Penerapan Deep Learning dalam Pembuatan Chatbot untuk Service Desk ITS
DOI:
https://doi.org/10.59378/jcenim.v2i3.57Keywords:
Chatbot, Deep Learning, Natural language processingAbstract
Artificial intelligence sudah menjadi sesuatu yang tidak bisa dipisahkan dari kehidupan manusia sekarang ini. Salah satu bentuk dari artificial intelligence yang sering ditemui dalam kehidupan sehari-hari adalah chatbot. Belakangan ini chatbot banyak sudah digunakan oleh perusahaan untuk menunjang bisnis mereka. Alasan penggunaan chatbot yang semakin marak di dunia bisnis adalah chatbot dapat digunakan sebagai layanan pelanggan atau \textit{customer service} yang biaya operasionalnya lebih murah dan layanannya yang tersedia 24 jam. Meskipun sudah banyak digunakan di beberapa bidang, penggunaan chatbot di bidang akademik dirasakan masih kurang. Dalam ruang lingkup Institut Teknologi Sepuluh Nopember (ITS) sendiri, layanan Service Desk ITS masih menggunakan metode manual dalam menjawab pertanyaan yang disampaikan, yaitu dijawab langsung oleh admin. Oleh karena itu dibutuhkan chatbot yang dapat digunakan untuk mengatasi masalah tersebut.
References
N. K. Manaswi, N. K. Manaswi, and S. John, Deep Learning with Applications Using Python. Springer, 2018.
R. Astuti, “Chatbot: Semua tentang chatbot,” Oktober 2020.
R. Csaky, “Deep learning based chatbot models,” arXiv preprint arXiv:1908.08835, 2019.
M. J. Garbade, “A simple introduction to natural language processing,” Oktober 2018.
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep Learning, vol. 1. MIT Press Cambridge, 2016.
T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” IEEE Computational Intelligence Magazine, vol. 13, no. 3, pp. 55–75, 2018.
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder–decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
S. Hochreiter, “The vanishing gradient problem during learning recurrent neural nets and problem solutions,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 6, no. 02, pp. 107–116, 1998.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” Advances in Neural Information Processing Systems, vol. 27, pp. 3104–3112, 2014.
T. Bocklisch, J. Faulkner, N. Pawlowski, and A. Nichol, “Rasa: Open source language understanding and dialogue management,” arXiv preprint arXiv:1712.05181, 2017.
G. Cukier, “Rasa architecture overview,” Desember 2020.
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