TRANSFER LEARNING TO PREDICT GENRE BASED ON ANIME POSTERS
Abstract
Anime is an animated film with a distinctive graphic design originating from Japan, which is widely favored by various groups. anime itself has a genre like a movie in general, but there is a slight difference from ordinary films, anime has additional genres that are not in ordinary films, such as the Ecchi, Mahou Shoujou, Seinen, Shounen, and Josei genres. Since those genres only exist in anime, this research is devoted to predicting those anime genres. The prediction will use posters from the anime itself, with the help of image processing, namely the Convolutional Neural Network method and Transfer Learning. Transfer Learning will be implanted as a comparison of the performance of the existing architecture with the architecture that will be created, whether the architecture is able to process the dataset properly. The dataset to be used is a dataset of posters and csv documents containing images and details of the anime, the dataset contains anime data from 1980 to 2021 and contains 11651 anime poster data which has different resolution sizes. The ResNet50 model has the highest accuracy rate of 48% with a loss rate of 36%, while InceptionV3 produces 35% accuracy with 69% loss. At the time of testing ResNet50 gave the smallest genre percentage value of CustomModel and InceptionV3, while CustomModel gave the highest genre value. In addition to the value, all modes also predicted the genre well. Especially InceptionV3 is able to predict the music genre, because the music genre has a very small number of datasets, and this music genre is difficult to predict by the ResNet50 and CustomModel models.
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References
D. F. N. Pratama and A. K. Adim, “Konsep Diri Mahasiswa Otaku Di Kota Bandung (Analisis Terhadap Konsep Diri Yang Dimiliki Oleh Mahasiswa Otaku Yang Ada Dikota Bandung),” Vol. 13, 2022.
I. Fansuri, “Penerapan Algoritma Smith Pada Aplikasi Pencarian Anime Dalam Kumpulan Anime,” Terapan Informatika Nusantara, Vol. 1, No. 6, Pp. 333–338, 2020, [Online]. Available: Https://Ejurnal.Seminar Id.Com/Index.Php/Tin
A. M. Ashari And I. Rochmawati, “Analisis Visual Pada Poster Promosi Film Trilogi Batman,” 2022. [Online]. Available: Https://Ojs.Unikom.Ac.Id/Index.Php/Divagatra
D. Kurniawan, S. F. Seni, D. Desain, S. Desain, And K. Visual, “STUDI SEMIOTIKA KARAKTER MONOKUMA PADA ANIME FRANCHISE SERIES ‘DANGANRONPA,’” 2019. [Online]. Available: Http://Publikasi.Dinus.Ac.Id/Index.Php/Andharupa
D. Alita, Y. Rahmanto, And A. Dwi Putra, “PELATIHAN DESAIN GRAFIS PADA SMKS NURUL HUDA PRINGSEWU,” Journal Of Technology And Social For Community Service (JTSCS), Vol. 3, No. 2, Pp. 337–346, 2022, [Online]. Available: Https://Ejurnal.Teknokrat.Ac.Id/Index.Php/Teknoabdimas
R. S. Hartono And H. Armanto, “Klasifikasi Genre Manga Menggunakan Convolutional Neural Network,” Vol. 8, No. 2, Pp. 573–584, 2021, [Online]. Available: Http://Jurnal.Mdp.Ac.Id
D. Sebastian, “Implementasi Algoritma K-Nearest Neighbor Untuk Melakukan Klasifikasi Produk Dari Beberapa E-Marketplace,” Vol. 5, Pp. 2443–2229, 2019, Doi: 10.28932/Jutisi.V5i1.913.
D. M. Wonohadidjojo, “Perbandingan Convolutional Neural Network Pada Transfer Learning Method Untuk Mengklasifikasikan Sel Darah Putih,” Ultimatics : Jurnal Teknik Informatika, Vol. 13, No. 1, P. 51, 2021.
Sunario Megawan And Wulan Sri Lestari, “Deteksi Spoofing Wajah Menggunakan Faster R-CNN Dengan Arsitektur Resnet50 Pada Video,” Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, Vol. 9, No. 3, Pp. 261–267, Aug. 2020, Doi: 10.22146/.V9i3.231.
F. Royana, P. Yuniar Maulida, R. Nurul Hasanah, And S. Setia Rahayu, “Aplikasi Mobile Deteksi Dini Kanker Kulit Berdasarkan Image Processing | 100,” 2021. [Online]. Available: Http://Journal.Pwmjateng.Com/Index.Php/Jle
A. Michael, “Journal Dynamic Saint Komparasi Kombinasi Pre-Trained Model Dengan SVM Pada Klasifikasi Kematangan Kopi Berbasis Citra,” Vol. 7, No. 1, 2022, Doi: 10.47178/Dynamicsaint.V5xx.Xxxx.
