Evaluasi Optimizer pada Residual Network untuk Klasifikasi Klon Teh Seri GMB Berbasis Citra Daun

KOREDIANTO USMAN, NOR KUMALASARI CAECAR PRATIWI, NUR IBRAHIM, HERI SYAHRIAN, VITRIA PUSPITASARI RAHADI

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ABSTRAK

Komoditas teh berperan strategis terhadap pertumbuhan perekonomian Indonesia, salah satunya dari teh klon Gambung (GMB). Klon GMB memiliki beberapa karakter khas, dengan tingkat kemiripan morfologi yang sangat tinggi. Hal ini berdampak pada proses pengenalan klon GMB dilakukan melalui pengamatan visual oleh tenaga ahli sangat rentan terhadap kesalahan identifikasi. Sehingga, dalam penelitian ini dirancang suatu sistem identifikasi terhadap 11 klon teh seri GMB (GMB-1 hingga GMB-11) dengan menggunakan arsitektur ResNet101. Evaluasi sistem akan dilakukan dengan membandingkan tujuh algoritma optimizer yang berbeda, yaitu; Adam, SGD, RMSProp, AdaGrad, AdaMax, AdaDelta dan Nadam. Hasil pengujian menunjukkan bahwa Adam dan SGD memberikan nilai rata-rata presisi, recall dan F1-score terbaik. Selain itu, Adam memberikan nilai akurasi yang cenderung stabil sejak iterasi pertama. Metode yang diusulkan memberikan tingkat presisi, recall, F1-score sebesar 96% dan akurasi terbaik sebesar 97%.

Kata kunci: klasifikasi daun teh, seri Gambung (GMB), CNN, ResNet101

 

ABSTRACT

Gambung (GMB) tea is one of the tea commodities that plays a key role in Indonesia's economic development. GMB clones have a number of distinguishing characteristics, including a high degree of morphological similarities. This has an impact on the process of identifying GMB clones through visual observation by experts who are subject to mistakes. In this study, ResNet101 architecture was used to create an identification scheme for 11 tea clones from the GMB series (GMB-1 to GMB-11). System evaluation will be carried out by comparing seven different optimizer; Adam, SGD, RMSProp, AdaGrad, AdaMax, AdaDelta, and Nadam. The test results indicate that Adam and SGD have the highest average accuracy, recall, and f1-score values. Adam also has an accuracy value that has remained consistent since the first iteration. The proposed method provides highest precision, recall, F1-score of 96% and accuracy of 97%.

Keywords: tea leaves classification, GMB series, CNN, ResNet101


Kata Kunci


Klasifikasi Daun Teh; Seri Gambung (GMB); Convolutional Neural Network (CNN); ResNet101

Teks Lengkap:

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Referensi


Basorudin, M., Rizqi, A., & Murdaningrum, S., & Maharani, W. (2019). Kajian Persebaran Komoditas Teh: Pengembangan Kawasan Perkebunan Teh Di Provinsi Jawa Barat Tahun 2015. Jurnal Sosial Ekonomi Pertanian, 15(3), 205. https://doi.org/10.20956/jsep.v15i3.6792

Chen, Y., & Chen, X. (2019). Tea Leaves Identification Based on Gray-Level Co-Occurrence Matrix and K-Nearest Neighbors Algorithm. AIP Conference Proceedings, (pp. 2073).

Gulli, A., Kapoor, A., & Pal, S. (2019). Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and More with TensorFlow 2 and the Keras API (2nd ed.). Birmingham, UK: Packt Publishing Ltd.

Haq, M. S., & Karyudi. (2013). Upaya Peningkatan Produksi Teh (Camelia Sinesi (L.) O. Kuntze) Melalui Penerapan Kultur Teknis. Warta PPTK, 24(1), 71–84.

Hariyani, Y. S., Hadiyoso, S., & Siadari, T. S. (2020). Deteksi Penyakit Covid-19 Berdasarkan Citra X-Ray Menggunakan Deep Residual Network. Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 8(2), 443–453.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition Kaiming. IEEE Conference on Computer Vision and Pattern Recognition, (pp. 770–778). https://doi.org/10.1002/chin.200650130

Ibrahim, N., Fu’adah, Y. N., Pratiwi, N. C., Rizal, S., & Usman, K. (2020). Computer Aided System for Gambung Tea Identification using Convolutional Neural Network. IOP Conference Series: Materials Science and Engineering, 982(1), 0–10. https://doi.org/10.1088/1757-899X/982/1/012001

Indarti, D. (2015). Outlook Teh Komoditas Pertanian Subsektor Perkebunan. In Sekretariat Jenderal Kementrian Pertanian, Pusat Data dan Sistem Informasi Pertanian (Vol. 1). Retrieved from http://epublikasi.setjen.pertanian.go.id/epublikasi/outlook/2015/Perkebunan/Outlook Teh 2015/files/assets/common/ downloads/Outlook Teh 2015.pdf

Indolia, S., Kumar, A., Mishra, S. P., & Asopa, P. (2018). ScienceDirect Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach. Procedia Computer Science, 132, 679–688. https://doi.org/10.1016/j.procs.2018.05.069

Lu, H., Jiang, W., Ghiassi, M., Lee, S., & Nitin, M. (2012). Classification of Camellia ( Theaceae ) Species Using Leaf Architecture Variations and Pattern Recognition Techniques. PloS One, 7(1). https://doi.org/10.1371/journal.pone.0029704

Manliguez, C. (2016). Generalized Confusion Matrix for Multiple Classes. (November), 2–4. https://doi.org/10.13140/RG.2.2.31150.51523

Ramdan, A., Suryawati, E., Kusumo, B. S., Pardedea, H. F., Mahendra, O., Dahlan, R., Syahrian, H. (2019). Deep CNNBased Detection for Tea Clone Identification. Jurnal Elektronika Dan Telekomunikasi (JET), 19(2), 45–50. https://doi.org/10.14203/jet.v19.45-50

Ramdan, A., Zilvan, V., Suryawati, E., Pardede, H. F., & Rahadi, V. P. (2020). Tea clone classification using deep CNN with residual and densely connections. Jurnal Teknologi Dan Sistem Komputer, 8(4), 289–296. https://doi.org/10.14710/jtsiskom.2020.13768

Rizal, S., Pratiwi, N. K. C., Ibrahim, N., Vidya, H., Saidah, S., & Fuadah, Y. N. (2020). Tea Leaves GMB Series Classification Using Convolutional Neural Network. Journal of Electrical and System Control Engineering, 3(2), 0–5.

Soydaner, D. (2020). A Comparison of Optimization Algorithms for Deep Learning. International Journal of Pattern Recognition and Artificial Intelligence, 34(13). https://doi.org/10.1142/S0218001420520138

Vilasini, M., & Ramamoorthy, P. (2020). CNN Approaches for Classification of Indian Leaf Species Using Smartphones. Computers, Materials & Continua, 62(3), 1445–1472. https://doi.org/10.32604/cmc.2020.08857

Wu, H., Xin, M., Fang, W., Hu, H. M., & Hu, Z. (2019). Multi-Level Feature Network with Multi-Loss for Person Re-Identification. IEEE Access, 7, 91052–91062. https://doi.org/10.1109/ACCESS.2019.2927052

Zhang, K., Sun, M., Han, T. X., Yuan, X., Guo, L., & Liu, T. (2018). Residual Networks of Residual Networks: Multilevel Residual Networks. IEEE Transactions on Circuits and Systems for Video Technology, 14(8), 1303–1314. https://doi.org/10.1109/TCSVT.2017.2654543




DOI: https://doi.org/10.26760/elkomika.v9i4.841

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