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Abstract





Machine learning application demand is increased massively because it provides good ability in the classification that is needed by decision makers. Machine learning application uses a programming language with strong characteristics in computing, usually the back-end programming language, such as Matlab, Python, R, etc. The obstacle faced by the decision support system developer is preparing an interface that makes it easy for the user. Some back-end programming languages have provided a good interface. Therefore, in this study they were compared by taking the case of a scholarship decision support system. The language used is Python with two web-based applications including Google Interactive Notebook and Flask framework. Both devices have their respective advantages and are worthy of being the first choice in the design of decision support systems.Python has advantages with framework Flask support and Matlab is easy in interface design.


 


 





Keywords

Decision Support System Flask Jinja2 Technical Computing Language Artificial Neural Networks

Article Details

Creative Commons License

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

Author Biographies

Rahmadya Trias Handayanto, Fakultas Teknik, Universitas Islam 45

Fakultas Teknik, Universitas Islam 45

Herlawati Herlawati, Fakultas Teknik, Universitas Bhayangkara Jakarta Raya

Fakultas Teknik, Universitas Bhayangkara Jakarta Raya

References

  1. S. Haykin, “Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7.,” The Knowledge Engineering Review, vol. 13, no. 4. pp. 409–412, 1999.
  2. M. Hagan, M. T., Demuth, H. B., & Beale, Neural Network Design. Boston: PWS Publishing Co., 1997.
  3. M. Koprawi, “Parallel Computation in Uncompressed Digital Images Using Computer Unified Device Architecture and Open Computing Language,” PIKSEL, vol. 8, no. 1, pp. 31–38, 2020.
  4. NVIDIA, “NVIDIA on GPU Computing and the Difference Between GPUs and CPUs.,” 2020.
  5. L. Pan, L. Gu, and J. Xu, “Implementation of medical image segmentation in CUDA,” 5th Int. Conf. Inf. Technol. Appl. Biomed. ITAB 2008 conjunction with 2nd Int. Symp. Summer Sch. Biomed. Heal. Eng. IS3BHE 2008, pp. 82–85, 2008.
  6. P. Kim, MATLAB Deep Learning. New York: Apress, 2017.
  7. L. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. USA: Prentice-Hall, Inc., 1994.
  8. R. N. Whidhiasih, “Identifikasi tingkat manis buah belimbing berdasarkan citra red green blue menggunakan fuzzy neural network,” PIKSEL Penelit. Ilmu Komput. Sist. Embed. Log., vol. 3, no. 2, pp. 109–120, 2015.
  9. R. T. Handayanto and H. Herlawati, “Prediksi Kelas Jamak dengan Deep Learning Berbasis Graphics Processing Units,” J. Kaji. Ilm., vol. 20, no. 1, pp. 67–76, 2020.
  10. L. Fausett, Fundamentals of Neural Networks, no. 1. 1994.
  11. M. Nehra, “Top 5 Python Frameworks for Web Development in 2020,” 2020. [Online]. Available: https://medium.com/@mahipal.nehra/top-5-python-frameworks-for-web-development-in-2020-4abd6ee5f8ec. [Accessed: 26-May-2020].
  12. P. P. Widodo, R. T. Handayanto, and H. Herlawati, Penerapan Data Mining dengan Matlab. Bandung: Informatika, 2013.
  13. H. Herlawati and R. T. Handayanto, “Penggunaan Matlab dan Python dalam Klasterisasi Data,” J. Kaji. Ilm., vol. 20, no. 1, pp. 103–118, 2020.

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