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Design of Facial Skin Type Detection Application Using CNN with Inceptionv3 Model and Google Cloud Platform Nur; Ade; Ahmad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): Juli
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/v171yr28

Abstract

The advancement of Artificial Intelligence (AI) and Computer Vision technologies has significantly impacted the beauty industry, particularly in facial skin type detection. This study developed a mobile application that utilizes CNN with the InceptionV3 architecture deployed on the Google Cloud Platform (GCP). The system uses a dataset of 1,735 facial images categorized into normal, dry, oily, and acne-prone skin types. The photos were preprocessed and augmented before being processed by the CNN model. Firestore and Cloud Storage were used to maintain the data, while Cloud Run was used to publish the trained model into a Flask-based API. The accuracy, precision, recall, and F1-score reached 91.7%, 91%, 91%, and 91% respectively. Compared to previous studies, this system offers real-time classification through a lightweight mobile application integrated with cloud computing, aiming to improve accessibility and efficiency in dermatological analysis and personalized skincare services.
Co-Authors ade Ahmad Ajeng Pipit Amang Sudarsono, Amang Anang Budikarso Anang Siswanto Ari Wijayanti Aries Pratiarso Cecep Kusmana Denok Risky Ayu Paramita Donny Farhan Eka Farel Juliansyah Fitri Haniah Mahmudah Hari Wahyuningrat S. Iin Wijayanti Kembong Marcelino, Al Vitto Marcelino Mikhania Christiningtyas Nanang Syahroni Patihul Rahim, ilma Riskha Nurmalasari, Dewi Rosyida Santi Sartini Selvi Titis Purwaningrum Tri Susanti Tribudi Santoso Yoga Yuniadi Yuniati, Fransiska