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      "abstract": "Perkembangan pesat industri perawatan kulit telah menyebabkan\r\nmembanjirnya variasi produk di pasaran yang berdampak pada kebingungan\r\nkonsumen dalam memilih produk yang sesuai preferensinya. Fenomena kelebihan\r\ninformasi ini menyulitkan pengguna untuk menemukan produk alternatif yang\r\nmemiliki kemiripan karakteristik, baik dari segi kandungan maupun kemasan,\r\nterutama pada produk baru yang belum memiliki riwayat penilaian dari pengguna\r\nlain. Penelitian ini bertujuan menyelesaikan masalah tersebut dengan membangun\r\nsistem rekomendasi menggunakan pendekatan Hybrid Multimodal Content Based\r\nFiltering. Metode ini mengintegrasikan ekstraksi fitur visual dari citra kemasan\r\nproduk menggunakan algoritma Convolutional Neural Network dengan arsitektur\r\nResNet dan ekstraksi fitur tekstual dari komposisi bahan menggunakan metode\r\npembobotan Term Frequency Inverse Document Frequency. Kedua fitur tersebut\r\ndigabungkan untuk menghitung tingkat kemiripan antar produk menggunakan\r\nalgoritma Cosine Similarity. Hasil akhir dari penelitian ini adalah sebuah sistem\r\nyang mampu memberikan rekomendasi produk perawatan kulit yang relevan\r\nberdasarkan kemiripan visual dan kandungan bahan secara bersamaan. Evaluasi\r\nkinerja sistem dilakukan menggunakan metrik presisi kategori dan validasi klaster\r\nuntuk memastikan kualitas pengelompokan data tanpa label. Penelitian ini\r\nmemberikan kontribusi nyata bagi konsumen dalam mempermudah pencarian\r\nproduk yang akurat serta membantu mengatasi kendala rekomendasi pada produk\r\nbaru yang belum memiliki interaksi pengguna sebelumnya.",
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      "date": "2026-05-19",
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      "keywords": "Sistem Rekomendasi, Skincare, Hybrid Multimodal, TF-IDF,\r\nResNet50, Recommendation System.",
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