BAB V PENUTUP
5.2 Saran
5.2 Saran
Rekomendasi yang dapat penulis berikan terkait pengembangan dan penggunaan aplikasi ini untuk penelitian yang akan datang sebagai berikut. 1. Melakukan pengembangan sistem dengan mengintegrasikan antara face
recognition, iris recognition dan integrasi dengan fingerprint atau sistem biometrik lain, untuk mencapai level keamanan sistem yang lebih optimal. 2. Untuk memperoleh kinerja sistem yang lebih baik, dapat mengintegrasikan
dengan algoritma lain dalam menunjang proses face recognition untuk memperoleh kecepatan penangkapan, dan keakuratan hasil pengenalan.
DAFTAR PUSTAKA
Abhirawan, H., Jondri, & Arifianto, A. (2017). Pengenalan Wajah Menggunakan Convolutional Neural Networks (CNN). Universitas Telkom, 4(3), 4907– 4916.
Angeline, R., Kavithvajen, K., Balaji, T., Saji, M., & Sushmitha, S. R. (2019). CNN integrated with HOG for efficient face recognition. International Journal of Recent Technology and Engineering, 7(6), 1657–1661.
Athale, S. S., Patil, D., Deshpande, P., & Dandawate, Y. H. (2015). Hardware Implementation of Palm Vein Biometric Modality for Access Control in Multilayered Security System. Procedia Computer Science, 58, 492–498. https://doi.org/10.1016/j.procs.2015.08.013
Bah, S. M., & Ming, F. (2019). An improved face recognition algorithm and its application in attendance management system. Array, 5(November 2019), 100014. https://doi.org/10.1016/j.array.2019.100014
Coskun, M., Ucar, A., Yildirim, O., & Demir, Y. (2017). Face recognition based on convolutional neural network. Proceedings of the International Conference on Modern Electrical and Energy Systems, MEES 2017, 2018-Janua(November), 376–379. https://doi.org/10.1109/MEES.2017.8248937
Dhomne, A., Kumar, R., & Bhan, V. (2018). Gender Recognition Through Face Using Deep Learning. Procedia Computer Science, 132, 2–10. https://doi.org/10.1016/j.procs.2018.05.053
Eka Putra, W. S. (2016). Klasifikasi Citra Menggunakan Convolutional Neural
Network (CNN) pada Caltech 101. Jurnal Teknik ITS, 5(1).
https://doi.org/10.12962/j23373539.v5i1.15696
Endrianti, F., Setiawan, W., & Wihardi, Y. (2018). Sistem Pencatatan Kehadiran Otomatis di Ruang Kelas Berbasis Pengenalan Wajah Menggunakan Metode Convolutional Neural Network ( CNN ). JATIKOM - Jurnal Aplikasi Dan Teori Ilmu Komputer, 1(1), 40–44.
Fahmi, R. (2018). TensorFlow.js Tutorial.
https://medium.com/@rizafahmi22/screencast-singkat-tentang-tensorflow-js-7e7c3aa6506e
Feng, X., Jiang, Y., Yang, X., Du, M., & Li, X. (2019). Computer vision algorithms and hardware implementations : A survey. Integration, the VLSI Journal, 69(August), 309–320. https://doi.org/10.1016/j.vlsi.2019.07.005
Fitriati, D. (2016). Perbandingan Kinerja CNN LeNet 5 Dan Extreme Learning Machine Pada Pengenalan Citra Tulisan Tangan Angka. Jurnal Teknologi Terpadu, 2(1), 10–16.
Gangopadhyay, I. (2018). Face Detection and Recognition Using Haar Classifier and Lbp Histogram. International Journal of Advanced Research in Computer Science, 9(2), 592–598. https://doi.org/10.26483/ijarcs.v9i2.5815
Gaouar, L., Benamar, A., & Le, O. (2018). ScienceDirect HCIDL : Human-computer interface description language for multi-target , multimodal , plastic user interfaces. 3, 110–130. https://doi.org/10.1016/j.fcij.2018.02.001
Gibert, D., Mateu, C., & Planes, J. (2020). Journal of Network and Computer Applications The rise of machine learning for detection and classification of malware : Research developments , trends and challenges. Journal of Network
and Computer Applications, 153(January), 102526.
Gorunescu, F. (2011). Data Mining (1st ed.). Springer-Verlag Berlin Heidelberg. https://www.springer.com/gp/book/9783642197208#aboutBook
Hendri, Z., & Sujana, A. P. (2018). Sistem Pengenalan Wajah Menggunakan Metode Eigenface Berbasis Raspberry Pi. 49–57.
Juneja, K. (2017). MPMFFT based DCA-DBT integrated probabilistic model for face expression classification. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2017.10.006 Khotimah, B. K., Sari R, E. M., & Yulianarta, H. (2010). Kinerja metode extreme
learning machine (elm) pada sistem peramalan *. Jurnal Simantec, 1(3), 186– 191.
Lamani, H. J., Wowor, H., Rumagit, A., & Tuturoong, N. (2012). Implementasi Metode Asynchronous Javascript and Xml (Ajax) Pada Pembuatan Website Universitas Sam Ratulangi. E-Journal Teknik Elektro Dan Komputer, 1(1), 1– 6.
