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Data science is a science that is currently very popular and can be applied in various industries. Of course, the need for proficient people in this field is growing in demand by many businesses, including large corporations. Data science is a science that combines expertise in a particular field of science with programming, mathematics, and statistics skills. The goal is to extract knowledge or information from data. The problem that usually occurs in the data science process is at the data modeling stage, namely the accuracy of the mathematical model that will be used to obtain the estimated parameter values. To overcome this problem, this research was conducted to test the correctness of analytic derivatives in mathematical models and conduct data modeling using analytic derivatives that have been tested to obtain the estimated value of each parameter. The validation process is carried out using an analytic and numeric gradient comparison. If the comparison of the two values is close to zero, then the analytical derivatives used are correct. The parameter estimation method used is the Maximum Likelihood Estimation. In this research, it was concluded that the value of the analytic gradient is the same or close to the numeric gradient value for each parameter so that the analytic derivatives are correct and can be used for the next stage. With the results of this research, the expected implication is to get more accurate data modeling so that the conclusions obtained are more precise. [ABSTRACT FROM AUTHOR]

The role of higher education in Indonesia is an essential key in determining the ability of the Indonesian nation to continue to make progress and create prosperity for all Indonesians. A country's progress in catching up with other countries also depends heavily on two other factors, namely the quality of institutions and the availability of infrastructure. The universities must create the next generation of the nation that can lay the foundation for Indonesia to become a developed country. However, since the Covid-19 pandemic, this situation has dramatically affected the learning process in universities. To prevent the spread of Covid-19 in Indonesia, the government has implemented a hybrid learning system for higher education levels. This hybrid system requires Internet access by students and faculty to help support the learning process from campus. However, the current hybrid system has not effectively and efficiently replaced formal education. Therefore, the research team took the initiative to conduct this study to observe the impact of the Covid-19 pandemic on learning outcomes through regression analysis with single and multiple predictors. The method used is regression analysis, where the parameter estimation uses the Maximum Likelihood Estimation. The regression model with single and multiple predictors resulted in the same conclusion. The relationship between the predictor variables and the responses was positive. With the results of this study, the expected implication is the utilization of the developed model to obtain policy-making scenarios by the university to achieve the targets. [ABSTRACT FROM AUTHOR]

Nowadays, the web platform is famously used to deliver information and to support trade activity in the marketplace online. Increasing internet users can be an opportunity for business people, especially for sellers that sell used cell phones. Unfortunately, the traditional market still conventionally sells their phone, which has shortcomings, i.e., the product cannot be promoted extensively to prospective customers. To overcome this problem, this study proposes the business idea of web-based platform technology that can use by sellers to promote their used cell phones in the online market. The business idea can also give detailed information related to the product so buyers' perspectives can get more trust from the seller, and the product can be accessed from anywhere through internet media. The system that we propose also provides information service to maintenance of mobile phone, so when the customers have problem with their phone can fix with the vendor provided on the website. [ABSTRACT FROM AUTHOR]

The relationship between dependent variable and independent variables can be described using linear regression model. The model equation is called global model. But if the data is a spatial data, where the differences of locations affect the model, then global model is no longer relevant. One of method can be used to analysis for spatial data is Geographically Weighted Regression (GWR). GWR is local model that represents model for each location. DKI Jakarta is a province with the highest human development index value in Indonesia. The HDI value is calculated based on some basic components of quality of life. In this paper, we analyze the HDI using multiple linear regression and we get a global model. But because DKI Jakarta consists of some regions with different condition, then we also used spatial data analysis methods. Then we apply GWR method to analyze the HDI in Jakarta. On the GWR model acquired local models for each location. We compared two models of global and local, the results showed that the factors that affect HDI in every region at DKI Jakarta are different. Based on the analysis by linear regression and GWR, the results show that the SSE of GWR model is smaller than linear regression model. So the GWR model is better than linear regression model. [ABSTRACT FROM AUTHOR]