ANALISIS IMPLEMENTASI PREPROCESSING DENGAN OTSU-GAUSSIAN PADA PENGENALAN WAJAH


Annahl Riadi(1*); Ruhmi Sulaehani(2);

(1) Universitas Ichsan Gorontalo
(2) Universitas Ichsan Gorontalo
(*) Corresponding Author

  

Abstract


In this research, we will focus on facial expressions to detect customer satisfaction in mini markets where the service level is less than optimal. To find out the level of custome satisfaction can be seen through facial recognition tahen through CCTV in the mini market. The problems that occur are many customers who do not directly convey the impression that is felt when shopping, while minimarkets and shopping conters must know the level of customer satisfaction to improve sales strategies. Research to solve the problem is still rerely done, therefore one of the roles of intelligent computing is to solve the problem using Support Vector machine (SVM). The purpose of this study is to improve the accuracy of facial expressions of mini market customers through improved preprocessing. The results of the application of the otsu method and the gaussian function can be used for the preprocessing stage through a threshold image that has good image quality. The otsu-gaussian method is not effectively used for preprocessing data sourced from video or images with poor image quality, making it difficult to recognize faces.


Keywords


Expression; Face; Customer; otsu-gaussian

  
  

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doi  https://doi.org/10.33096/ilkom.v11i3.457.200-205
  

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References


E. Paulus, Paulus, Erik, et al. "Pengenalan Ekspresi Wajah Dalam Waktu Nyata Menggunakan Hausdorff Distance", Bandung : Seminar Nasional Teknologi Informasi, 2015.

H. Husdi, “Pengenalan Ekspresi Wajah Pengguna Elearning Menggunakan Artificial Neural Network Dengan Fitur Ekstraksi Local Binary Pattern Dan Gray Level Co-Occurrence Matrix”, Ilk. J. Ilm., 2016.

Shahrabi Farahani F, “A fuzzy approach for facial emotion recognition”. J. Neurol. Sci., vol. 02, no. 2, 2013.

M. Athoillah, M. I. Irawan, dan E. M. Imah, “Study Comparison Of Svm-, K-Nn- And Backpropagation-Based Classifier For Image Retrieval”, hal. 883, 2015.

N. Neneng, K. Adi, dan R. Isnanto, “Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM)”, J. Sist. Inf. BISNIS, 2016.

A. Riadi, “Pengenalan Plat Nomor Kendaraan Ganjil Genap Menggunakan Metode Support Vector Machine ( SVM )”, vol. 10, hal. 168–185, 2014.

F. Roberti de Siqueira, W. Robson Schwartz, dan H. Pedrini, “Multi-scale gray level co-occurrence matrices for texture description”, Neurocomputing, 2013.

Prasetyo, “Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab”, Penerbit Andi, 2014.

R. A. Surya, A. Fadlil, dan A. Yudhana, “Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix (GLCM) dan Filter Gabor untuk Klasifikasi citra Batik Pekalongan”, J. Inform., vol. 02, no. 02, hal. 23–26, 2017.

M. Widyaningsih, “Identifikasi Kematangan Buah Apel Dengan Gray Level Co-Occurrence Matrix (GLCM)”, J. SAINTEKOM, vol. 6, no. 1, hal. 71–88, 2017.

I. Kononenko dan M. Kukar, “Data Preprocessing”, in Machine Learning and Data Mining, 2007.

E. Suryanto dan S. W. Purnami, “Perbandingan Reduced Support Vector Machine dan Smooth Support Vector Machine untuk Klasifikasi Large Data”, J. Sains dan Seni ITS, 2015.

J. S. Wibowo, “Deteksi dan Klasifikasi Citra Berdasarkan Warna Kulit Menggunakan HSV”, J. Teknol. Inf. Din., vol. 16, no. 2, hal. 118–123, 2011.

H. Gao, W. Xu, J. Sun, dan Y. Tang, “Multilevel thresholding for image segmentation through an improved quantum-behaved particle swarm algorithm”, IEEE Trans. Instrum. Meas., 2010.

E. P. Mandyartha dan C. Fatichah, “Three-level Local Thresholding Berbasis Metode Otsu untuk Segmentasi Leukosit pada Citra Leukemia Limfoblastik Akut”, J. Buana Inform., vol. 7, no. 1, hal. 43–54, 2016.


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