Modified K-Nearest Neighbor Web Based Chatbot as Service and Academic Information School

Main Article Content

Natanael
Viny Christanti Mawardi

Abstract

At a time when everything is done on an Online basis starting from office activities, teaching and learning activities especially administrative activities school. Where in the activity, aninformant needs to be can serve academic and non-academic information needs for para parents of students and other parties. Chatbot is one of the means to overcome these needs by presentingfeatures that allows for someone to ask when and where. Therefore, in the conclusion that chatbot can be a public means especially on schools - schools that can implement their uses as one school information sources other than the school's business. This Chatbot made using the Modified K-Nearest Neighbor method or can be briefed MKNN. MKNN is the latest version of the previous method, K-Nearest Neighbor where in the application MKNN method does the process additional after Euclidean Distance calculation, Weight Voting. Premise from the weight voting method itself is the biggestweight calculation, so chatbot will calculate the value of the shortest distance to the database from the chatbot then after completion, the chatbot will calculate the value weight or information from that distance so that the results that can be from this are the shortestdistance to the data class that is being addressed by carrying the weight of information biggest or most. This is used to facilitate the deep chatbot determine the category of a database so that it can provide answers that more certain and accurate.

Article Details

Section
Articles

References

Vamsi, G. K., Rasool, A., & Hajela, G. (2020, July). Chatbot: A deep neural network based human to machine conversation model. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.

Miralay, F. (2020). Evaluation of distance education practice in 2020 Covid 19 pandemic process. Near East University Online Journal of Education, 3(2), 80-86.

Lee, Y. C., Yamashita, N., Huang, Y., & Fu, W. (2020, April). " I hear you, I feel you": encouraging deep self-disclosure through a chatbot. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-12).

Duijst, D. (2017). Can we improve the user experience of chatbots with personalisation. Master's thesis. University of Amsterdam.

Parvin, H., Alizadeh, H., & Minaei‐Bidgoli, B. (2009, May). Validation Based Modified K‐Nearest Neighbor. In AIP Conference Proceedings (Vol. 1127, No. 1, pp. 153-161). American Institute of Physics.

Jesslyn, J., Mawardi, V. C., & Hendryli, J. (2021). Penerapan Teknologi Dalam Percakapan Virtual Sebagai Sarana Pembelajaran di Sekolah Dasar Immanuel. PROSIDING SERINA, 1(1), 1479-1488.

Ciayandi, A., Mawardi, V. C., & Hendryli, J. (2020, December). Retrieval based chatbot on tarumanagara university with Multilayer Perceptron. In IOP Conference Series: Materials Science and Engineering (Vol. 1007, No. 1, p. 012146). IOP Publishing.

Sianipar, Y. P., Mawardi, V. C., & Sutrisno, T. (2022). PENGGUNAAN APRIORI PADA REKOMENDASI PAKET MENU DAN DILENGKAPI FITUR CHATBOT. Jurnal Ilmu Komputer dan Sistem Informasi, 10(1).

Lewis, H. G., & Brown, M. (2001). A generalized confusion matrix for assessing area estimates from remotely sensed data. International journal of remote sensing, 22(16), 3223-3235.

Kohavi, R. (1995, August). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai (Vol. 14, No. 2, pp. 1137-1145).

Gazalba, I., & Reza, N. G. I. (2017, November). Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. In 2017 2nd international conferences on information technology, information systems and electrical engineering (ICITISEE) (pp. 294-298). IEEE.