Email this article Solusi Virtual Try-On Kacamata Berbasis AI dengan Integrasi Model Deep Learning untuk E-Commerce Fashion
Abstract
Abstrak :
Banyak pengguna menghadapi kesulitan dalam memilih kacamata secara daring karena tidak dapat memastikan apakah model tertentu sesuai dengan bentuk wajah mereka. Masalah ini sering menimbulkan ketidakpuasan pelanggan dan tingginya tingkat pengembalian produk. Penelitian ini bertujuan untuk mengembangkan solusi Virtual Try-On kacamata berbasis kecerdasan buatan (AI), yang mengintegrasikan model deep learning untuk menciptakan pengalaman belanja daring yang lebih interaktif dan personal. Sistem bekerja dengan mendeteksi bentuk wajah dari foto yang diunggah pengguna menggunakan model Face Shape Detection yang telah dilatih dan mencapai akurasi hingga 89% kemudian memberikan rekomendasi kacamata yang paling cocok berdasarkan sistem rekomendasi Rule-Based. Setelah pengguna memilih salah satu produk dari daftar tersebut, sistem memanfaatkan AI Nano Banana untuk menggabungkan citra wajah dan produk kacamata secara realistis. Teknologi utama yang digunakan meliputi EfficientNetB2 sebagai model CNN utama, InsightFace untuk deteksi wajah presisi tinggi, dan AdamW sebagai algoritma optimasi. Hasil pengujian menunjukkan bahwa sistem ini efektif dalam menghasilkan visualisasi try-on yang akurat dan memuaskan, serta berpotensi meningkatkan konversi penjualan di platform e-commerce fashion.
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Abstract :
Many users experience difficulty in selecting eyeglasses online due to the inability to determine whether a particular model suits their facial shape. This issue often results in customer dissatisfaction and high product return rates. This study aims to develop an AI-based virtual try-on solution for eyeglasses by integrating deep learning models to create a more interactive and personalized online shopping experience. The system functions by detecting the user’s face shape from an uploaded photo using a pre-trained Face Shape Detection model that achieves an accuracy of up to 89%, followed by a rule-based recommendation system that suggests the most suitable eyeglass frames. Once the user selects a product from the recommended list, the system utilizes AI Nano Banana to realistically generate a composite image of the user's face wearing the selected eyeglasses. The core technologies implemented include EfficientNetB2 as the primary CNN model for visual feature extraction, InsightFace for high-precision face detection, and AdamW as the optimization algorithm. Experimental results demonstrate that the system effectively generates accurate and realistic try-on visualizations, which are not only satisfactory to users but also have the potential to increase sales conversion rates in fashion e-commerce platforms.
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