The Implementation of Deep Learning Techniques in Developing Conversational Chatbot as The Source of Vaccination Information

Q3 Engineering
Yuliska Yuliska, Nina Fadillah Najwa, K. U. Syaliman
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Abstract

The Covid-19 pandemic has hit Indonesia for more than 2 years. To overcome Covid-19, Indonesian government implemented a vaccination program with a target of 70% of the population being vaccinated. However, the recorded population that has been vaccinated to reduce the risk of being exposed to Covid-19 is still low. Several studies have stated that information and invitations to vaccines through mass media are considered insufficient to convince the population to vaccinate. Residents who are still unsure and do not even want to vaccinate need really comprehensive information from experts. To answer this problem, a chatbot that can replace experts in explaining everything related to vaccines can be one solution. This is evidenced by a study which states that the interaction between people who have not been vaccinated with a chatbot that explains about vaccination can reduce the level of doubt of the population about the vaccine by up to 20%. The purpose of this research is to build a chatbot using deep learning technique. Meanwhile, the deep learning technique used to build a conversational chatbot is the Multilayer Perceptron Network (MLP). Based on the result of our study, our chatbot can answer 83% questions correctly out of 30 questions.
深度学习技术在开发会话聊天机器人作为疫苗接种信息来源中的应用
新冠肺炎大流行袭击印度尼西亚已有两年多的时间。为了克服Covid-19,印度尼西亚政府实施了一项疫苗接种计划,目标是70%的人口接种疫苗。然而,为降低暴露于Covid-19的风险而接种疫苗的记录人口仍然很低。若干研究表明,通过大众传播媒介提供的信息和邀请接种疫苗被认为不足以说服民众接种疫苗。那些仍然不确定甚至不想接种疫苗的居民需要专家提供真正全面的信息。为了回答这个问题,一个可以取代专家解释与疫苗有关的一切的聊天机器人可能是一个解决方案。一项研究证明了这一点,该研究指出,未接种疫苗的人与解释疫苗接种的聊天机器人之间的互动可以将人口对疫苗的怀疑程度降低多达20%。本研究的目的是利用深度学习技术构建一个聊天机器人。同时,用于构建会话聊天机器人的深度学习技术是多层感知器网络(Multilayer Perceptron Network, MLP)。根据我们的研究结果,我们的聊天机器人在30个问题中可以正确回答83%的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.50
自引率
0.00%
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0
审稿时长
4 weeks
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