Integrating Information Gain methods for Feature Selection in Distance Education Sentiment Analysis during Covid-19

Sentiment analysis is a way to automatically understand and process text data to figure out how someone feels about an opinion sentence. If there are too many reviews, it will take a lot of time and they will start to be biased. Sentiment classification tries to solve this problem by putting user reviews into groups based on whether they are positive, negative, or neutral. The dataset comes from Drone Emprit Academic. It is made up of tweets with the words "online learning method" in them, with as many as 4887 data crawled from them. Information Gain and adaboost on the C4.5 (FS+C4.5) method are used in the feature selection method. We use feature options to get rid of bias and improve accuracy. The results of the experiments will be compared to other algorithms like C4.5 and random forest. Based on the results, the accuracy of the two standard decision tree models (C4.5 and random forest) went up from 48.21% and 50.35% to 94.47 %. The value of how accurate it was went up by 44 percent. The FS+C4.5 model, on the other hand, has an RMSE of 0.204 and a correlation of 0.944. So, adding the feature selection technique to the sentiment analysis of bold learning education can make the C4.5 algorithm even more accurate © 2023 Syamsu Rijal et al;published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License

Licensing services in the era of the COVID-19 pandemic

The licensing service system tries to be integrated electronically to keep up with technological developments. With the occurrence of COVID-19 in Indonesia, the bureaucracy is expected to have a service system based on information technology. This research method is through literature review by looking at the permit service system phenomenon in Indonesia, with data sources through online media, articles, and proceedings. The study results describe the licensing service system in Indonesia with the COVID-19 pandemic showing an increase in the application of information technology in each local government. The accelerating application of technology to the bureaucracy indicates that there has been significant bureaucratic reform in the government in Indonesia, especially during the COVID-19 pandemic. © IEOM Society International.