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Performa Metode Convolutional Neural Network Pada Face Landmark Untuk Virtual Make Up Try On | Angeline | JURNAL MEDIA INFORMATIKA BUDIDARMA

Performa Metode Convolutional Neural Network Pada Face Landmark Untuk Virtual Make Up Try On

Dameethia Angeline, Erico Jochsen, Dyah Erny Herwindiati, Janson Hendryli

Abstract


Make up or facial makeup, is an activity to change the appearance from its original form with the help of make up materials and tools. Make-up tools are beauty tools that are commonly used by most women to beautify the appearance of their faces with many shade choices. The shade on the make-up tool is the color usually used in make-up. Examples of make-up tools that are most often used include eyeshadow, blush on, and lipstick. These make-up tools are sold widely online and offline in physical stores. However, usually a tester is also needed so that those who want to buy can try the shade that suits them. When buying online, they often find it difficult to choose the right shade, while testers in physical stores are sometimes considered less hygienic because they have been used by many people. The aim of this paper is to measure the performance of the Convolutional Neural Network (CNN) method using the ResNet-50 architecture on facial landmarks for creating virtual make up try ons which can be an alternative to this problem. The facial image data source used is from the Kaggle site called Facial Keypoints Detection. The testing process produces 78.99% accuracy while the training process produces 95.12% accuracy. The evaluation results of this model use Root Mean Squared Error (RMSE) of 2.2577 and Mean Absolute Error (MAE) of 1.5389.


Keywords


Make Up; CNN; Face Landmark Detection; ResNet-50; Virtual Make Up Try On

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References


A. Hermawanto dan M. Anggraini, Globalisasi, Revolusi Digital dan Lokalitas: Dinamika Internasional dan Domestik di Era Borderless World, Yogyakarta: LPPM Press, 2020.

CNBC Indonesia, “Mengenal Apa Itu Internet, Sejarah Perkembangan & Manfaatnya,†21 September 2022. [Online]. Available: https://www.cnbcindonesia.com/tech/20220921131159-37-373856/mengenal-apa-itu-internet-sejarah-perkembangan-manfaatnya. [Diakses 17 Agustus 2023].

B. Damanik, “RANCANGAN SISTEM INFORMASI SMP NEGERI 1 TUHEMBERUA KABUPATEN NIAS UTARA MENGGUNAKAN PHP CODEIGNITER,†https://doi.org/10.51544/jurnalmi.v6i1.1979, vol. 6, no. 1, p. 1, Juni 2021.

F. D. Sari dan N. S. S. Ambarwati, “Pembuatan Video Tutorial Make Up Pada Wajah Yang Memiliki Bekas Luka,†https://doi.org/10.21009/jtr.12.2.06, vol. 12, no. 2, 17 2 2023.

F. Chusna, “Daftar Alat Make Up Lengkap dan Fungsinya (Plus Rekomendasi Produk),†Lottemart, 26 Oktober 2022. [Online]. Available: https://lottemart.co.id/smartalog/inspirasi/daftar-alat-make-up. [Diakses 17 Agustus 2023].

S. P. Ristiawanto, B. Irawan dan C. Setianingsih, “Pengenalan Ekspresi Wajah Berbasis Convolutional Neural Network Menggunakan Arsitektur Residual Network-50,†vol. 8, no. 5, Oktober 2021.

B. Hartanto dan T. Susyanto, “Penerapan Image Recognition Dalam Pengenalan Objek Menggunakan Model ResNet-50,†vol. 2, no. 2, 31 Juli 2023.

V. T. Hoang, S. D. Huang dan K. H. Jo, “3-D Facial Landmarks Detection for Intelligent Video Systems,†DOI 10.1109/TII.2020.2966513, vol. 17, no. 1, 14 Januari 2020.

M. A. Nurdin, R. C. Wihandika dan F. Utaminingrum, “Deteksi Pergerakan Arah Mata menggunakan Convolution Neural Networkberdasarkan Facial Landmark,†vol. 4, no. 10, pp. 3338-3345, Oktober 2020.

I. P. Udayana dan I. K. Supartha, “IMPLEMENTASI KOMBINASI METODE MEAN DENOISING DAN CONVOLUTIONAL NEURAL NETWORK PADA FACIAL LANDMARK DETECTION,†https://doi.org/10.23887/janapati.v10i1.29779, vol. 10, no. 1, Maret 2021.

P. A. Nugroho, I. Fenriana dan R. Arijanto, “IMPLEMENTASI DEEP LEARNING MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK ( CNN ) PADA EKSPRESI MANUSIA,†vol. 2, no. 1, 5 November 2020.

J. C. Koo, Y. C. Hum, K. W. Lai, W. S. Yap dan S. Manickam, “Deep Machine Learning Histopathological Image Analysis for Renal Cancer Detection,†https://doi.org/10.1145/3532213.3532313, p. 657–663, 13 Juli 2022.

G. Boesch, “Deep Residual Networks (ResNet, ResNet50) – 2023 Guide,†viso.ai, [Online]. Available: https://viso.ai/deep-learning/resnet-residual-neural-network/. [Diakses 5 September 2023].

N. L. P. U. Premananda dan N. K. U. K. Dewi, “VALUASI HARGA SAHAM PERUSAHAAN SUBSEKTOR TOURISM, RESTAURANT, DAN HOTEL (STUDI PADA PERUSAHAAN YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2022),†vol. 8, no. 2, 29 September 2023.

A. T. Nurani, A. Setiawan dan B. Susanto, “Perbandingan Kinerja Regresi Decision Tree danRegresi Linear Berganda untuk Prediksi BMI pada Dataset Asthma,†https://doi.org/10.24246/juses.v6i1p34-43, vol. 6, no. 1, pp. 34-43, Februari 2023.

L. Anggraini dan Y. Yamasari, “Klasifikasi Citra Wajah Untuk Rentang Usia Menggunakan Metode Artificial Neural Network,†https://doi.org/10.26740/jinacs.v5n02.p185-192, vol. 5, no. 2, 28 Agustus 2023.

Y. Solawetz, “Mengapa dan Bagaimana Menerapkan Augmentasi Data Rotasi Acak,†roboflow, 24 Juni 2020. [Online]. Available: https://blog.roboflow.com/why-and-how-to-implement-random-rotate-data-augmentation/. [Diakses 26 Oktober 2023].

S. D. P. Bahari dan U. Latifa, “KLASIFIKASI BUAH SEGAR MENGGUNAKAN TEKNIK COMPUTER VISION UNTUK PENDETEKSIAN KUALITAS DAN KESEGARAN BUAH,†https://doi.org/10.36040/jati.v7i3.6871, vol. 7, no. 3, Juni 2023.

L. H. Ganda dan H. Bunyamin, “Penggunaan Augmentasi Data pada Klasifikasi Jenis Kanker Payudara dengan Model Resnet-34,†vol. 3, no. 1, 24 April 2021.

C. Shorte dan T. Khoshgoftaar , “A survey on Image Data Augmentation for Deep Learning,†https://doi.org/10.1186/s40537-019-0197-0, vol. 6, no. 60, 6 Juli 2019.

K. Hamidah dan A. Voutama, “Analisis Faktor Tingkat Kebahagiaan Negara Menggunakan Data World Happiness Report dengan Metode Regresi Linier.,†https://doi.org/10.35891/explorit.v15i1.3874, vol. 15, no. 1, 18 Juni 2023.




DOI: https://doi.org/10.30865/mib.v7i4.6619

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