Implementasi Metode Simple Additive Weighting dan Machine Learning Untuk Rekomendasi Produk Skin Care Berbasis Android
DOI:
https://doi.org/10.30865/mib.v4i4.2389Keywords:
Machine Learning, Simple Additive Weighting, K-Nearest Neighbor, Skincare Product, Android ApplicationAbstract
Skin health, especially facial skin, is important because the face is the main attraction seen by others. This is closely related to the use of skincare products or skin care products that are used daily. Before determining which skincare product to use, it is very important to know the conditions and problems of facial skin. To make it easier to find out skin conditions and problems, researchers created an android-based application called "Hi Beautiful" using the Machine Learning and Simple Additive Weighting methods. In the application, Machine Learning plays a role in providing information on skin problems based on the results of feature extraction using the Gray Level Co-Occurrence Matrix (GLCM) method whose feature extraction results will be classified by the K-Nearest Neighbor method through inputting images on facial skin using the cellphone camera feature. Meanwhile, the simple additive weighting method is used to provide recommendations for skincare products based on the criteria for skin problems, skin types, age and product price ranges to be recommended. The implementation of the Hi Beautiful application is made using the open source Android Studio application. The results of tests carried out on the Hi Beautiful application include information on skin problems and recommendations for skincare products in the form of cleanser, toner, serum and moisturizer.
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