Klasifikasi Kulit Wajah untuk Rekomendasi Produk Skincare Menggunakan Convolutional Neural Network (CNN)
DOI:
https://doi.org/10.35870/emt.v10i3.6321Keywords:
Facial Skin Classification, Skincare, Convolutional Neural Network (CNN), Resnet50, Recommendation System, Deep Learning, Community ServiceAbstract
This study utilizes the ResNet50 architecture with transfer learning techniques. The dataset consists of 5,200 facial images categorized into five classes: normal, dry, oily, combination, and sensitive. Data was split with an 80:10:10 ratio for training, validation, and testing. Data augmentation was applied to increase dataset variety. The recommendation system was developed using a content-based filtering approach with rule-based mapping between classification results and product attributes. The ResNet50 model achieved a classification accuracy of 90.4% on test data, with the highest F1-score for the oily class (94.7%) and the lowest for the sensitive class (86.9%). The recommendation system produced a Mean Reciprocal Rank (MRR) of 0.82 and precision@3 of 0.76. User satisfaction testing with 50 participants showed an 84% satisfaction rate. CNN with the ResNet50 architecture is effective for facial skin type classification with high accuracy. The integration of the classification system with content-based recommendation mechanisms successfully provides relevant skincare product recommendations. This system has the potential to become a digital tool that can enhance public skin health literacy and reduce errors in skincare product selection.
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Copyright (c) 2026 Sri Lestari, Mesra Betty Yel, Ahlan Nur Fallah, Giraldi Freddy Simanungkalit, M. Ilyan Fadiliah, M. Dicky Adicandra, Dadang Iskandar Mulyana, Sutisna Sutisna
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