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Classification Cherry’s Coffee using k-Nearest Neighbor (KNN) and Artificial Neural Network (ANN)
Anita S.a, Albardaa
a Institut Teknologi Bandung, School of Electrical Engineering and Informatics, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.The quality of coffee is determined from 60% when planted, 30% when roasted and 10% when brewed. This research examines more deeply the process of sorting the coffee cherries using the dry method. The technology that is possible to solve this coffee cherry fruit sorting problem is image processing, this is seen because the current conventional method uses human eyes and hands in sorting. This sorting process aims to separate superior fruit (red, half red, broken red, brown)., black, half black, orange, yellow, and green) of inferior fruit (spotted, moldy, with 1 hole, and more than 1 hole) and coffee cherries (round, oval, broken, perfect).The purpose of this study was to develop a coffee cherry sorting machine technology with faster and more accurate results so that it could replace the conventional coffee cherry sorting process. The coffee cherries are categorized into ripe, undercooked, raw, and damaged cherries using the GLCM (Gray-Level Co-Occurrence Matrix) algorithm for feature extraction and the KNN (k-Nearest Neighbor) and ANN (Artificial Neural Network) classification algorithm. newrb. The success obtained from this research is ANN accuracy of 24.41% and using the KNN method of 72.12%. With the simulation carried out, the coffee cherries classification process with an amount of 1, 885 can be carried out in a total time of 356.02 seconds or the equivalent of 6 minutes. Author identifies indicators of coffee cherries as skin color (red, half red, cracked red, brown, black, half black, orange, yellow, and green), cherri shape (round, oval, broken, perfect), and cherries skin defects (speckled -button, moldy, with 1 hole, and more than 1 hole). It is hoped that the results of this study can serve as a consideration for developing a more advanced national coffee industry.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ANN (artificial neural network),Classification algorithm,Classification process,Conventional methods,Gray level co-occurrence matrix,K nearest neighbor (KNN),K-nearest neighbors,Sorting machines[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ANN,Cherry’s coffee,Classification,GLCM,Image processing,KNN,Machine learning,RGB[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Acknowledgment The research is partially supported by Aman Kuba Coffee Aceh Sumatra and Murbeng Puntang Coffee Bandung West Java. This research is conducted in the School of Electrical sEngineering and Informatics, Institut Teknologi Bandung (ITB).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICITSI50517.2020.9264927[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]