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


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

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