TY - GEN
T1 - Deciphering Consumer Skin Care Needs
T2 - 2023 International Conference on Information Technology and Computing, ICITCOM 2023
AU - Nooryawati, Dinda Ockta
AU - Bachtiar, Fitra Abdurrachman
AU - Ridok, Achmad
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This research aims to understand the usage and perception of skincare products, specifically focusing on the facial wash from the Cetaphil brand. A survey revealed that a significant number of women invest in skincare monthly, yet many still lack confidence in their skin's appearance. Despite the plethora of skincare products available, many users still face confusion in choosing the right product, leading to a trial and error approach. This study attempts to map the use of skincare products and identify specific points that can address various skin problems. The research employs sentiment analysis and topic classification to analyze user reviews, aiming to provide insights into user perspectives on skincare products. The sentiment analysis, using the IndoBERT method, achieved an average accuracy of 85% and an F1-score of 93%. Topic classification, on the other hand, achieved an accuracy of 93.49% and an F1-score of 90.28%. The findings suggest that while many users find Cetaphil products beneficial, there are specific skin concerns that the product may not address for everyone. This study provides valuable insights for both consumers and skincare brands, emphasizing the importance of understanding user feedback and tailoring products to meet diverse skincare needs.
AB - This research aims to understand the usage and perception of skincare products, specifically focusing on the facial wash from the Cetaphil brand. A survey revealed that a significant number of women invest in skincare monthly, yet many still lack confidence in their skin's appearance. Despite the plethora of skincare products available, many users still face confusion in choosing the right product, leading to a trial and error approach. This study attempts to map the use of skincare products and identify specific points that can address various skin problems. The research employs sentiment analysis and topic classification to analyze user reviews, aiming to provide insights into user perspectives on skincare products. The sentiment analysis, using the IndoBERT method, achieved an average accuracy of 85% and an F1-score of 93%. Topic classification, on the other hand, achieved an accuracy of 93.49% and an F1-score of 90.28%. The findings suggest that while many users find Cetaphil products beneficial, there are specific skin concerns that the product may not address for everyone. This study provides valuable insights for both consumers and skincare brands, emphasizing the importance of understanding user feedback and tailoring products to meet diverse skincare needs.
KW - IndoBERT
KW - Machine learning
KW - Natural Language Processing
KW - Sentiment Analysis
KW - Skin Care Product Reviews
KW - Topic Classification
UR - https://www.scopus.com/pages/publications/85187234196
U2 - 10.1109/ICITCOM60176.2023.10442129
DO - 10.1109/ICITCOM60176.2023.10442129
M3 - Conference contribution
AN - SCOPUS:85187234196
T3 - Proceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023
SP - 291
EP - 296
BT - Proceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023
A2 - Chen, Hsing-Chung
A2 - Damarjati, Cahya
A2 - Blum, Christian
A2 - Jusman, Yessi
A2 - Kanafiah, Siti Nurul Aqmariah Mohd
A2 - Ejaz, Waleed
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 December 2023 through 2 December 2023
ER -