Disagreement on social media and stock trading volume: The Indonesian context

This research intends to test the relationship between disagreements on social media and stock trading volume using the Indonesia Stock Exchange (IDX) as a research object. The Covid-19 pandemic has made the use massively of social media to invest in Indonesia's capital market There has been an increasing number of investors in the IDX. They trade and discuss stocks online. The research question is whether the information on social media has worhted for Indonesian investors. Research on the relationship between social media features and stock market features, especially using trading volume, has never been done in Indonesia. To do this, we tested the influence that the number of posts and disagreements on Telegram social media has on stock trading volume in IDX. The test was done using multivariate regression method. The results show that discussions on social media have a positive and significant effect on stock trading volume, while disagreements do not significantly affect it. © Foundation of International Studies, 2022 © CSR, 2022.

Evaluation of YOLO-X and MobileNetV2 as Face Mask Detection Algorithms

Even though COVID-19 still exists, people are more reluctant to wear masks in public places, in fact only 73% of Indonesian still do. Hence, automatic mask surveillance in public places is still needed. In this paper, we compare two algorithms named YOLO-X and MobileNetV2 to detect face masks. YOLO-X was able to outperform other YOLO algorithms in object detection. While, according to researchers, MobileNetV2 achieved 9S% in face mask detection. To fairly evaluate both algorithms we need to conduct research under controlled variables including using the same datasets and devices. We used public datasets which consists of 1493 mask images and 6451 non mask images for training and testing. The results show that YOLO-X outperforms MobileNetV2 as it achieves 95.0%, 98.7%, 93.7%, and 96.1% for accuracy, average precision, recall, and F1-score respectively. YOLO-X also performs better in detecting faces with occlusion such as glasses, hands, and postures than MobileNetV2. However, YOLO-X detects faces and face masks 31.9% slower than MobileNetV2. © 2022 IEEE.

Sentiment Analysis of the PeduliLindungi on Google Play using the Random Forest Algorithm with SMOTE

At the end of 2019, the world was hit by the COVID-19 virus, which caused a pandemic. Indonesia has become one of the countries that are affected by this pandemic. To control the COVID-19 pandemic, the government has made various efforts, one of which is the use of the PeduliLindunig app. To access the PeduliLindungi app, the public can download it from Google Play. Google Play enables its users to write reviews on the apps that have been downloaded. This study aims to determine the sentiment analysis on the PeduliLindungi application on Google Play using the Random Forest Algorithm with SMOTE. Based on this study, public sentiment towards the PeduliLindungi app on Google Play tends to be negative. The Random Forest and SMOTE algorithms are used to classify sentiment in this study. The implementation of Random Forest and SMOTE resulted in 71% accuracy, 70% recall, and 70% precision. © 2022 IEEE.

Physical Distancing Detection using YOLO v3 and Bird's Eye View Transform

One regulation that has been established by governments in most countries to curb the spread of Covid-19 is physical distancing. However, many people still ignore the importance of this regulation. Thus, it is important to develop a system that can help enforcing this regulation. In this paper, we propose a system that can automatically detect the presence of humans in a video frame and measure their distances from each other. Object detection is performed using YOLO v3 and the accuracy of distance measurement is enhanced using Bird's Eye View Transformation. Our experiments show that using this transformation yields an accuracy improvement of up to 20.93% compared to the performance of the system without transformation (i.e., from 74.42% to 95, 35% accuracy). © 2021 IEEE.

Optimized Deep Transfer Learning for Covid-19 Screening using Chest X-Ray Image

The Covid-19 pandemic continues to spread at an alarming rate. One of the methods used to screen potential Covid-19 infected patients is analysis of chest x-ray images. However, the sheer number of patients may overwhelm the radiologists that have to perform such analysis. Therefore, it is desirable to perform automatic screening to provide Covid-19 positive cases as early as possible. Inthis study, transfer learning by using pre-trained deep residual network model was implemented to perform the binary classification between Covid-19 infected persons and normal (ie., non-infected) persons. We also use automatic cropping of the input images to focus on lung area to optimize the learning performance. Our experiments show that this approach yields better performance achieved, achieving accuracy rate of 99.35% compared to an accuracy rate of 98.08% without application of automatic cropping. The performance of the proposed system when used to classify images taken from a dataset that are completely different from those used in the training process is also satisfactory. Thesystem only produces 1 false negative (out of a dataset containing 66 images) with automatic cropping compared to 3 false negatives and one false positive without cropping. These results show that the pre-trained model with automatic cropping gives superior performance and is suitable to be used in automated Covid-19 screening based on chest X-ray images. © 2020 IEEE.