Algoritma Artificial Neural Network pada Text-based Chatbot Frequently Asked Question (FAQ) Web Kuliah Universitas Nasional
DOI:
https://doi.org/10.35870/jtik.v5i4.261Keywords:
Chatbot, Artificial Neural Network, ANN, Text-basedAbstract
The technology development increase the number of automation system in indudustry. One of them is Chatbot application in education industry. This automation technology is able to lessen university's service in order to facilitate the students' need of information whenever and wherever they are. Lack of student literacy regarding the functions and use of the web in conducting online lectures causes the same number of questions repeatedly to the university, which are actually frequently asked questions that have been written in a list of frequently asked questions (Faq), such as: assignment submission, forget passwords, lectures online, video conference lectures and lecture web applications on android. Chatbot will automatically answer students' question in university web page by providing information and suggesting a proper answer suit to the question. This research will develop Chatbot type based on text by applying Artificial Neural Network (ANN) algorithm . The applied data set while conducting the Chatbot coaching is the questions data which frequently being asked (FAQ) in the study web, 25 questions with its answer which is divided into 16 labels or classes. The testing is conducted by using 110 different conversations from the dataset but have the same intention. From those 110 conversation, the Chatbot succed in answering 107 questions precisely and made 3 wrong conversation. The testing result shows a good result by having 97,27% accuracy and 2,72% error.
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Wu, B., Wang, B. and Xue, H., 2016, December. Ranking responses oriented to conversational relevance in chat-bots. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (pp. 652-662).
Wu, Y., Wu, W., Xing, C., Zhou, M. and Li, Z., 2016. Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots. arXiv preprint arXiv:1612.01627.
Chandra, Y.W. and Suyanto, S., 2019. Indonesian chatbot of university admission using a question answering system based on sequence-to-sequence model. Procedia Computer Science, 157, pp.367-374.
Rizki, U., 2019. Multi Respon Ranking Pada Percakapan Layanan Travel Berdasarkan Riwayat Obrolan. Jurnal Informa, 5(3), pp.73-79.
Bhagwat, V.A., 2018. Deep Learning for Chatbots., San Jose State University, 2018.
Jwala, K., Sirisha, G.N.V.G. and Raju, G.P., 2019. Developing a Chatbot using Machine Learning. International Journal of Recent Technology and Engineering (IJRTE), 8(1S3), pp.89-92.
Imamah, F. and Dores, A., 2018. Aplikasi chatbot (milki bot) yang terintegrasi dengan web CMS untuk customer service pada UKM MINSU. Jurnal Cendikia, 16(2 Oktober), pp.100-106.
Trivedi, A., Gor, V. and Thakkar, Z., 2019. Chatbot generation and integration: A review. International Journal of Advance Research, Ideas and Innovations in Technology, 5(2), pp.1308-1311.
Nagarajan, S.K., 2019. Server-Less Rule-Based Chatbot Using Deep Neural Network.
Wibawa, M.S., 2017. Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan. Jurnal Sistem dan Informatika (JSI), 11(2), pp.167-174.
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