PENGARUH JUMLAH FILE TRAINING TERHADAP AKURASI PENDETEKSIAN OBYEK PADA METODE VIOLA JONES

Anggraini Kusumaningrum

Abstract


The development of artificial intelligence-based systems, particularly the field of computer vision more specifically the detection of objects always involves a process of training and testing. The quality of the object detection greatly influenced the training process object detection. Viola-Jones method is often used in object detection process where this method requires a classifier file obtained from the training process. This research is used to determine the trend of the number of the training process on the quality of bottle caps object detection fanta, sprite, Frestea, coca-cola and soda. Tendency obtained can be linear or exponential graph. the result of the research detection a bottle cap, happened saturation amount of test data so that a larger number no longer affect the quality of detection the bottle caps and the relationship between the number of files of positive file and negative file affect the accuracy in detecting an object.


Keywords


training, testing, artificial intelligence, Viola-Jones

References


M. Dwisnanto Putro., Teguh Bharata Adji., Bondhan Winduratna, 2012, Sistem Deteksi Wajah dengan Menggunakan Metode Viola-Jones, Seminar Nasional Science Engineering and Technology, Yogyakarta.

Paul Viola., Jones, M. J., 2001, Rapid Object Detection Using A Boosted Cascade of Simple Features, IEEE Conference on Computer Vision and Pattern Recognition. Jauai. Hawaii.

Raditya Nugraha, Setiawardhana., Nana Ramadijanti, 2012, Sistem Pendeteksian Jari Telunjuk pada Game TicTacToe Menggunakan Metode Viola dan Jones, Jurnal I Link vol 16, No 1, Februari 2012. Surabaya.

Setyo Nugroho., Agus Harjoko. 2004, Sistem Pendeteksi Wajah Manusia pada Citra Digital, Tesis Program Studi Ilmu Komputer Jurusan MIPA, Universitas Gadjah Mada. Yogyakarta.

Muhammad Fikri Hidayattullah., Hapsari Yustia, 2013, Automatic Nipple Detection Pada Citra Pornografi Menggunakan Algoritma Viola And Jones Berbasis AdaBoost Untuk Feature Selection, Program Studi DIII Manajemen Informatika Politeknik Muhammadiyah Pekalongan, Seminar Nasional Teknologi Informasi & Komunikasi Terapan, Semarang.




DOI: http://dx.doi.org/10.28989/senatik.v3i0.99

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Conference SENATIK P-ISSN :2337-3881 and  E-ISSN : 2528-1666

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