The Utility of Decision Tree and Analytics Hierarchy Process in Prioritizing of Social Aid Distribution due to Covid-19 Pandemic in Indonesia

The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status.

Ensemble Learning for Sentiment Analysis on Twitter Data Related to Covid-19 Preventions

The World Health Organization (WHO) declared the 2019 Coronavirus disease outbreak (Covid-19) as a pandemic and made it a trending topic on social media platforms, such as Facebook and Twitter. Unfortunately, news and opinions shared on social media affect people's mentality and create panic situations in society, but in the other hand, these opinions can be analyzed using sentiment analysis approach to generate knowledge and insight for the local government to monitor people reaction to the policies that have been issued to prevent the outbreak of Covid-19 virus. Therefore, this work aimed to propose an ensemble learning model that can classify the sentiment inside the people's opinions from Twitter. The ensemble model used Naïve Bayes Classifier, C4.5, and k-Nearest Neighbors as base learners with voting mechanism to generate the final decision. For learning, the ensemble model used a dataset containing 3884 clean data that was successfully downloaded using Twitter API related to Covid-19 outbreak prevention and processed using TF-IDF method. The dataset has two classes, i.e., 'positive' and 'negative' to represent the sentiment of the opinion in each data. The proposed model got 80.61% of accuracy, 79.49% of recall, and 81.20% of precision, after being evaluated using 10-fold Cross Validation. It also performed better when compared to several learning models using only single Machine Learning algorithm. © 2022 IEEE.