect mangosteen rind surface. Our research is to find configuration which was the best accurancy value. The rind detection calcuted between epoch and layer to obtain maximum accurancy value. This method achieved maximum value by parameter 4 layer and epoch value of 30. From our experiment, the test result for rind detection was 98% accurancy.


deep learning, Convolution neural network, Epoch, mangosteen

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Balai Besar Penelitian dan Pengembangan

Pascapanen Pertanian. (2010). Laporan

Kinerja 2010. Bogor: Badan Penelitian

dan Pengembangan Pertanian.

Gonzalez, R. C., & Woods, R. E. (2002).

Digital Image Processing. New Jersey:

Pearson Education.

Hermawati, F. A. (2013). Pengolahan Citra

Digital: Konsep dan Teori. Yogyakarta:


Khoje, S. A., Bodhe, S. K., & Adsul, A. (2013).

Automated Skin Defect Identification

System for Fruit Grading Based on

Discrete Curvelet Transform.

International Journal of Engineering

and Technology (IJET), 5(4), 3251-

Krizhevsky, A., Sutskever, I., & Hinton, G. E.

(2012). Imagenet Classification With

Deep Convolutional Neural Networks. In

Advances in neural information

processing systems (pp. 1097-1105).

LeCun, Y., Bengio, Y., & Hinton, G. (2015).

Deep Learning. Nature, 521 (7533), 436-

Mathworks. (2017). Convolutional Neural

Network. Diambil kembali dari



Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee,

H., & Ng, A. Y. (2011). Multimodal

deep learning. In Proceedings of the

th international conference on

machine learning (ICML-11) (pp. 689-

Putra, W. S. E. (2016). Klasifikasi

Citra Menggunakan Convolutional

Neural Network (CNN) pada Caltech

Jurnal Teknik ITS, 5(1).

Ranjit, K. N., Chethan, H. K., & Naveena, C.

(2016). Identification and Classification

of Fruit Diseases. International Journal

of Engineering Research and Application

(IJERA), pp.11 – 14).

Sianipar, R. (2013). Pemograman MATLAB

dalam Contoh dan Terapan. Bandung:

Penerbit Informatika.

Sindonews. (2014). Tiga jenis buah-buahan ini

jadi andalan ekspor Indonesia. Diambil

kembali dari


ead/853574 /34/tiga-jenis-buah-buahanini-


Springenberg, J. T., Dosovitskiy, A., Brox, T.,

& Riedmiller, M. (2014). Striving for

Simplicity: The All Convolutional Net.


Socher, R., Huval, B., Bath, B., Manning, C.

D., & Ng, A. Y. (2012). Convolutional-

Recursive Deep Learning for 3D Object

Classification. In Advances in Neural

Information Processing Systems (pp.


Zufar, M., & Setiyono, B. (2016).

Convolutional Neural Networks untuk

Pengenalan Wajah Secara Real-Time.

Jurnal Sains dan Seni ITS, 5(2).


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