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The government may use social media, such as Twitter, to socialize a policy or a program to society. We may predict whether a program is successful or not by analyzing the sentiment of societies towards such program or communities through their tweets. The latest program of Indonesia's government during the COVID-19 pandemic is to make people do social distancing. It is socialized using the hashtag of stay at home appeal (#dirumahaja). The objective of this study is to analyze the understanding of societies regarding this program through people's tweet. We compared two classification algorithms (Naïve Bayes and Random Forest), using tokenization and unigram features to build classification model of tweet sentiment. The tweets that included some hashtags regarding social distancing program, were collected with 5101 tweets in total. The highest accuracy is obtained using the Random Forest algorithm and term weighting feature, which yielded 95.98%. From the model we found that the number of positive sentiments is greater than the negative sentiment. Which can be concluded that the societies are understand and agree to the social distancing program.

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Information system architecture of Directorate General of Tax (DGT) is centralized with distributed data. The main problem are replication of master and reference data which spread among applications which vary on data structure and the synchronization jobs that spread in many locations and not well managed. Therefore, Master Data Management (MDM) needs to be implemented with accordance to characteristic of centralized distributed information system. Master data management maturity evaluation is conducted using MDM maturity model (MD3M) Spruit dan Pietzka, the result is Data Protection, Data Quality and Maintenance topic have maturity level 3 or defined process stage, while Data Model, Usage and Ownership topic have maturity level 2 or repeatable stage. Solutions to solve MDM issues and to enhance the master data management maturity level are proposed based on Data Management Body of Knowledge (DMBOK). DGT’s MDM issues are related to insufficiency of policy and architecture of MDM system. Policy and architectural approach of centralized MDM system is required to solve that issues. Proposed solution involves the use of data virtualization to enable implementation of centralized system of MDM without consolidate all master and reference data into new database.

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