Implementation Of A Web-Based Chatbot Using Machine Learning For Question And Answer Services In Universities

Airlangga Satria Dewantara; Joko Aryanto

doi:10.26877/asset.v6i1.17590

Implementation Of A Web-Based Chatbot Using Machine Learning For Question And Answer Services In Universities

Airlangga Satria Dewantara, Joko Aryanto

Abstract

Advances in communication technology in line with information technology, Chatbot is an innovation that combines communication technology and information technology, is an application that can communicate with humans like a virtual assistant who can respond and answer every question asked. A university must already have a website that can be accessed by the general public so that information about the college can be accessed by everyone anywhere and anytime. To make it easier to get information on the website, chatbots can be the solution because most prospective students and students who are on the campus feel reluctant to browse further into the website that has been provided and usually only open the main homepage page of the website. Parents of students also find it difficult to find out what is on campus if a lot of information is provided in certain tabs of the website. In this study, I utilized Chatbot technology which is a Machine Learning that can process every text that inputted then analyze it and conduct machine training using the Neural Network algorithms that have been provided. This research uses a case study methodology, with Yogyakarta University of Technology as the subject, to develop a chatbot website that incorporates machine learning to facilitate the processing of user input questions

Keywords

Chatbot; Machine Learning; Question and Answer; Neural Network; Python

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References

A. Augello, G. Pilato, G. Vassallo, and S. Gaglio, “Chatbots as Interface to Ontologies,” vol. 260, pp. 285–299, 2014.

F. Ishlakhuddin, “Chatbot Berbasis Ontologi untuk Mendukung Pemantauan Kinerja dan Keamanan Server dengan Rule-Base,” 2020.

Agung Siswanto Bayu Aji, “Membangun Chatbot Layanan Helpdesk Perpajakan Kpp Pratama Jakarta Setiabudi Satu,” Sebatik, vol. 26, no. 1, pp. 194–201, Jun. 2022, doi: 10.46984/sebatik.v26i1.1916.

Teddy Wijaya, Muhammad Rusli, Erwin Syah Rany, and Harfebi Fryonanda, “Membangun Aplikasi Chatbot Berbasis Web Pada CV. Unomax Indonesia,” 2019.

Ahmad Cucus, Robby Yuli Endra, and Tiya Naralita, “Chatter bot untuk konsultasi akademik di perguruan Tinggi,” Jurnal Sistem Informasi dan Telematika, vol. 10, no. 1, 2019.

A. Menura Mukhiddinovna, “PROGRAMMING LANGUAGE PYTHON METHODOLOGY FOR CREATING AND USING DIDACTIC MATERIALS FOR STUDENTS,” 2022.

A. Trivedi and Z. Thakkar, “Chatbot generation and integration: A review,” 2019. [Online]. Available: www.IJARIIT.com

J. Wei et al., “Machine learning in materials science,” InfoMat, vol. 1, no. 3. Blackwell Publishing Ltd, pp. 338–358, Sep. 01, 2019. doi: 10.1002/inf2.12028.

D. Assyakurrohim, D. Ikhram, R. A. Sirodj, and M. W. Afgani, “Metode Studi Kasus dalam Penelitian Kualitatif,” Jurnal Pendidikan Sains dan Komputer, vol. 3, no. 01, pp. 1–9, Dec. 2022, doi: 10.47709/jpsk.v3i01.1951.

Universitas Teknologi Yogyakarta, “UTY Homepage,” 2023. https://uty.ac.id/ (accessed Sep. 20, 2023).

Universitas Teknologi Yogyakarta, “PMB UTY,” 2023. https://pmb.uty.ac.id/ (accessed Sep. 20, 2023).

B. Cecconi, C. K. Louis, X. Bonnin, A. Loh, and M. B. Taylor, “Time-frequency catalogue: JSON implementation and python library,” Frontiers in Astronomy and Space Sciences, vol. 9, Feb. 2023, doi: 10.3389/fspas.2022.1049677.

O. Filipova and O. Nikiforova, “Definition of the Criteria for Layout of the UML Use Case Diagrams,” Applied Computer Systems, vol. 24, no. 1, pp. 75–81, May 2019, doi: 10.2478/acss-2019-0010.

J. Lang and D. Spišák, “Activity Diagram as an Orientation Catalyst within Source Code,” 2021.

J. Li, “Using Flowchart to Help Students Learn Basic Circuit Theories Quickly,” Sustainability (Switzerland), vol. 14, no. 12, Jun. 2022, doi: 10.3390/su14127516.

R. R. Rerung, M. Fauzan, and H. Hermawan, “Website Quality Measurement of Higher Education Services Institution Region IV Using Webqual 4.0 Method,” International Journal of Advances in Data and Information Systems, vol. 1, no. 2, pp. 89–102, May 2020, doi: 10.25008/ijadis.v1i2.185.

Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A survey of convolutional neural networks: analysis, applications, and prospects,” IEEE Trans Neural Netw Learn Syst, 2021.

R. Tabarés, “HTML5 and the evolution of HTML; tracing the origins of digital platforms,” Technol Soc, vol. 65, p. 101529, 2021, doi: https://doi.org/10.1016/j.techsoc.2021.101529.

A. Wirfs-Brock and B. Eich, “JavaScript: The first 20 years,” Proceedings of the ACM on Programming Languages, vol. 4, no. HOPL, Jun. 2020, doi: 10.1145/3386327.

M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing for Student Complaint Document Classification Using Sastrawi,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Jul. 2020. doi: 10.1088/1757-899X/874/1/012017.

V. Rama Vyshnavi and A. Malik, “Efficient Way of Web Development Using Python and Flask,” 2019.

T. A. Koleck, C. Dreisbach, P. E. Bourne, and S. Bakken, “Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review,” Journal of the American Medical Informatics Association, vol. 26, no. 4, pp. 364–379, Apr. 2019, doi: 10.1093/jamia/ocy173.

A. Rajšp and I. Fister, “A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training,” 2020, doi: 10.3390/appxx010005.

C. R. Harris et al., “Array programming with NumPy,” Nature, vol. 585, no. 7825. Nature Research, pp. 357–362, Sep. 17, 2020. doi: 10.1038/s41586-020-2649-2.

Z. Huang et al., “Binary tree-inspired digital dendrimer,” Nat Commun, vol. 10, no. 1, Dec. 2019, doi: 10.1038/s41467-019-09957-6.

D. Chicco, N. Tötsch, and G. Jurman, “The matthews correlation coefficient (Mcc) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation,” BioData Min, vol. 14, pp. 1–22, 2021, doi: 10.1186/s13040-021-00244-z.

M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/ACCESS.2022.3151048.

DOI: https://doi.org/10.26877/asset.v6i1.17590

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