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All Journal Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) DEVICE
Ubaidullah Hasibuan
Universitas Potensi Utama
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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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%.
Utilization of Digital Image and Convolution Neural Network Algorithm in Customer Satisfaction Survey with Facial Expressions Tri Andre Anu; Rika Rosnelly; Dedi Irawan; Progresif Bulolo; Ubaidullah Hasibuan
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v4i2.15915

Abstract

The human face provides us with a lot of information about a person, and arguably the two most important pieces of information in a face are a person's identity and their emotional state. Judgments of identity and emotion facilitate social interactions. Services are a crucial part of the activities of all organizations, especially those in the service sector. Good services support customer satisfaction and ultimately impact the progress of the organization. The Convolutional Neural Network algorithm has become the most widely used neural architecture in various tasks, including image classification, audio pattern recognition, machine translation of text, and speech recognition. The data groups (angry, fearful, happy, neutral, sad, and surprised) tested with a threshold value of 30 epochs achieved a loss (error) accuracy of 1.5146 on the test data. The accuracy on the test data is 0.61. The proposed Convolutional Neural Network algorithm and digital image utilization achieved high accuracy performance to assist in evaluating a service-related field.
Co-Authors Dedi Irawan Dedi Irawan Progresif Bulolo Progresif Bulolo5 Rika Rosnelly Tri Andre Anu Tri Andre Anu