Authentication is the act of confirming the validity of someone's personal data. In the traditional authentication system, username and password are sent to the server for verification. However, this scheme is not secure, because the password can be sniffed. In addition, the server will keep the user's password for the authentication. This makes the system vulnerable when the database server is hacked. Zero knowledge authentication allows server to authenticate user without knowing the user's password. In this research, this scheme was implemented with Guillou-Quisquater protocol. Two login mechanisms were used: file-based certificate with key and local storage. Testing phase was carried out based on the Open Web Application Security Project (OWASP) penetration testing scheme. Furthermore, penetration testing was also performed by an expert based on Acunetix report. Three potential vulnerabilities were found and risk estimation was calculated. According to OWASP risk rating, these vulnerabilities were at the medium level.
Adopting technology could give competitive advantage and positively impact the hotel's profitability, thus hotels should keep up with the latest hotel technologies. An important part in the hotel services is the customer service. A problem with the human-to-human customer services today is a long time in answering customers query. On the other hand, nowadays customers need easy and effective services. Thus, a chatbot is required to answer consumers' issues automatically which leads to higher customer satisfaction and a growing profit. Because of the need and there is still an absence of chatbot for hotel industry in Indonesia, this study is conducted. The chatbot for hotel industry in Indonesia, named Bershca, has been successfully developed using artificial intelligence markup language (AIML) to construct the knowledge. Google Flutter is used for the system's front-end, while Python is used for the back-end of the system. As a text-preprocessing method, Nazief-Adriani Algorithm is implemented in the system's back-end. The system is evaluated using technology acceptance model (TAM). As a result, 85.7% of the respondents believe that using chatbot would enhance their job performance and 84.33% of the respondents believe that using the technology would be free of effort.
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