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Journal : IJIIS: International Journal of Informatics and Information Systems

Implementing Machine Learning Techniques for Predicting Student Performance in an E-Learning Environment Adi Suryaputra Paramita; Laura Mahendratta Tjahjono
International Journal of Informatics and Information Systems Vol 4, No 2: September 2021
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v4i2.112

Abstract

The pandemic of COVID-19 has altered the way people learn. Learning has moved from offline to online throughout this pandemic. Predicting student performance based on relevant data has opened up a new field for educational institutions to improve teaching and learning processes, as well as course curriculum adjustments. Machine learning technology can assist universities in forecasting student performance so that necessary changes in lecture delivery and curriculum can be made. The performance of the pupils was predicted using machine learning techniques in this research. Open University (OU) educational data is examined. Demographic, engagement, and performance metrics are used. The results of the experiment. The k-NN strategy outperformed all other algorithms on the OU dataset in some circumstances, but the ANN approach outperformed them all in others.
Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm Marco Febriadi Kokasih; Adi Suryaputra Paramita
International Journal of Informatics and Information Systems Vol 3, No 2: September 2020
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v3i2.65

Abstract

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.