S. Ramaneswaran, K. Srinivasan, P. M. D. R. Vincent, And C. Y. Chang, “Hybrid Inception V3 Xgboost Model For Acute Lymphoblastic Leukemia Classification,” Computational And Mathematical Methods In Medicine, Vol. 2021. Hindawi Limited, 2021. Doi: 10.1155/2021/2577375.
C. W. T. Koh, J. S. G. Ooi, G. L. C. Joly, And K. R. Chan, “Gene Updater: A Web Tool That Autocorrects And Updates For Excel Misidentified Gene Names,” Sci Rep, Vol. 12, No. 1, Dec. 2022, Doi: 10.1038/S41598-022-17104-3.
A. Aboah, M. Boeding, And Y. Adu-Gyamfi, “Mobile Sensing For Multipurpose Applications In Transportation,” 2021.
S. Lestari And S. Saepudin, “ANALISIS SENTIMEN VAKSIN SINOVAC PADA TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES,” 2021. [Online]. Available: Https://Vaksin.Kemkes.Go.Id/
A. Peryanto, A. Yudhana, And R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network Dan K Fold Cross Validation,” 2020. [Online]. Available: Http://Jurnal.Polibatam.Ac.Id/Index.Php/JAIC
N. D. Miranda, L. Novamizanti, And S. Rizal, “CONVOLUTIONAL NEURAL NETWORK PADA KLASIFIKASI SIDIK JARI MENGGUNAKAN RESNET-50,” Jurnal Teknik Informatika (Jutif), Vol. 1, No. 2, Pp. 61–68, Dec. 2020, Doi: 10.20884/1.Jutif.2020.1.2.18.
F. Nurona Cahya Et Al., “SISTEMASI: Jurnal Sistem Informasi Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network ( CNN),” 2021. [Online]. Available: Http://Sistemasi.Ftik.Unisi.Ac.Id
Rizki Putra Pamungkas, Dwina Kuswardani, And Riki Ruli A Siregar, “Penerapan Algoritma Backpropagation Pada Pengenalan Tanda Nomor Kendaraan Bermotor Untuk Kartu Parkir Berbasis RFID,” 2020.
M. Hasbi Ashshiddieqy And A. Rizal, “Klasifikasi Suara Paru Dengan Convolutional Neural Network (CNN),” 2020.
M. V. Al Hasri And E. Sudarmilah, “Sistem Informasi Pelayanan Administrasi Kependudukan Berbasis Website Kelurahan Banaran,” MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, Vol. 20, No. 2, Pp. 249–260, May 2021, Doi: 10.30812/Matrik.V20i2.1056.
M. Agarina And A. Suryadi Karim, “Seminar Nasional Hasil Penelitian Dan Pengabdian 2019 IBI DARMAJAYA Bandar Lampung,” 2019.
R. Pratama, A. Fuad Assagaf, And F. Tempola, “DETEKSI KEMATANGAN BUAH TOMAT BERDASARKAN FITUR WARNA MENGGUNAKAN METODE TRANSFORMASI RUANG WARNA HIS,” Jurnal Informatika Dan Komputer) P-ISSN, Vol. 2, No. 2, Pp. 2355–7699, 2019, Doi: 10.33387/Jiko.
D. Agusti And A. A. Nababan, “Penerapan Metode Harmonic Mean Filter Dalam Mereduksi Gaussian Noise Pada Citra Digital,” Jurnal Nasional Komputasi Dan Teknologi Informasi, Vol. 5, No. 3, 2022.
N. D. Miranda, L. Novamizanti, And S. Rizal, “CONVOLUTIONAL NEURAL NETWORK PADA KLASIFIKASI SIDIK JARI MENGGUNAKAN RESNET-50,” Jurnal Teknik Informatika (Jutif), Vol. 1, No. 2, Pp. 61–68, Dec. 2020, Doi: 10.20884/1.Jutif.2020.1.2.18.
B. Yanto Et Al., “Klasifikasi Tekstur Kematangan Buah Jeruk Manis Berdasarkan Tingkat Kecerahan Warna Dengan Metode Deep Learning Convolutional Neural Network,” Vol. 6, No. 2, P. 2021, 2021.
C. M. Lengkong, R. Sengkey, And A. Sugiarso, “Sistem Informasi Pariwisata Berbasis Web Di Kabupaten Minahasa,” Jurnal Teknik Informatika, Vol. 14, No. 1, 2019.
P. Septiana Rizky, R. Haiban Hirzi, U. Hidayaturrohman, U. Hamzanwadi Selong Jl TGKH Muhammad Zainuddin Abdul Madjid Pancor, And L. Timur, “Perbandingan Metode Lightgbm Dan Xgboost Dalam Menangani Data Dengan Kelas Tidak Seimbang,” 2022. [Online]. Available: Www.Unipasby.Ac.Id
K. De Angeli Et Al., “Class Imbalance In Out-Of-Distribution Datasets: Improving The Robustness Of The Textcnn For The Classification Of Rare Cancer Types,” J Biomed Inform, Vol. 125, Jan. 2022, Doi: 10.1016/J.Jbi.2021.103957.
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