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All Journal Jurnal Manajemen dan Ekonomi Kreatif Ignite: Journal Islamic Global Network for Information Technology and Entrepreneurship
Qatrunnada Salsabila
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Pengaruh Leverage dan Likuiditas Terhadap Kebijakan Dividen Pada Perusahaan Manufaktur di Bursa Efek Indonesia Tahun 2020 – 2023 Qatrunnada Salsabila; Oryza Sativa; Muhammad Nur Faizin Ramadhan; Isti Pujihastuti
Jurnal Manajemen dan Ekonomi Kreatif Vol. 2 No. 3 (2024): Juli: Jurnal Manajemen dan Ekonomi Kreatif
Publisher : Universitas Kristen Indonesia Toraja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jumek.v2i3.403

Abstract

This research is important to provide valuable insight for companies in determining the optimal dividend policy and attracting investor interest. Hopefully, this research can make a significant empirical and theoretical contribution regarding the effect of leverage and liquidity on dividend policy in manufacturing companies on the IDX for the 2020-2023 period and can be a reference for companies in making decisions. This study uses some independent variables, namely Leverage and Liquidity. The dependent variable is the Dividend Policy which uses a purposive sampling method on one hundred samples of manufacturing companies on the IDX. In this case, H1 is accepted, leverage has a significant effect on dividend policy with a path coefficient (-0.055) and p-value (0.000 < 0.005). Then H2 is accepted, liquidity has a significant impact on dividend policy with path coefficient (0.109) and p-value (0.050 < 0.005). In this study, the data collected by researchers is quite limited, so future researchers are expected to develop data updates from previous studies where future researchers can find other variables that can affect dividend policy.
Health and Safety PPE Compliance Tracking Qatrunnada Salsabila
Journal Islamic Global Network for Information Technology and Entrepreneurship Vol. 2 No. 2 (2024): April : Journal Islamic Global Network for Information Technology and Entrepren
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/ignite.v2i2.1049

Abstract

Computer vision technology is used to improve work safety in the construction industry. The key in this project is the utilization of the YOLO method on the Roboflow platform. In addition to the Convolutional Neural Networks (CNN) algorithm, YOLO efficiently divides the image into a grid and classifies the objects in the grid by bounding box and confidence score. With the integration of YOLO, this project can achieve accurate and fast PPE detection. This project uses the YOLO method to detect head and body parts from input images. The detected body parts are then cropped and processed using the CNN method for classification. This project will also implement computer vision algorithms, including Deep Learning methods that currently have the most significant results in image recognition is CNN method, to automatically detect and monitor the use of PPE. This model achieves mAP 64.1%, Precision 73.2%, and Recall 60.2%. The Streamlit framework was used for deployment, creating a web application for PPE compliance tracking. This project, ''Health and Safety PPE Compliance Tracking'', aims to improve work safety in the construction industry. This project uses Computer Vision technology to detect, monitor, and ensure worker compliance with the use of appropriate PPE. The suggestion is to conduct further trials using other datasets in the form of photos or videos that can be done in real-time by ensuring that the colors of hats and vests do not vary too much to detect the conformity of labeling with PPE use.
Co-Authors Isti Pujihastuti Muhammad Nur Faizin Ramadhan Oryza Sativa