Utilization of Digital Image and Convolution Neural Network Algorithm in Customer Satisfaction Survey with Facial Expressions

Rika, Rosnelly

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DC Field	Value	Language
dc.contributor.author	Rika, Rosnelly	-
dc.date.accessioned	2023-09-22T07:52:01Z	-
dc.date.available	2023-09-22T07:52:01Z	-
dc.date.issued	2023-08-30	-
dc.identifier.uri	http://repository.potensi-utama.ac.id/jspui/jspui/handle/123456789/5718	-
dc.description	The human face provides us with a lot of information about someone, and it can be said that the two most important pieces of information in a face are identity and emotional state. Assessing identity and emotions facilitates social interactions (Noyes et al., 2021). Facial expression recognition has become important with the advancement of technology in computers, mobile phones, robots, and so on. This advancement has made human-technology interactions increasingly unavoidable. By using facial expressions, systems can be developed to recognize customer satisfaction, among other things (Alamsyah et al., 2020). Customer service is a crucial part of all organizations, especially those in the service sector, and good service supports customer satisfaction, ultimately leading to the progress of the respective organization. Consumer satisfaction as an implication of service maximization has been extensively researched both in service and non-service companies (Badar et al., 2021). Segmentation refers to partitioning an image into several parts based on similarity of characteristics or uniformity. Its usefulness is particularly significant in image analysis and digital image processing applications (Anwar et al., 2021). Digital Image Processing is a discipline that studies techniques for processing images, which can be images or videos (Anwar et al., 2021) Segmentation refers to partitioning an image into several parts based on similarity of characteristics or uniformity. Its usefulness is particularly significant in image analysis and digital image processing applications (Anwar et al., 2021). Digital Image Processing is a discipline that studies techniques for processing images, which can be images or videos (Fakultas et al., 2020) Classification is one of the techniques in data mining that involves the concept of neural networks, where the artificial neural network, often referred to as Convolutional Neural Network (CNN), undergoes validation processes with training and test data. The process of neural networks involves classification with guidance (Sari Hutagalung et al., 2023). Convolutional Neural Network (CNN) has become the most widely used neural architecture in various tasks, including image classification, audio pattern recognition, machine translation of texts, and speech recognition (Zhu et al., n.d.) Based on human knowledge in distinguishing facial expressions, this research will explore the utilization of digital image of human facial expressions with Convolutional Neural Network Algorithm in a survey of customer satisfaction towards a service.	en_US
dc.description.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	en_US
dc.publisher	Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)	en_US
dc.relation.ispartofseries	Vol. 4, No. 2, September 2020;pp. 420~427	-
dc.subject	Digital Image; Convolutional Neural Network; Customer Satisfaction; Facial Expression.	en_US
dc.title	Utilization of Digital Image and Convolution Neural Network Algorithm in Customer Satisfaction Survey with Facial Expressions	en_US
dc.type	Other	en_US
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