Pengaruh Reputasi Underwriter, Financial Leverage, Profitabilitas dan Ukuran Perusahaan terhadap Underpricing Saham IPO di Bursa Efek Indonesia (BEI) Periode 2022

The aim of this research is to determine the influence of underwriter reputation, financial leverage, profitability and company size on the underpricing of IPO shares on the Indonesian Stock Exchange (BEI) for the period 2022. This research is quantitative research that uses secondary data. The population in this study was 59 companies that will IPO in 2022. The total sample was 46 companies using purposive sampling techniques. Data analysis uses time series data regression with the help of SPSS 25. The research results show that the underwriter's reputation influences share underpricing with a significant value of 0.046 < 0.05. Financial leverage influences stock underpricing with a significant value of 0.049 <0.05. Profitability influences stock underpricing with a significant value of 0.003 < 0.05. Company size has no effect on stock underpricing with a significant value of 0.913 > 0.05.

Automated scratch detection syseem for the automative industry.

Following the manufacturing process within an automotive industry, a vehicle is prone to defects being present on its body. In the Pakistani industry, one of the largest contributors to these defects are scratches. The current measure being taken to detect scratches on vehicles involve the presence of trained specialists who inspect the body under specific lighting conditions. For this very reason, a low cost and effective automated system is desired which is capable of detection scratches on the front bumper of vehicles on the production line of industries. The main features of this system should its ability to pinpoint a range of areas where the scratches originate from in the manufacturing process as well as to help improve the current manual inspection system in place. To tackle the problem at hand, we propose a scratch detection device, based on a microcontroller interfaced with a distance sensor and a camera module, set up on a robotic arm which is mapped to the front bumper of a vehicle. The system is placed on the manufacturing line. It detects the presence of a vehicle through the threshold being broken on the distance sensor. It then begins the mapping process and takes images of the bumper which it then sends to the image processor located on the microcontroller of the device. The image processor loads the images and begins to preprocess them. This involves the removal of unwanted objects, the removal of reflections of surfaces, etc. It then passes the preprocessed image onto a trained machine learning model which outputs a decision stating whether a scratch is present or not. This data is then logged onto a spreadsheet online which is accessible by the human inspector. The trained machine learning model is based on a Convolutional Neural Network that is trained on a image dataset of around 1500 images of scratches and non-scratches. This model has an accuracy rate of 90% and is capable of telling whether a scratch is present in an image or not. The Robotic Arm has 6 degrees of freedom, allowing it to move in all four directions in a spacial plane as well as extend further into space. This is done to take into account the curved body of a front bumper. The proposed implementation is to place two such Automated Scratch Detection Systems on two sides of the vehicle at a distance of 16 centimeters from the body. This ensures optimal processing time as well as detection accuracy. Each of the systems map towards one half of the bumper and perform the image processing task in a total of 12 minutes