RANCANG BANGUN APLIKASI CHATBOT SEBAGAI LAYANAN INFORMASI JURUSAN MENGGUNAKAN NAMED ENTITY RECOGNITION
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
PRATAMA, IRFAN HANANDRA
Arianto, Rakhmat
Siswipraptini, Puji Catur
Arianto, Rakhmat
Siswipraptini, Puji Catur
Subject
Teknik Informatika
Datestamp
2022-09-22 06:14:55
Abstract :
The chatbot application can be used to help provide infomration needs just by sending a texc message to the bot. In this study, chatbot is used as an information service for informatics engineering majors at PLN Technical College Jakarta. Named Entity Recognition (NER) is used to identify keywords from human language. The keyword pattern is matched to a pattern in AIML (Artificial Intelligence Markup Language) to provide relevant answers. The named entity recognition of words in this study using Multinomial Naïve Bayes algorithm. Broadly speaking, there are two processes performed, namely the process of NER learning and chatbot process. Before performing the calculations, the text first passes the tokenizing process, part of speech tagging, BILOU tagging and feature extraction. Based on the test results, this chatbot app can recognize the named entity of a word with an average precision value of 88% and an average recall value of 85% and can provide a precise answer with an accuracy of up to 82% based on a pattern already found.Based test results, this chatbot app can recognize the named entity of a word with an average precision value of 88% and an average recall rate of 85% and can provide a precise answer with an accuracy of up to 82% based on a pattern already found.
The chatbot application can be used to help provide infomration needs just by sending a texc message to the bot. In this study, chatbot is used as an information service for informatics engineering majors at PLN Technical College Jakarta. Named Entity Recognition (NER) is used to identify keywords from human language. The keyword pattern is matched to a pattern in AIML (Artificial Intelligence Markup Language) to provide relevant answers. The named entity recognition of words in this study using Multinomial Naïve Bayes algorithm. Broadly speaking, there are two processes performed, namely the process of NER learning and chatbot process. Before performing the calculations, the text first passes the tokenizing process, part of speech tagging, BILOU tagging and feature extraction. Based on the test results, this chatbot app can recognize the named entity of a word with an average precision value of 88% and an average recall value of 85% and can provide a precise answer with an accuracy of up to 82% based on a pattern already found.Based test results, this chatbot app can recognize the named entity of a word with an average precision value of 88% and an average recall rate of 85% and can provide a precise answer with an accuracy of up to 82% based on a pattern already found.
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