A Tracer Study Design With Whatsapp Chatbot Integration Using Natural Language Processing
DOI:
https://doi.org/10.33005/itij.v2i2.24Keywords:
Whatsapp, Bot, NLP, Tracer StudyAbstract
Tracer study is a method used by educational institutions to track alumni and assess the effectiveness of the education provided. A key challenge in conducting these studies is the low participation rate of respondents, often due to lengthy surveys and a lack of interactive engagement. To address this issue, a WhatsApp chatbot system powered by Natural Language Processing (NLP) was developed. This system facilitates an interactive and user-friendly survey experience, allowing respondents to complete the survey directly through WhatsApp without needing to visit a website. Responses are automatically stored in Google Sheets via an API. By using a microservices architecture, the project efficiently separates crucial components such as WhatsApp API, NLP services, and Google Sheets API, leading to improved data collection efficiency and a more convenient survey process for respondents.
Downloads
References
H. Schomburg, Handbook for Graduate Tracer Studies, 2nd ed. Moenchebergstrasse, Germany: International Centre for Higher Education Research, 2003.
Kementerian Pendidikan dan Kebudayaan, “Tracer Study Kemdikbud,” https://tracerstudy.kemdikbud.go.id/. Accessed: Oct. 15, 2024. [Online]. Available: https://tracerstudy.kemdikbud.go.id/
Trisnani, “Pemanfaatan WhatsApp Sebagai Media Komunikasi Dan Kepuasan Dalam Penyampaian Pesan Di Kalangan Tokoh Masyarakat,” J. Komunika J. Komunikasi, Media dan Inform., vol. 6, no. 3, Dec. 2017, doi: 10.31504/komunika.v6i3.1227.
Z. N. Baiti and F. Nugroho, “Aplikasi Chatbot ‘Mi3’ Untuk Informasi Jurusan Teknik Informatika Berbasis Sistem Pakar Menggunakan Metode Forward Chaining,” MATICS, Oct. 2013, doi: 10.18860/mat.v0i0.2425.
B. Setiaji and F. W. Wibowo, “Chatbot Using a Knowledge in Database: Human-to-Machine Conversation Modeling,” in Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS, IEEE, Jan. 2016, pp. 72–77. doi: 10.1109/ISMS.2016.53.
J. Pustejovsky and A. Stubbs, Natural language annotation for machine learning. Sebastopol,USA: O’RellyMedia,Inc, 2013. [Online]. Available: http://it-ebooks.info/book/681/%5Cnpapers3://publication/uuid/906A922E-DE39-4CAB-8067-F222D065ACEF
C. S. , Yulrio Brianorman, Rio Alamanda, “Aplikasi Pendeteksi Plagiat Terhadap Karya Tulis Berbasis Web Menggunakan Natural Language Processing Dan Algoritma Knuth-Morris-Pratt,” Coding J. Komput. dan Apl., vol. 4, no. 1, Jan. 2016, doi: 10.26418/coding.v4i1.13332.
M. F. A. Muri, H. S. Utomo, and R. Sayyidati, “Search Engine Get Application Programming Interface,” J. Sains dan Inform., vol. 5, no. 2, pp. 88–97, Dec. 2019, doi: 10.34128/jsi.v5i2.175.
Y. Y. Marwanta and B. Badiyanto, “mplementasi arsitektur microservice pada pembuatan surat unit kegiatan mahasiswa informatika dan komputer menggunakan Node.js,” JIKO (Jurnal Inform. dan Komputer), vol. 4, no. 2, pp. 71–80, Sep. 2019, [Online]. Available: https://ejournal.akakom.ac.id/index.php/jiko/article/view/516
M. R. Adani, “Mengenal Metode Agile dan 12 Prinsipnya bagi Software,” https://www.sekawanmedia.co.id/blog/metode-agile-development/. Accessed: Oct. 16, 2024. [Online]. Available: https://www.sekawanmedia.co.id/blog/metode-agile-development/
I. Carolina and A. Rusman, “Penerapan Extreme Programming Pada Sistem Informasi Penjualan Pakaian Berbasis Web (Studi Kasus Toko ST Jaya),” INOVTEK
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Wigananda Firdaus
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.