ABSTRACT
The COVID-19 outbreak caused a slowdown in the Indonesian economy, as it did in many other impacted nations. Consequently, the housing market in Indonesia, along with other industries, deteriorated. Other post-pandemic issues displace the property industry's priorities in Indonesia. Determining a fair property price is a problem occurring because of the economic slowdown. Property sellers expected their property selling prices to be the same before the pandemic or even increase, but property agents hoped the properties would be selling fast, creating a sense of distrust between the seller and the property agents. This work aims to develop a machine learning-based prediction model for real estate agents to use in determining property prices, with the expectation that the resulting predictions will be more accurate and supported by the data, increasing seller and buyer confidence. Following the suggestion from previous studies, several supervised algorithms such as Linear Regression, Decision Tree, and Random Forest were used to develop the model. Training data were collected from five property agents in Surabaya and as well as web scraping from the online home sales portals. Findings from the study show that Random Forest performs best in predicting with the highest coefficient of determination and lowest error. Using evaluation measures such as Mean Absolute Percent Error (MAPE), the error was calculated to be 23%, which is acceptable for prediction. © 2022 Science Press. All rights reserved.
ABSTRACT
As an alternative to learning in overcoming the COVID-19 crisis, online learning is carried out through learning applications and social media. This online learning requires teachers to effectively convey material from various subject matters, primarily material with experimental characteristics, such as natural science. For this reason, this study aims to analyze the online natural science learning implementation in elementary schools in Central Java Province during the COVID-19 pandemic. This exploratory study research used a qualitative descriptive approach, and the data were collected through a questionnaire. Respondents involved were teachers from grades 1 to 6, totaling 50 teachers from 15 regencies in Central Java who taught natural science learning in elementary schools. The data collection method in this study was in the form of a multiple-choice questionnaire, distributed by purposive sampling via a google form. Data analysis was then carried out with interactive analysis. The results revealed that scientific attitude was a supporting factor for students in mastering process skills efficiently. However, the study results also uncovered that the mastery of scientific attitudes in elementary school students was still low. It was due to several things: the use of text media more dominant than the natural environment as a process of seeking knowledge, low student motivation, broad material but limited teaching time, and the use of learning models that had not been able to adapt to the material characteristics and student needs. © 2021 ACM.
ABSTRACT
Online learning has been a demand in the education world. Some platforms are being utilized to support online learning in the Covid-19 pandemic. This research aims to analyse online learning during school from home (SFH) due to the Covid-19 pandemic as the challenge to realize the digitalization era in the education field in the 21st century. This research uses a quasi-experiment method. A quasi-experiment method conducts pre-test - post-test group designs. The populations of this research are all Elementary School teachers in Surakarta which consist of 166 Elementary Schools with a total of 851 teachers. Samples used in this research are the teachers of grade 2nd, 3rd, 4th, and 5th amount of 40 teachers. The samples have been decided through purposive sampling. There are 20 teachers including experiment classes i.e. Elementary Schools in Laweyan and Banjarsari district, amount 20 teachers including control classes i.e. Elementary School in Banjarsari and Pasar Kliwon district. In order to obtain the effectiveness of innovative online learning on the learning quality of the elementary schools, this research counts the normalized n gain score. The result of this research shows that the implementation of innovative online learning 0n the learning process in the elementary school can improve the quality of learning to accelerate the digitalization era in the education field in the 21st century during the Covid-19 pandemic. The learning quality has improved because the teachers implement innovation in online learning with Gain score of 0.72 that belongs to the high category. © 2020 ACM.