Penerapan Genetic Neural Network dalam Pemilihan Color Palette untuk Desain Skema Warna

Authors

  • Jason Alexander Bunda Mulia University
  • Daniel Ronaldo Pangestu Bunda Mulia University
  • Ferdy Nicolas Bunda Mulia University
  • Lukman Hakim Bunda Mulia University

DOI:

https://doi.org/10.31154/cogito.v6i2.271.284-297

Abstract

Artificial Neural Network (ANN) is a branch of Artificial Intelligence which possesses great potential. However, ANN has a huge drawback: the need for complex parameters to train a developed ANN in order to provide the right solution to the proposed problem. One way to overcome this weakness is the use of Genetic Algorithm as the best parameter selection method for a ANN. In this study, said concept was applied in an application that aims to help solve one of the aspects of design that has a high significance, namely color selection, by implementing both technologies simultaneously into a predictive system. To predict colors based on given parameters, the algorithm is formed to distinguish how colors are used so that they are able to group colors into specific color categories to then be combined to form a palette, which can be the basis of a color scheme for various design purposes. Based on the results of this study, researchers hope to provide additional insights into various fields that can synergize with Artificial Intelligence and its derivative disciplines to improve efficiency in various occupations, of which design is one of them.

Author Biographies

Jason Alexander, Bunda Mulia University

Department of Informatics Engineering, Faculty of Technology & Design, college student

Daniel Ronaldo Pangestu, Bunda Mulia University

Department of Informatics Engineering, Faculty of Technology & Design, college student

Ferdy Nicolas, Bunda Mulia University

Department of Informatics Engineering, Faculty of Technology & Design, college student

Lukman Hakim, Bunda Mulia University

Head of Department of Informatics Engineering, Faculty of Technology & Design

References

E. Stolterman and J. Pierce, “Design tools in practice: Studying the designer-tool relationship in interaction design,” Proc. Des. Interact. Syst. Conf. DIS ’12, no. June, pp. 25–28, 2012, doi: 10.1145/2317956.2317961.

J. Li, “Applications of computer-aided design technology in engineering and industry,” Comput. Aided Des. Technol. Types Pract. Appl., no. December 2012, pp. 87–102, 2012.

O. Huei, R. Che Rus, and A. Kamis, “Construct Validity and Reliability in Content Knowledge of Design and Technology Subject: A Rasch Measurement Model Approaches for Pilot Study,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 10, Mar. 2020, doi: 10.6007/IJARBSS/v10-i3/7066.

I. Hestiningsih, “Pengantar Kecerdasan Buatan,” kecerdasan buatan (Artificial Intell., pp. 1–2, 2019.

D. Setiawan, R. N. Putri, and R. Suryanita, “Implementasi Algoritma Genetika Untuk Prediksi Penyakit Autoimun,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 4, no. 1, pp. 8–16, 2019, doi: 10.36341/rabit.v4i1.595.

A. Pujianto, K. Kusrini, and A. Sunyoto, “Perancangan Sistem Pendukung Keputusan Untuk Prediksi Penerima Beasiswa Menggunakan Metode Neural Network Backpropagation,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 2, p. 157, 2018, doi: 10.25126/jtiik.201852631.

C. Reeves, “Chapter 3 GENETIC ALGORITHMS Part A : Background,” Inf. Sci. (Ny)., no. May, 1975, doi: 10.1007/978-1-4419-1665-5.

V. Mallawaarachchi, “Introduction to Genetic Algorithms — Including Example Code,” 2017. https://towardsdatascience.com/introduction-to-genetic-algorithms-including-example-code-e396e98d8bf3.

S. Mandal, T. A. Anderson, J. S. Turek, J. Gottschlich, S. Zhou, and A. Muzahid, “Learning Fitness Functions for Genetic Algorithms,” 2019, [Online]. Available: http://arxiv.org/abs/1908.08783.

S. Marsili Libelli and P. Alba, “Adaptive mutation in genetic algorithms,” Soft Comput., vol. 4, no. 2, pp. 76–80, 2000, doi: 10.1007/s005000000042.

L. Pater, “Application of artificial neural networks and genetic algorithms for crude fractional distillation process modeling,” no. 2, 2016, [Online]. Available: http://arxiv.org/abs/1605.00097.

S. Tomás, P. Suárez, and S. T. Pérez Suárez, “EasyChair Preprint Design methodology of sigmoid functions for Neural Networks using lookup tables on FPGAs Design methodology of sigmoid functions for Neural Networks using lookup tables on FPGAs,” no. October, 2018, doi: 10.13140/RG.2.2.23433.70248.

P. Marius-Constantin, V. E. Balas, L. Perescu-Popescu, and N. Mastorakis, “Multilayer perceptron and neural networks,” WSEAS Trans. Circuits Syst., vol. 8, no. 7, pp. 579–588, 2009.

T. Gnanasegaram, “Artificial Neural Network-A Brief Introduction,” 2018. https://medium.com/@tharanignanasegaram/artificial-neural-network-a-brief-introduction-572d462666f1.

M. Inthachot, V. Boonjing, and S. Intakosum, “Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend,” Comput. Intell. Neurosci., vol. 2016, 2016, doi: 10.1155/2016/3045254.

D. Venkatesan, K. Kannan, and R. Saravanan, “A genetic algorithm-based artificial neural network model for the optimization of machining processes,” Neural Comput. Appl., vol. 18, no. 2, pp. 135–140, 2009, doi: 10.1007/s00521-007-0166-y.

D. H. Timmons, “Choosing a Color Scheme From the Color Wheel,” The Spruce, 2019. .

P. Weingerl and D. Javoršek, “Theory of colour harmony and its application,” Teh. Vjesn., vol. 25, no. 4, pp. 1243–1248, 2018, doi: 10.17559/TV-20170316092852.

P. Lyons and G. Moretti, “Nine tools for generating harmonious colour schemes,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3101, no. June 2004, pp. 241–251, 2004, doi: 10.1007/978-3-540-27795-8_25.

B. McMillin, Software Engineering, vol. 51, no. 2. 2018.

M. Swedia, Ericks Rachmat & Cahyanti, “Algoritma Tranformasi Ruang Warna,” Vis. Bassic6, Vis. Basic.NET dan java, pp. 1–94, 2010.

P. Menesatti, C. Angelini, F. Pallottino, F. Antonucci, J. Aguzzi, and C. Costa, “RGB color calibration for quantitative image analysis: The ‘3D Thin-Plate Spline’ warping approach,” Sensors (Switzerland), vol. 12, no. 6, pp. 7063–7079, 2012, doi: 10.3390/s120607063.

K. Srinivasan, A. K. Cherukuri, D. R. Vincent, A. Garg, and B. Y. Chen, “An efficient implementation of artificial neural networks with K-fold cross-validation for process optimization,” J. Internet Technol., vol. 20, no. 4, pp. 1213–1225, 2019, doi: 10.3966/160792642019072004020.

Downloads

Published

2020-12-11

How to Cite

Alexander, J., Pangestu, D. R., Nicolas, F., & Hakim, L. (2020). Penerapan Genetic Neural Network dalam Pemilihan Color Palette untuk Desain Skema Warna. CogITo Smart Journal, 6(2), 284–297. https://doi.org/10.31154/cogito.v6i2.271.284-297