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All Journal Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) DEVICE
Tri Andre Anu
Universitas Muhammadiyah Sumatera Utara
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mechanical Engineering

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Journal : DEVICE

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KLASIFIKASI FITUR WARNA LEVEL ROASTING BIJI KOPI MENGGUNAKAN ARTIFICIAL NEURAL NETWORK Tri Andre Anu; Rika Rosnelly; Dedi Irawan; Ubaidullah Hasibuan; Progresif Bulolo5
Device Vol 13 No 1 (2023): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v13i1.4094

Abstract

Abstract align="justify"Small and Medium Enterprises (SMEs) are using a manual method to notice the roasting level classification of coffee beans. However, the weaknesses in this technique are that the coffee roaster staff consumes time sorting the roasting level of the coffee beans. As a result, the coffee roaster focuses less because they take too long to sort the coffee beans—consequently, the mixed coffee beans in packages that should be elsewhere. Therefore a system is needed to help coffee roaster officers classify coffee beans using an artificial neural network. The data used are 60 coffee beans with three roasting levels: light roasted, medium roasted, and dark roasted. The classification process consists of a training stage and a testing stage. At the testing stage, using a sample of 30 coffee beans and based on the results of this study, the best results were obtained with a training value of 90%. In contrast, the testing accuracy was 66.67%.
Co-Authors Dedi Irawan Dedi Irawan Progresif Bulolo Progresif Bulolo5 Rika Rosnelly Ubaidullah Hasibuan