Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu

Wellia Shinta Sari, Christy Atika Sari

Abstract


Bunga merupakan modifikasi tunas dimana bentuk, warna, dan susunannya menyesuaikan kepentingan dari tumbuhan tersebut. Bunga berfungsi sebagai tempat berlangsungnya penyerbukan. Ada sangat banyak jenis bunga yang dapat dikenali di dunia ini. Perkembangan teknologi saat ini dapat dimanfaatkan sebagai fasilitas kepada manusia untuk membangun sebuah sistem yang dapat mengenali suatu citra. Penelitian ini, mengusulkan teknik mengidentifikasi citra bunga dengan ekstraksi ciri orde satu dan berbasis Multi-Support Vector Machine (Multi-SVM). Pemilihan ekstraksi ciri orde satu adalah karena merupakan ekstraksi ciri tekstur pada struktur makro yang dianggap cocok dalam mengidentifikasi jenis bunga. Pada tahap ekstraksi ciri, citra yang semula adalah citra RGB dikonversi terlebih dahulu menjadi citra berskala abu-abu. Multi-SVM memiliki keunggulan dalam mengklasifikasikan lebih dari dua kelas. Dalam penelitian ini digunakan lima jenis bunga yaitu Rose, Calendula, Peony, Leucanthemum Maximum, dan Iris dengan 300 citra pelatihan dan 150 citra pengujian. Berdasarkan pengujian identifikasi, menghasilkam akurasi sebesar 90.6667%.

Keywords


Identifikasi; Multi-SVM; Ekstraksi ciri; Orde satu;

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References


M. Nguyen and N. A. V. B, “SVMs with Deep Learning and Random,” in Springer, vol. 2, Springer International Publishing, 2019, pp. 157–172. doi: 10.1007/978-3-030-10925-7.

S. Maji, A. C. Berg, and J. Malik, “Efficient classification for additive kernel SVMs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 66–77, 2013, doi: 10.1109/TPAMI.2012.62.

N. A. Hamid and N. N. A. Sjarif, “Handwritten Recognition Using SVM, KNN and Neural Network,” Feb. 2017, [Online]. Available: http://arxiv.org/abs/1702.00723

H. Hiary, H. Saadeh, M. Saadeh, and M. Yaqub, “Flower classification using deep convolutional neural networks,” IET Computer Vision, vol. 12, no. 6, pp. 855–862, 2018, doi: 10.1049/iet-cvi.2017.0155.

Isman, Andani Ahmad, and Abdul Latief, “Perbandingan Metode KNN Dan LBPH Pada Klasifikasi Daun Herbal,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 3, pp. 557–564, Jun. 2021, doi: 10.29207/resti.v5i3.3006.

Norhidayu and N. Nur, “Handwritten Recognition Using SVM, KNN and Neural Network,” 2017.

M. P. Vaishnnave, K. Suganya Devi, P. Srinivasan, and G. Arutperumjothi, “Detection and classification of groundnut leaf diseases using KNN classifier,” 2019 IEEE International Conference on System, Computation, Automation and Networking, ICSCAN 2019, pp. 1–5, 2019, doi: 10.1109/ICSCAN.2019.8878733.

A. Ambarwari, Q. J. Adrian, Y. Herdiyeni, and I. Hermadi, “Plant species identification based on leaf venation features using SVM,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 2, pp. 726–732, 2020, doi: 10.12928/TELKOMNIKA.V18I2.14062.

M. T. Ghazal and K. Abdullah, “Face recognition based on curvelets, invariant moments features and SVM,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 2, pp. 733–739, 2020, doi: 10.12928/TELKOMNIKA.v18i2.14106.

J. D.Pujari, R. Yakkundimath, and Abdulmunaf. S. Byadgi, “SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 3, no. 7, p. 6, 2016, doi: 10.9781/ijimai.2016.371.

S. Y. R. Riska and P. Subekti, “Klasifikasi Level Kematangan Buah Tomat Berdasarkan Fitur Warna Menggunakan Multi-Svm,” Jurnal Ilmiah Informatika, vol. 1, no. 1, 2016, [Online]. Available: http://ejournal.amiki.ac.id/index.php/JIMI/article/view/8/6

M. M. Sani, S. B. Kutty, H. A. Omar, and I. N. M. Isa, “Classification of Orchid Species using Neural Network,” in IEEE International Conference on Control System, Computing and Engineering, 2013, vol. 23, no. 5, pp. 431–435. doi: 10.5391/jkiis.2013.23.5.431.

P. Kalavathi and T. Priya, “Removal of impulse noise using Histogram-based Localized Wiener Filter for MR brain image restoration,” 2016 IEEE International Conference on Advances in Computer Applications, ICACA 2016, pp. 4–8, 2017, doi: 10.1109/ICACA.2016.7887913.

S. Krishnakumar and K. Manivannan, “Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images,” Journal of Ambient Intelligence and Humanized Computing, no. 0123456789, 2020, doi: 10.1007/s12652-020-02300-8.

J. C. Coetsier and R. Jiamthapthaksin, “Parallelized FPA-SVM: Parallelized parameter selection and classification using Flower Pollination Algorithm and Support Vector Machine,” Proceedings of the 2017 14th International Joint Conference on Computer Science and Software Engineering, JCSSE 2017, 2017, doi: 10.1109/JCSSE.2017.8025899.

P. B. Padol and A. A. Yadav, “SVM classifier based grape leaf disease detection,” Conference on Advances in Signal Processing, CASP 2016, pp. 175–179, 2016, doi: 10.1109/CASP.2016.7746160.

M. S. Kadhm, “Handwriting Word Recognition Based on SVM Classifier,” International Journal of Advanced Computer Science and Applications, vol. 6, no. 11, pp. 64–68, 2015.




DOI: http://dx.doi.org/10.22441/incomtech.v13i1.15012

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pISSN: 2085-4811
eISSN: 2579-6089
Jurnal URL: http://publikasi.mercubuana.ac.id/index.php/Incomtech
Jurnal DOI: 10.22441/incomtech

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