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The Social Security Administering Body (BPJS) is a facility established by The government in providing services to citizens in The field of health welfare. The Spirit of cooperation in the utilization of health services which is very much currently a constraint in the budget is still insufficient in covering health services as a whole. For this reason, government policy is following with PERPRES No. 75 in 2019, the Government officially raised the BPJS Health contributions for 2020. The increase in BPJS Health contributions certainly caused a lot of comments. Namely Twitter, one of the social media that is used by the public to express disapproval or support for this government policy. This study, testing was carried out related to the prediction of comments from social media on community responses to the increase in BPJS Health contributions taken by the government. In the test carried out 3 (three) input algorithms. For every single algorithm including getting results through the K-NN method with an accuracy of 71.83% and AUC value of 0812, for the Naïve Bayes method produces an accuracy of 81.63% and AUC value of 0586. As for the C 4.5 method, the accuracy is 65.37% and the AUC value is 0628. While testing conducted through the Ensembles Vote method which combines the 3 algorithms above gives the best results with an accuracy of 80.10% and AUC value is 0871 for Twitter comment predictions.

This paper aims to provide an overview based on the literatures regarding the development of the study of policy dynamics research in the field of public administration. The study aims to answer the research question, "What is the theoretical and conceptual framework of "policy dynamics" in the contemporary discourse of current public administration?". The method used is a bibliometric analysis and semi-systematic literature review in the form of a literature review of international publications published between 1990 and 2020, sourced from the Google Scholar database. The sixty articles that are most relevant in terms of Article title, Abstract, and Keywords (journal articles: 55 papers and Conferences proceedings: 5 papers) are chosen for analysis. As a result, policy dynamics can be grouped into three categories: policy dynamics as historical institutionalism, policy dynamics as policy change, and policy dynamics as policy evolution. This study concludes that policy dynamics is the evolution of policy changes, where these changes have implications for the policies of a regime/government on institutions, actors, and systems within a certain period. This concept manifests in the five new clusters in the definition of policy dynamics, which include regime/government change, institutional change/transformation, changes in issue, direction and content of policy, actor's role and existence, and policy object inputs and outputs.

The Indonesian Government Budget Appropriations or Outlays for Research and Government (GBAORD) has been analyzed manually every year to measure the government expenditures in research and development. The analysis process involved several experts in making the budget classification. This method, commonly known as manual classification, has its downsides, which are time consumption and inconsistent result. Therefore, a study about implementing the machine learning method in GBAORD budget classification to avoid inconsistency is proposed in the previous research. For further analysis, this paper evaluates the performance of the Naïve Bayes algorithm for the GBAORD budget classification. This paper aims to measure the robustness of the Naïve Bayes to classify GBAORD data taken from 2017 until 2019. This paper uses three models of Naive Bayes with different preprocessing methods and features. This paper concludes that using the Naïve Bayes algorithm in Indonesian GBAORD budget classification is suitable since the robustness of the algorithm is proved to be high with 96.788+-0.185% average accuracy.