THE DEVELOPMENT OF AN AI-BASED MEDICINE INFORMATION SEARCH FEATURE IN THE POTIO APPLICATION
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Abstract
Potio was a mobile-based alarm application that functioned as a medication reminder for users who often forgot their medication schedules, which consisted of several features, such as adding alarms, medication history, themes customization, and medicine information research. In its development, this application used a MySQL database to store the results of inputting alarm data and medicine information. However, with the increasing amount of data that needed to be managed, researchers faced challenges in data management, because the very large number of medicines needed to be inputted manually, so it took longer and there was the potential for errors in data input. Therefore, the development of a medicine information search feature in the Potio application was carried out using one of the models, namely Gemini AI, by obtaining an Application Programming Interface key to be implemented into the medicine information search feature, followed by a prompt technique to provide effective output. The results of testing using Blackbox Testing with test cases showed that Potio successfully utilized Gemini AI for the medicine information search feature, eliminating the need for manual input of medicine information into the MySQL database.
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References
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