| Issue |
MATEC Web Conf.
Volume 372, 2022
International Conference on Science and Technology 2022 “Advancing Science and Technology Innovation on Post Pandemic Through Society 5.0” (ICST-2022)
|
|
|---|---|---|
| Article Number | 06002 | |
| Number of page(s) | 5 | |
| Section | Engineering and Product Design | |
| DOI | https://doi.org/10.1051/matecconf/202237206002 | |
| Published online | 08 December 2022 | |
The Design of Facial Expression Detection System to Determine the Level of Customer Satisfaction using K-Nearest Neighbor Method
1 Electrical Engineering Department, Faculty of Engineering, University of Trunojoyo Madura, Bangkalan, Indonesia
2 Electrical Engineering Department, Faculty of Engineering, University of Trunojoyo Madura, Bangkalan, Indonesia
3 Electrical Engineering Department, Faculty of Engineering, University of Trunojoyo Madura, Bangkalan, Indonesia
4 Electrical Engineering Department, Faculty of Engineering, University of Trunojoyo Madura, Bangkalan, Indonesia
5 Electrical Engineering Department, Faculty of Engineering, University of Trunojoyo Madura, Bangkalan, Indonesia
6 Electronic Weapon System Department, Indonesian Army Polytechnic, Batu, Indonesia
7 Electrical Engineering Department, Faculty of Engineering, University of Trunojoyo Madura, Bangkalan, Indonesia
* Corresponding author: diana.rahmawati@trunojoyo.ac.id
Facial expressions are one of the ways humans communicate to convey personal emotions to their communication partner nonverbally. Therefore, human facial expressions can be used for various purposes, one of which is knowing customer satisfaction. So far, customers of Bank Rakyat Indonesia (BRI) provide feedback on service quality using only a polling system, namely by filling out a criticism and suggestion form and then entering it in a suggestion box which is distinguished between satisfied and dissatisfied. However, such a method is less effective because it can be easily manipulated by customers and customers are often indifferent to the feedback. So that the improvement of service quality tends to be less effective. This research will design a system that can recognize human facial expressions to determine the level of customer satisfaction with input data in the form of video data taken by a webcam camera with the viola-jones method to detect faces and determine facial patterns. Then the facial data will be classified using the K-Nearest Neighbor method to determine the type of facial expression. Determination of the value of k will determine the success rate of facial expression detection. The processed data will be displayed on a liquid crystal display (LCD) and then stored in a MySQL database. The results showed that the accuracy of facial expression detection was 80.77% from 52 facial expression data.
Key words: Conventional System / Facial Expression / Customer Satisfaction / Viola-Jones / K-NearestNeighbor
© The Authors, published by EDP Sciences, 2022
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