Rahmatulloh, Alam and Ginanjar, Anjar and Darmawan, Irfan and Kurniati, Neng Ikan and Haerani, Erna Chatbot for Diagnosis of Pregnancy Disorders using Artificial Intelligence Markup Language (AIML). INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION.
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1. Chatbot for Diagnosis of Pregnancy Disorders using Artificial Intelligence Language (AIML).pdf Download (3MB) |
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2.A1-Korespondensi Chatbot for Diagnosis of Pregnancy Disorders using Artificial 31 Maret 2023.pdf Download (52kB) |
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2.A1-Similaritas gnosis_of_Pregnancy_Disorders_using_Artificial_31_Maret_2023.pdf.pdf Download (2MB) |
Abstract
Artificial Intelligence has evolved in sophistication and widespread use. This study aims to create a chatbot application in the health sector regarding the early diagnosis of pregnancy disorders. Based on basic health research, only 44 percent of pregnant women know the danger signs of pregnancy. The chatbot application developed is expected to facilitate and increase knowledge for pregnant women about the danger signs of pregnancy, especially early diagnosis of pregnancy disorders. The chatbot application was developed with artificial intelligence technology based on Artificial Intelligence Markup Language with the question-answer concept using the Pandorabots framework. The test is carried out in two stages: functional and pattern matching. The functional testing uses the black- box testing method, and the pattern-matching test on the chatbot uses the sentence similarity and bigram methods based on user input and keywords similarity in the bot's knowledge base. The functional testing results show that the chatbot application runs well, with the eligibility criteria reaching 81.4% and the results of the keyword similarity test (pattern matching) are zero to one, in the sense that the value of one has the same similarity between user input and pattern. Meanwhile, the zero value has no similarities, so the bot will respond to it as free input. So it can be concluded that the bot can respond to user questions when the pattern and input have the same level of similarity.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Teknik > Informatika > Artikel Dosen Informatika |
| Depositing User: | Mrs Linda Amelia Oktavia |
| Date Deposited: | 15 May 2023 02:50 |
| Last Modified: | 15 May 2023 02:50 |
| URI: | http://repositori.unsil.ac.id/id/eprint/9181 |
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