Analisis Exploratory Kata “donasi” Akibat Pandemi Covid-19 Pada Media Sosial Twitter

Pandemi Covid telah memberikan banyak dampak negatif bagi umat manusia. Banyaknya dampak negatif tersebut telah mendorong masyarakat untuk saling membantu sesamanya. Tujuan yang ingin dicapai dalam penelitian ini melakukan analisis eksplaratori kata “donasi” sebagai representasi dari saling membantu di Tweeter. Analisis dilakukan dengan membandingkan tweet “donasi” 2 tahun sebelum pandemi dan 2 tahun saat pandemi. Selain tweet dalam penelitian ini juga akan dilakukan analisis terhadap replies,  Like, dan Retweet dari kata “donasi” tersebut. Setelah dilakukan analisis di dapat hasil bahwa tweet, replies,  Like, dan Retweet sebelum masa pandemi cenderung rendah dan stabil, sedangkan pada masa pandemi terjadi loncakan saat awal pandemi yaitu pada bulan Maret 2020 dan Mei 2021 saat puncak pandemi gelombang ke dua

A computational approach in analyzing the empathy to online donations during COVID-19

The COVID-19 pandemic has a negative impact on many aspects of life. The global economic downturn is one of these negative consequences. Nonetheless, even though everyone feels the threat of this pandemic for themselves, some people still have the empathy to help others. An empirical analysis of this empathy attitude is expected to be a catalyst in realizing a social force for the community to work together to combat this pandemic. This study will look at how people felt about donating during the COVID-19 pandemic on Twitter. The goals of this study are to (1) compare differences in donor desire before and during the COVID-19 pandemic using the developed model, and (2) determine whether there is a significant difference in empathy for donating before and during the pandemic. This study employs computational social science (CSS) techniques to achieve this goal. The data was obtained from Twitter using the keyword "donation" in the 24 months preceding the pandemic and in the 24 months following the pandemic's arrival in Indonesia. Data analysis includes hypothesis testing using Mann-Whitney and Cohen's D statistical tests, showing a significant increase in online donation support among Indonesian Twitter users since the COVID-19 pandemic hit. From the results of data processing data obtained 159.995 data in accordance with the criteria to be analyzed. From the results of the Mann-Whitney test, all variables showed significant results between before and during the Covid-19 pandemic and in the results of the Cohen's d test, all variables got a large effect size. From the results of the two tests, it can open Twitter social media users who have increased empathy to donate during the Covid-19 pandemic in Indonesi

The Improvement of Artificial Neural Network Accuracy Using Principle Component Analysis Approach

An important problem in a classification system is how to get good accuracy results. A way to increase the accuracy of a classifier system is to improve the number of input data attributes. Improving the number of input data attributes can be done using the Principal Component Analysis (PCA) method. The aim of this research is to reduce the number of input data attributes to increase the accuracy in a mushroom classification system. The research method used in this study started from collecting datasets from Kaggle.com related to mushroom-classification, then the data visualization process was carried out using pie charts then a dimension reduction process was carried out to reduce the number of variables using the PCA method. The next step is the training and testing of the artificial neural network. The architecture of artificial neural network used is backward error propagation with the number of hidden layers as much as 2 layers with the number of cells as many as 3 and 2. The training data used is 80%, while the testing data is 20%. Based on the test results, obtained an accuracy of 100% with 150,000 iterations and using 11 input variables from 22 existing input variables. By adding Principal Component Analysis part of the development that can improve the accuracy and performance of Artificial Neural Network