Purpose: This research aims to develop a classification model using histogram-based gradient boosting to identify relevant contextual tweets about disasters. This model can then be used for subsequent data cleaning stages.
Methods: This study uses a semi-supervised approach to develop a classification model using histogram-based gradient boosting. The model is trained to identify and remove irrelevant tweets that are related to disasters and gathered from Twitter. Optimization techniques, such as the AdaBoost classifier, calibrated classifier, and self-training classifier, are used to enhance the model's performance. The goal is to accurately recognize and categorize relevant tweets for additional data analysis and decision-making.
Result: The classification model that has been developed has achieved a high F1-score of 93.07%, which indicates its effectiveness in filtering disaster-related tweets that are relevant. This highlights the potential of the model to enable more precise aid distribution and faster decision-making in disaster response efforts. The successful implementation of the model also demonstrates its usefulness in utilizing social media data to enhance disaster management practices.
Novelty: This research contributes to the analysis of social media through machine learning algorithms. By utilizing social media, specifically Twitter, as a valuable resource for disaster response efforts, this study tackles challenges related to data collection and analysis in disaster management. The classification of relevant tweets into different types of natural disasters offers opportunities to enhance stakeholder decision-making processes in disaster scenarios.

This study aims to discuss the effect of earnings per share (EPS) and dividend policy on firm value. Firm value is used as a measure of the success of a company. By increasing the value of the company can be interpreted as an increase in the prosperity of company owners or shareholders in the company. The population of this research is 43 companies in the Food and Beverage Sub-Sector Companies listed on the Indonesia Stock Exchange in 2019-2021. By using the purposive sampling method, a sample of 18 companies was obtained. The data used in this study is secondary data obtained from the company's annual report. The research method used is Multiple Regression Analysis to see the effect of the independent variables on the dependent variable using the SPSS version 16 program. The results of the partial t test show that earnings per share (EPS) has no significant effect on firm value. Dividend policy partially has a significant effect on firm value and the results of the simultaneous F-test show that the earnings per share variable and dividend policy have a significant effect on firm value variables.