Implementation of Random Forest on Face Recognition Using Isomap Features

Rifki Kosasih, Achmad Fahrurozi, Desti Riminarsih

Abstract


Sistem pengenalan wajah merupakan salah satu bidang yang digunakan untuk mengenali wajah seseorang. Dalam penelitian ini, data yang dikumpulkan merupakan data citra wajah yang terdiri dari 24 citra dengan komposisi 6 orang dan tiap orang memiliki 4 citra dengan berbagai ekspresi. Untuk mengenali wajah tersebut, dilakukan ekstraksi fitur wajah terlebih dahulu menggunakan metode isomap. Isomap merupakan metode reduksi dimensi yang dapat mereduksi dari dimensi tinggi menjadi fitur-fitur yang berdimensi rendah. Berdasarkan hasil ekstraksi diperoleh 4 fitur yang digunakan untuk mengklasifikasikan wajah. Untuk mengklasifikasikan wajah, digunakan algoritma random forest. Berdasarkan hasil penelitian diperoleh tingkat akurasi hasil klasifikasi sebesar 87,5%, nilai weighted average precision sebesar 81,25% dan nilai weighted average recall sebesar 87,5%.


Keywords


Pengenalan Wajah; Ekstraksi Fitur; Isomap; Random Forest

Full Text:

PDF

References


R. Kosasih, “Penggunaan Metode Linear Discriminant Analysis Untuk Pengenalan Wajah Dengan Membandingkan Banyaknya Data Latih,” J. Ilm. Teknol. dan Rekayasa, vol. 26, no. 1, pp. 25–34, 2021.

R. Kosasih and C. Daomara, “Pengenalan Wajah dengan Menggunakan Metode Local Binary Patterns Histograms ( LBPH ),” Media Inform. Budidarma, vol. 5, pp. 1258–1264, 2021, doi: 10.30865/mib.v5i4.3171.

L. Van der Maaten, E. Postma, and J. Van den Herik, “Dimensionality Reduction : A Comparative Review,” Tilburg, Netherlands, 2009.

J. B. Tenenbaum, V. De Silva, and J. C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science (80-. )., vol. 290, no. December, pp. 2319–2323, 2000.

Salamun and F. Wazir, “Rancang Bangun Sistem Pengenalan Wajah Dengan Metode Principal Component Analysis,” J. Teknol. dan Sist. Inf. UNIVRAB, vol. 1, no. 2, pp. 59–75, 2016, doi: 10.36341/rabit.v1i2.25.

P. Rosyani, “Pengenalan Wajah Menggunakan Metode Principal Component Analysis (PCA) dan Canberra Distance,” J. Inform. Univ. Pamulang, vol. 2, no. 2, p. 118, 2017, doi: 10.32493/informatika.v2i2.1515.

A. Fahrurozi and R. Kosasih, “Face Recognition Using Local Binary Pattern Combined With PCA For Images Under Various Expression and Illumination,” in Proceeding on International Workshop on Academic Collaboration 2017, 2017, no. May, pp. 1–7.

R. Kosasih and A. Fahrurozi, “Clustering of Face Images by Using Isomap method,” in Proceeding on International Workshop on Academic Collaboration 2017, 2017, no. May, pp. 52–56.

R. Kosasih, “Kombinasi Metode Isomap dan KNN Pada Image Processing Untuk Pengenalan Wajah,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 2, pp. 166–170, 2020.

S. Menaria and D. Mukherjee, “Video Manifold Feature Extraction Based on ISOMAP,” Int. J. Eng. Sci. Invent., vol. 4, no. 4, pp. 64–67, 2015, [Online]. Available: www.ijesi.org.

V. Y. Kullarni and P. K. Sinha, “Random Forest Classifier: A Survey and Future Research Directions,” Int. J. Adv. Comput., vol. 36, no. 1, pp. 1144–1156, 2013.

A. Krogh and J. Vedelsby, “Neural Network Ensembles, Cross Validation, and Active Learning Anders,” pp. 6–7, 2010.

D. Opitz and R. Maclin, “Popular ensemble learning: an empirical study,” J. Artif. Intell. Res., vol. 11, pp. 169–198, 1999.

L. Breiman, “Bagging predictors,” Mach. Learn., vol. 24, no. 2, pp. 123–140, 1996, doi: 10.1007/bf00058655.

R. E. Schapire, “The Boosting Approach to Machine Learning: An Overview BT - Nonlinear Estimation and Classification,” Nonlinear Estim. Classif., vol. 171, no. Chapter 9, pp. 149–171, 2003, [Online]. Available: http://link.springer.com/10.1007/978-0-387-21579-2_9%0Apapers3://publication/doi/10.1007/978-0-387-21579-2_9.

M. Reza, S. Miri, and R. Javidan, “A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset,” Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 6, pp. 1–33, 2016, doi: 10.14569/ijacsa.2016.070603.

J. Ali, R. Khan, N. Ahmad, and I. Maqsood, “Random forests and decision trees,” IJCSI Int. J. Comput. Sci. Issues, vol. 9, no. 5, pp. 272–278, 2012.

I. Amalia, “Pengenalan Citra Tanda Tangan Menggunakan Gray Level Co-Occurrence Matrix (GLCM) Dan Probabilistic Neural Network (PNN),” E-Jurnal Politek. negeri Lhokseumawe, vol. 14, pp. 29–34, 2014.

L. A. Septiandi, E. M. Yuniarno, and A. Zaini, “Deteksi Kedipan dengan Metode CNN dan Percentage of Eyelid Closure (PERCLOS),” Tek. Its, vol. 10, no. 1, pp. A56–A57, 2021.

T. Yulianti, M. Telaumbanua, H. D. Septama, and H. Fitriawan, “Pengaruh Seleksi Fitur Citra Terhadap Klasifikasi Tingkat the Effect of Image Feature Selection on the Local Beef,” vol. 10, no. 1, pp. 85–95, 2021.

D. P. Lestari, R. Kosasih, T. Handhika, Murni, I. Sari, and A. Fahrurozi, “Fire Hotspots Detection System on CCTV Videos Using You only Look Once (YOLO) Method and Tiny YOLO Model for High Buildings Evacuation,” in 2nd International Conference of Computer and Informatics Engineering IC2IE, 2019, pp. 87–92.

M. Sokolova and G. Lapalme, “A Systematic Analysis of Performance Measures for Classification Tasks,” Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, 2009.




DOI: https://doi.org/10.24114/cess.v7i2.34498

Article Metrics

Abstract view : 155 times
PDF - 194 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

CESS (Journal of Computer Engineering, System and Science)

Creative Commons License
CESS (Journal of Computer Engineering, System and Science) is licensed under a Creative Commons Attribution 4.0 International License