'Pakarena' dance image classification using convolutional neural network algorithm


Abdul Ibrahim(1*); Rachmat Rachmat(2);

(1) STMIK Dipanegara
(2) Politeknik Informatika Nasional
(*) Corresponding Author

  

Abstract


One of the riches of the Indonesian nation comes from the diversity of ethnicities and cultures, especially dance, which is the culture of the Indonesian people, starting from their ancestors until now, their authenticity is still maintained. The wrong cultural dance that develops, especially in South Sulawesi, which consists of four (4) ethnic groups, namely: Bugis, Makassar, Toraja and Mandar, which have their own dance dances from each tribe in South Sulawesi to maintain this dance. There is a need for collaboration between local community leaders, government and researchers, especially researchers to raise dance dances from the Makassar Tribe called Pakkarena dance using the Convolutional Neural Network (CNN) method to the Pakarena dance image in distinguishing or classifying an object on digital images with an accuracy level of 95 75%.

Keywords


Makassar Community Dance; Convolutional Neural Networks; Image

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 367 times
PDF view: 218 times
     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v13i2.816.134-139
  

Cite

References


R. Cioffi, M. Travaglioni, G. Piscitelli, A. Petrillo, and F. De Felice, Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions, Sustain., vol. 12, no. 2, 2020, doi: 10.3390/su12020492

W. S. Eka Putra, Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101, J. Tek. ITS, vol. 5, no. 1, 2016, doi: 10.12962/j23373539.v5i1.15696.

E. N. Arrofiqoh and H. Harintaka, Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Tanaman Pada Citra Resolusi Tinggi, Geomatika, vol. 24, no. 2, p. 61, 2018, doi: 10.24895/jig.2018.24-2.810.

A. N. H, M. Ichwan, and I. M. S. Putra, Segmentasi Citra Untuk Deteksi Objek Warna Pada Aplikasi Pengambilan Bentuk Citra Rectangle, J. Unpubl., pp. 110, 2015.

M. Dahria, Kecerdasan buatan (Artificial Intelligence), J. SAINTIKOM, vol. 5, no. 2, pp. 110, 2008.

I. Namat?vs, Deep Convolutional Neural Networks : Structure , Feature Extraction and Training, vol. 20, no. December, pp. 4047, 2017, doi: 10.1515/itms-2017-0007.

D. E. Tarkus, S. R. U. A. Sompie, and A. Jacobus, Implementasi Metode Recurrent Neural Network pada Pengklasifikasian Kualitas Telur Puyuh, J. Tek. Inform., vol. 15, no. 2, pp. 137144, 2020.

J. Sturm, C. Stachniss, and W. Burgard, A probabilistic framework for learning kinematic models of articulated objects, J. Artif. Intell. Res., vol. 41, pp. 477526, 2011, doi: 10.1613/jair.3229.

M. Z. Fawziah, Rumiwiharsih, and W. R. Ananda, Function Shifts and Composition Form Reconstruction of Pakarena Dance, vol. 444, no. Icaae 2019, pp. 190195, 2020, doi: 10.2991/assehr.k.200703.038.

M. Zufar, Convolutional Neural Networks untuk Pengenalan Wajah Secara Real - Time, vol. 5, no. 2, pp. 7277, 2016.

A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, Deep Learning for Computer Vision: A Brief Review, Comput. Intell. Neurosci., vol. 2018, 2018, doi: 10.1155/2018/7068349.

H. J. A. Y. Siegel and L. J. Siegel, PASM : for Image Processing and Pattern Recognition System, vol. c, no. 12, pp. 934947, 1981.

A. S. Kurniawan, Implementasi Metode Artificial Neural Network Dalam Memprediksi Hasil Ujian Kompetensi Kebidanan (Studi Kasus Di Akademi Kebidanan Dehasen Bengkulu), Pseudocode, vol. 5, no. 1, pp. 3744, 2018, doi: 10.33369/pseudocode.5.1.37-44.

M. Theresia, the Markers of Politeness of Refusal in Penelope Movie, Tell-Us J., vol. 2, no. 2, pp. 5677, 2016, doi: 10.22202/tus.2016.v2i2.1331.

Y. A. Hasma and W. Silfianti, Implementasi Deep Learning Menggunakan Framework Tensorflow Dengan Metode Faster Regional Convolutional Neural Network Untuk Pendeteksian Jerawat, J. Ilm. Teknol. dan Rekayasa, vol. 23, no. 2, pp. 89102, 2018, doi: 10.35760/tr.2018.v23i2.2459.

V. Maha, P. Salawazo, D. Putra, J. Gea, F. Teknologi, and U. P. Indonesia, Implementasi Metode Convolutional Neural Network ( CNN ) Pada Peneganalan Objek Video Cctv, J. Mantik Penusa, vol. 3, no. 1, pp. 7479, 2019.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Rachmat memet

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