Optimasi Kinerja E Government melalui Data Media Sosial Berskala Besar (Big Data)

Abstract

Advances in internet and communication technology underlie the growth of e-commerce, e-business applications and social media which are expected to contribute to increasing economic growth and community welfare, therefore the community needs to be involved in the development process through various technology initiatives to build services that focus on community needs. and provide better access to government services. Data has grown on a large scale and in various fields. Big data expressions are increasingly popular in the academic field, including research on awareness, adoption, and perceived usage in social media interactions. Big data can improve decision-making processes and increase organizational efficiency and effectiveness, but only if organizations use scientific methods to create knowledge about data. With the opportunity for the development of big data by utilizing social media, how to build a model to capture hot news as decision support in optimizing e-government. A new approach is proposed to identify the most popular news stories to identify hot topics accurately, which has four main parts, namely a method is proposed to identify new topics separated by word segmentation algorithms in news according to community service, utilization of time distribution of use of topics. identified topics to increase the IDF score, calculate the average weight for each hot news topic, offer a model for finding hot news or viral news related to government services in the community. So in this study, a model called DS TF IDF was produced in capturing patterns, trends, hot news which was developed by calculating the weight value of hot news about government services experienced by the community based on a certain period on social media platforms. As a comparison with other dimensions of social media, the average weight of hot news is calculated from a number of social media platforms and the attention used to regulate the distribution of time and distribution of public attention to the importance of developing news or information.