Lampropoulos, G., Keramopoulos, E., & Diamantaras, K. (2020). Visual Informatics Enhancing the functionality of augmented reality using deep learning , semantic web and knowledge graphs : A review. Visual Informatics, xxxx, 1–11. https://doi.org/10.1016/j.visinf.2020.01.001
Mazur, M. (2015). A Step By Step Backpropagation Example. Artifical Intelegence. https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ Nalepa, J., Antoniak, M., Myller, M., Ribalta, P., & Marcinkiewicz, M. (2020).
Microprocessors and Microsystems Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation. 73. https://doi.org/10.1016/j.micpro.2020.102994
O’Shea, K., & Nash, R. (2015). An Introduction to Convolutional Neural Networks. 1–11. http://arxiv.org/abs/1511.08458
Online, T., Widiasari, C., St, S., Insani, P., Diono, M., & St, S. (2019). Jurnal Politeknik Caltex Riau Sistem Monitoring Tangki dan Penghitung RunHour Genset Otomatis Berbasis Internet of Things ( IoT ). 5(2), 59–70.
Phan-Xuan, H., Le-Tien, T., & Nguyen-Tan, S. (2019). FPGA Platform applied for facial expression recognition system using convolutional neural networks.
Procedia Computer Science, 151(2018), 651–658.
https://doi.org/10.1016/j.procs.2019.04.087
Putro, M Dwisnanto, dkk. (2012). Sistem Deteksi Wajah dengan Menggunakan Metode Viola-Jones. Seminar Nasional “Science, Engineering and Technology,” 1–5.
Revina, I. M., & Emmanuel, W. R. S. (2018). A Survey on Human Face Expression Recognition Techniques. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.09.002
Saha, S. D. T. S. (2018). A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way. https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
Santhoshkumar, R., & Kalaiselvi Geetha, M. (2019). Deep learning approach for emotion recognition from human body movements with feedforward deep convolution neural networks. Procedia Computer Science, 152, 158–165. https://doi.org/10.1016/j.procs.2019.05.038
Santoso, A., & Ariyanto, G. (2018). Implementasi Deep Learning Berbasis Keras Untuk Pengenalan Wajah. Emitor: Jurnal Teknik Elektro, 18(01), 15–21.
https://doi.org/10.23917/emitor.v18i01.6235
Schrouff, J., Stiffler, R., Bertocci, M., Bebko, G., Chase, H., Lockovitch, J., Aslam, H., Graur, S., Greenberg, T., Pereira, M., Oliveira, L., Phillips, M., & Mourão-miranda, J. (2019). NeuroImage : Clinical Predicting anxiety from wholebrain activity patterns to emotional faces in young adults : a machine learning approach. 23(March). https://doi.org/10.1016/j.nicl.2019.101813
Septianto, T., Setyati, E., & Santoso, J. (2018). Model CNN LeNet dalam Rekognisi Angka Tahun pada Prasasti Peninggalan Kerajaan Majapahit. Jurnal
Teknologi Dan Sistem Komputer, 6(3), 106–109.
https://doi.org/10.14710/jtsiskom.6.3.2018.106-109
Setiadi, H., Priyandari, Y., & Cahyono, S. I. (2017). Implementation of Parking System Based on Radio Frequency Identification ( RFID ) at the Faculty of Engineering Sebelas. ITSMART: Jurnal Ilmiah Teknologi Dan Informasi, 6(1), 39–44.
Shustanov, A., & Yakimov, P. (2017). ScienceDirect ScienceDirect CNN Design for Real-Time Traffic Sign Recognition. Procedia Engineering, 201, 718–725. https://doi.org/10.1016/j.proeng.2017.09.594
Siddharth Das. (2017). CNN Architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet and more…. November 16. https://medium.com/analytics- vidhya/cnns-architectures-lenet-alexnet-vgg-googlenet-resnet-and-more-666091488df5
Suendri. (2018). Implementasi Diagram UML (Unified Modelling Language) Pada Perancangan Sistem Informasi Remunerasi Dosen Dengan Database Oracle (Studi Kasus: UIN Sumatera Utara Medan). Jurnal Ilmu Komputer Dan Informatika, 3(1), 1–9.
Sujatha, B. M., Venukumar, B. V., Madiwalar, C. T., Munna, N. C. A., Babu, K. S., Raja, K. B., & Venugopal, K. R. (2016). Translation Based Face Recognition Using Fusion of LL and SV Coefficients. Procedia Computer Science, 89, 877–886. https://doi.org/10.1016/j.procs.2016.06.077
Sun, Y., Xu, C., Li, G., Xu, W., & Kong, J. (2020). Intelligent human computer interaction based on non redundant EMG signal. Alexandria Engineering Journal. https://doi.org/10.1016/j.aej.2020.01.015
Suprianto, D. (2013). Sistem Pengenalan Wajah Secara Real-Time. Sistem Pengenalan Wajah Secara Real-Time Dengan Adaboost, Eigenface PCA & MySQL, 7(2), 179–184.
Yang, C., Kim, Y., Ryu, S., & Gu, G. X. (2020). Prediction of composite microstructure stress-strain curves using convolutional neural networks.
Materials and Design, 189, 108509.
https://doi.org/10.1016/j.matdes.2020.108509
Zufar, M., & Setiyono, B. (2016). Convolutional Neural Networks Untuk Pengenalan Wajah Secara Real-Time. Jurnal Sains Dan Seni ITS, 5(2), 72–77. https://doi.org/10.12962/j23373520.v5i2.18854