Monitoring Gadget Usage Behavior Among Adolescents Using Machine Learning

Abstract

The aim study has a long-term goal, namely to reduce negative impact of gadget use among adolescents. By giving awareness and the ability for teens to control the use of gadgets, adolescents are expected will be more productive and act as users of information technology intelligent. From economic products, the software developed can be marketed to various educational institutions such as junior high school or university, or where parents or schools will get a monitoring report on the use of gadgets from adolescents users. The method used in this study includes artificial intelligence techniques (machine learning) for various development models of text/speech / video/type classification user; User Centered Design techniques for application development; and multiple techniques social humanities such as desk study activities, focus group discussions, survey/questionnaire/interview. The results of the first year research to date are software development to monitor user behavior on the gadget, collecting user behavior data adolescents on gadgets, interviewing gadget use on teenage respondents, development. The hate learning model based on deep learning, the development of the rude classification model words based on deep learning and the development of Indonesian parsers.

Keywords

• adolescents
• machine learning
• monitoring behavior
• using gadget
• artificial intelligence