PROSES EKSTRAKSI DAN KLASIFIKASI CITRA EMOSI MENGGUNAKAN METODE PCA DAN CNN

Authors

  • Resty Wulanningrum Prodi Teknik Informatika Universitas Nusantara PGRI Kediri
  • Anggi Nur Fadzila Prodi Teknik Informatika Universitas Nusantara PGRI Kediri
  • Danar Putra Pamungkas Prodi Teknik Informatika Universitas Nusantara PGRI Kediri

DOI:

https://doi.org/10.30736/jti.v6i2.664

Keywords:

Artificial Intelegent

Abstract

Manusia secara alami menggunakan ekspresi wajah untuk berkomunikasi dan menunjukan emosi mereka dalam berinteraksi sosial. Ekspresi wajah termasuk kedalam komunikasi non-verbal yang dapat menyampaikan keadaan emosi seseorang kepada orang yang telah mengamatinya. Penelitian ini menggunakan metode Principal Component Analysis (PCA) untuk proses ekstraksi ciri pada citra ekspresi dan metode Convolutional Neural Network (CNN) sebagai prosesi klasifikasi emosi, dengan menggunakan data Facial Expression Recognition-2013 (FER-2013) dilakukan proses training dan testing untuk menghasilkan nilai akurasi dan pengenalan emosi wajah. Hasil pengujian akhir mendapatkan nilai akurasi pada metode PCA sebesar 59,375% dan nilai akurasi pada pengujian metode CNN sebesar 59,386%.

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References

Utami, E., & Wulanningrum, R. (2014). Penggunaan Principal Component Analysis dan Euclidean Distance untuk Identifikasi Citra Tanda Tangan. JURNAL IPTEKKOM (Jurnal Ilmu Pengetahuan & Teknologi Informasi), 16(1), 1-16.

Nugroho Adi Pulung, Fenriana Indah, Arijanto,M.Kom, Rudi “Implementasi Deep Learning Menggunakan Convolutional Neural Network(CNN) pada Ekspresi Manusiaâ€, April 2020. JURNAL ALGOR - VOL. 2 NO. 1

Yusuf Achmad, Wihandika Cahya Randy, Dewi Candra “Klasifikasi Emosi Berdasarkan Ciri Wajah Menggunakan Convolutional Neural Network, November 2019.

Hariri Rohman Fajar, Putra Pamungkas Danar, “Implementasi Metode PCA dan City Block Distance untuk Presensi Mahasiswa Berbasis Wajahâ€, Seminar Nasional Teknologi Informasi, Komunikasi dan Aplikasinya Volume 04, Tahun 2017

Nasution Zulfahmi Muhammad, Nababan Addillah Adli, Syaliman Umam Khairul, Novelan Syahputra Muhammad, Jannah Miftahul “Penerapan Principal Component Analysis (PCA) Dalam Penentuan Faktor Dominan Yang Mempengaruhi Kanker Serviks (Studi kasus: Cervical Cancer dataset)†Jurnal Mantik Penusa Vol. 3, No. 1, Juni 2019

Johnson, W.A. & Wichern, D.W. 2007. Applied Multivariate Statistical Analysis. 6th Edition. Pearson Prentice Hall: New Jersey.

E. P Suartika I Wayan, Wijaya Yudhi Arya, Soelaiman Rully, “Klasifikasi Citra Menggunakan Convolutional Neural Network pada Caltechâ€. Jurnal Teknik ITS Vol. 5, No. 1 Maret 2016

Alamsyah Derry, Pratama Dicky “Implementasi Convolutional Neural Networks (CNN) Untuk Klasifikasi Ekspresi Citra Wajah Pada FER-2013 Dataset†(Jurnal Teknologi Informasi) Vol.4, No.2, Desember 2020.

Shafira Tiara “Implementasi CNN untuk Klasifikasi Citra Tomat Menggunakan Kerasâ€. Maret 2018.

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Published

2021-09-13

How to Cite

Wulanningrum, R., Fadzila, A. N., & Pamungkas, D. P. (2021). PROSES EKSTRAKSI DAN KLASIFIKASI CITRA EMOSI MENGGUNAKAN METODE PCA DAN CNN. Joutica, 6(2), 484–491. https://doi.org/10.30736/jti.v6i2.664

Issue

Vol. 6 No. 2 (2021)

Section

Jouticla Jurnal Teknik Informatika

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