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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JUSIFO : Jurnal Sistem Informasi JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) InComTech: Jurnal Telekomunikasi dan Komputer JRST (Jurnal Riset Sains dan Teknologi) JURIKOM (Jurnal Riset Komputer) Media Informatika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Ilmiah Teknologi Informasi dan Robotika Journal of Telecommunication, Electronics and Control Engineering (JTECE) Decode: Jurnal Pendidikan Teknologi Informasi eProceedings of Engineering semanTIK
Iqsyahiro Kresna A
Telkom University
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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CLOTHING RECOMMENDATION AND FACE SWAP MODEL BASED ON VGG16, AUTOENCODER, AND FACIAL LANDMARK POINTS Ramadhanti, Imada; Prasetiadi, Agi; Kresna A, Iqsyahiro
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1016

Abstract

The selection of clothes in e-commerce sometimes contains doubts about the clothes that consumers choose because the clothes are not yet known to suit the consumer's body. So this research provides a solution through a clothing recommendation model according to the size and concept of clothing. Furthermore, there is a face exchange model whose job is to exchange faces between the consumer's face and the face on the recommended clothing. The dataset used in the classification model is clothing that is put into 8 classes with variations in size, clothing concept, and veiled or without headscarves, while making the autoencoder model requires source and target face datasets of 3,000 faces each. The method used to make clothing model recommendations is VGG16 and the face exchange model uses the autoencoder and facial landmark points methods. The results of the classification model with 2 different architectures obtain an accuracy of 97.01% and 94.49% respectively. Then the results of the autoencoder models for the 12 models produced the lowest loss values ​​with autoencoder I of 0.00012951 and in autoencoder II of 8.01e-05. The face landmark point method is used if the autoencoder method does not produce a good face swap.
Co-Authors Aditya Wijayanto Agatha, Fasha Agi Prasetiadi Alip Adhani Anggi Zafia arif amrullah Aulia Desy Nur Utomo Aulia, Aisyah Hasna Ayu Andini, Oktavia Bayu Setyaji Bita Parga Zen Cahyo Prihantoro Diandra Chika Fransisca Dzunuri Elvira Yaniar, Amanda Imada Ramadhanti Julian Saputra Julian Saputra Junitasari, Refi Larasae, Dewi Makhdum Wahid, Muhammad Maryona Septiara Mawar Auliya Insani Ma’rifah, Triana Umi Mega Pranata Muhamad Azrino Gustalika Muhammad Aqilla Farrel Hussain Naufal Wardhana, Sandya Novian Adi Prasetyo Pradana Ananda Raharja Putri Agustyaningsih Ramadhanti, Imada Senegal, Frantino Setiawan, Aditya Setiawan, Shodik Siagian, Claudia Angelita Trihastuti Yuniati Tsaqif Arkan, Naufal Utami, Sukhaenah Tri Vindi Eka Safitri Visalva Mega Melinia, Vinna Wahyu Adiningsih, Laras Wibowo, Fahrudin Mukti Wildan Nugroho, Muhhamad Yankotinu Al Qod'r Jonnata Yoso Adi Setyoko Zahrah Charli Putri, Kemala