Penggunaan Named Entity Recognition dan Artificial Intelligence Markup Language untuk Penerapan Chatbot Berbasis Teks

Authors

  • David Christianto Faculty of Informatics Engineering, Institut Teknologi Harapan Bangsa
  • Elisafina Siswanto Faculty of Informatics Engineering, Institut Teknologi Harapan Bangsa
  • Ria Chaniago Faculty of Informatics Engineering, Institut Teknologi Harapan Bangsa

DOI:

https://doi.org/10.61769/telematika.v10i2.130

Keywords:

Chatbot, NER, AIML, Natural Language Processing, Naïve Bayes, Spelling Correction, Pattern Matching

Abstract

Aplikasi chatbot dapat digunakan untuk membantu memberikan kebutuhan informasi pada sistem layanan operator service. Sistem chatbot yang digunakan adalah sistem chatbot yang berbasiskan pada teks. Pada penelitian ini, chatbot dibuat untuk memenuhi kebutuhan informasi di ITHB dengan menggunakan Named Entity Recognition (NER) dan Artificial Intelligence Markup Language (AIML). NER digunakan untuk membantu  mengenali pola (kata kunci) kalimat dari bahasa sehari-hari manusia (Natural Language Processing). AIML digunakan untuk memberikan jawaban yang relevan dan sesuai  dengan pola (kata kunci) kalimat yang telah ditemukan di dalam bahasa manusia.  Selain itu, pada penelitian ini juga dilakukan beberapa optimasi seperti optimasi pada proses perhitungan Naïve Bayes pada NER, proses spelling correction, dan proses pattern matching yang terbukti dapat mempercepat dan meningkatkan akurasi sistem chatbot dalam proses pencarian jawaban. Berdasarkan hasil pengujian, sistem chatbot ini dapat mengenali pola kalimat bahasa manusia dengan akurasi (NER) hingga 97% dan sistem dapat memberikan jawaban yang tepat dengan akurasi hingga 90% berdasarkan pola yang telah ditemukan tersebut.

In operator service system area, information is an essential needs for every individuals. Chatbot application can be used to support the fulfilment of information in operation service system. Chatbot system that will be implemented is a text-based chatbot system. In this paper, chatbot was made in order to fulfil the information needs in ITHB by using Named Entity Recognition (NER) and Artificial Intelligence Markup Language (AIML). NER is used to recognize the sentence pattern (keyword) in human natural language (Natural Language Processing). AIML is used to process relevant responses based on the keyword patterns found in human natural language which then will be transformed into data which can be processed and understood by system. This research also covers several  optimizations, such as Naïve Bayes calculation optimization in NER, spelling correction optimization, and pattern matching optimization that has been proven to hasten and increase chatbot system’s accuracy in finding answers as response. Based on the empirical examination, this chatbot system can recognize human sentence pattern (NER process) with accuracy of 97% and system can provide suitable response with accuracy of 90% based on the recognized patterns from NER process.

References

I. Doumanis and S. Smith. Beyond Artificial Intelligence Markup Language (AIML) Rapid Prototyping of a Q&A system. Middlesex University, The Burroughs Hendon, London NW4 4BT, 2013.

A. F. Wicaksono, A. Purwarianti. “HMM based part-of-speech

tagger for Bahasa Indonesia,” in Proceeding of the Fourth

International MALINDO Workshop, 2010.

T. Nudd BA MSc. 2010. Introduction To Improving Your Communication Through Chunking Information. http://www.chunkmaps.com/nlp/ introduction. [21 Maret 2015].

D. Xhemali, Christopher J. Hinde and Roger G. Stone. 2009. “Naive Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages,” in IJCSI International Journal of Computer Science Issues, Vol. 4, No. 1, 2009.

Kamus Besar Bahasa Indonesia Online. http://pusatbahasa.diknas.go.id [10 Juni 2015].

Charlix. 2010. “Reverse AIML”. Internet: http://www.charlix. sourceforge.net. [7 Februari 2015]

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Published

2016-12-07

Issue

Vol. 10 No. 2 (2015)

Section

Articles

License

Copyright (c) 2016 David Christianto, Elisafina Siswanto, Ria Chaniago

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