Lala Septem Riza

gradDescentR: An R package implementing gradient descent and its variants for regression tasks

Gradient Descent (GD) is one of famous machine-learning methods used for prediction on regression tasks in many fields. However, only a few software library utilizing it can be found in the literature. Therefore, this research is aimed to implement the method and the following variants: Mini-Batch Gradient Descent (MBGD), Stochastic Gradient Descent (SGD), and Stochastic Average Gradient Descent (SAG). The package is written in the programming language R, which is a software environment offering many facilities for statistics and machine-learning tools. Moreover, we provide the use of these approaches in a case study of gas industries, which is to predict values of the compressibility factor (Z-factor) of CO2. Basically, we consider the problem as a regression task including pressure and temperature as the input attributes. The first step is generating a learning model from available training data by using the methods. Then, the model is used to predict the Z-factor over new data. A performance comparison between the package and other methods from the literature is presented as well.

Analysis on atmospheric pressure, temperature, and wind speed profiles during total solar eclipse 9 March 2016 using time series clustering

Air temperature, pressure, and wind speed measurements on the surface taken during the Total Solar Eclipse (TSE) of March 9, 2016, are made. They were taken in Terentang Beach, Bangka Island, Indonesia. In this paper, we propose to analyze them by using time series clustering. The following steps are conducted: data collecting, splitting, smoothing, distance calculation, and clustering. The final results show cluster memberships of the three parameters on 3 time frames: one day before, the TSE day, and one day after. After doing some simulations, it can be seen that the profiles of temperature and pressure on the TSE day are on the same cluster while the wind-speed profile on the TSE day is the same as on the one day after.

Preface: International Seminar on Mathematics, Science and Computer Science Education (MSCEIS)

The vision of Education 2030 is to transform lives through education by ensuring “inclusive and equitable quality education and promote lifelong learning opportunities for all” (Sustainable Development Goal 4).In order to address the three inter-related issues of equity, quality and efficiency, there is a need to rethink teaching and learning in schools. This keynote address proposes 5 key strategies that could be implemented by education policy-makers and leaders to enhance equity, quality and efficiency. These strategies include: (1) the development of students’ 21 Century Competencies (including learning-how-to-learn competencies), (2) curriculum alignment, (3) the 7actor7y7on of digital technologies such as Blended Learning and eportfolios, (4) the capacity-building of teachers and their motivation, and (5) the establishment of partnerships and collaborations.