The Impact of Indonesia's Higher Education Program on Improving Students' Competence in Achieving Global Employability

Teachers are asked to adopt innovative teaching approaches and use cutting-edge technology in their classrooms. This method is deemed necessary as technological advancement is fast and educational institutions must be agile and adaptive. The Indonesian Ministry of Education and Culture has implemented the MBKM program focusing on building the students ' competencies through collaborative and participatory learning to increase global employability. The study was conducted at one of the reputable universities in Indonesia and gathered survey data from 5653 students, 523 faculty members, and 245 education personnel. The findings show that from implementing MBKM activities in student learning, 97% of student respondents stated that it is valuable enough to develop their competencies and skills as a provision to work after graduation. Therefore, this program is considered successful. However, this program 's socialization and dissemination of news need to be improved to increase program comprehension, especially among students, faculty members, and education personnel. Hence, the MBKM program is needed to prepare students socially, technologically and lead to better student competencies.

A Comparison: Prediction of Death and Infected COVID-19 Cases in Indonesia Using Time Series Smoothing and LSTM Neural Network.

COVID-19 is a virus causing pneumonia, also known as Corona Virus Disease. The first outbreak was found in Wuhan, China, in the province of Hubei on December 2019. The objective of this paper is to predict the death and infected COVID-19 in Indonesia using Savitzky Golay Smoothing and Long Short Term Memory Neural Network model (LSTM-NN). The dataset is obtained from Humanitarian Data Exchange (HDX), containing daily information on death and infected due to COVID-19. In Indonesia, the total data collected ranges from 2 March 2020 and by 26 July 2020, with a total of 147 records. The results of these two models are compared to determine the best fitted model. The curve of LSTM-NN shows an increase in death and infected cases and the Time Series also increases, however the smoothing shows a tendency to decrease. In conclusion, LSTM-NN prediction produce better result than the Savitzky Golay Smoothing. The LSTM-NN prediction shows a distinct rise and align with the actual Time Series data.