The University of Jambi (UNJA) currently manages the Single Tuition Fee (UKT) manually using Microsoft Excel, which causes various problems such as the length of the UKT determination process, data duplication, and difficulty in accessing the UKT determination history by the leadership.This research aims to develop UKT management features on the UNJA registration system to increase the effectiveness and automation of the UKT management process. System development is carried out using the Star Life Cycle Model method by implementing the features of the student UKT program proposal and uploading the required files. The research produced a prototype of a web-based registration system with a UKT Management feature that allows the process of determining and submitting a reduction / exemption of UKT to be carried out systematically. The implementation of this system is expected to overcome problems in managing UKT at UNJA such as duplication of UKT reduction which is detrimental to the institution in terms of finance.

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.