Search for Articles:

MECS Press Menu

Christine Dewi

Work place: Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Indonesia

E-mail: christine.dewi13@gmail.com

Website:

Research Interests: Machine Learning, Computer Vision, Artificial Intelligence

Biography

Christine Dewi received a B.S. Degree (S. Kom.) from the Informatics engineering study program in 2010, and a Master of Computer Science (M.Cs.) from the Master of Information Systems study program in 2012, both from the Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Indonesia. In 2021 she finished her Ph.D. and work as a postdoctoral researcher at the College of Informatics, Chaoyang University of Technology, Taiwan until 2022. She got an Excellent Dissertation Award from the Taiwanese Association for Consumer Electronics (TACE), Taiwan 2021. This award aims to honor a dissertation project funded by MOST Taiwan. The participants were all students from various universities in Taiwan. She is now an Assistant Professor in the Department of Information Technology and serves as Head of the Associate degree Informatics Engineering Study Program at Satya Wacana Christian University, Indonesia. She is active as a reviewer and guest editor in many journals. Her current research interests include image processing, computer vision, object detection and recognition, artificial intelligence, and machine learning.

Author Articles
The Impact of Financial Statement Integration in Machine Learning for Stock Price Prediction

By Febrian Wahyu Christanto Victor Gayuh Utomo Rastri Prathivi Christine Dewi

DOI: https://doi.org/10.5815/ijitcs.2024.01.04, Pub. Date: 8 Feb. 2024

In the capital market, there are two methods used by investors to make stock price predictions, namely fundamental analysis, and technical analysis. In computer science, it is possible to make prediction, including stock price prediction, use Machine Learning (ML). While there is research result that said both fundamental and technical parameter should give an optimum prediction there is lack of confirmation in Machine Learning to this result. This research conducts experi-ment using Support Vector Regression (SVR) and Support Vector Machine (SVM) as ML method to predict stock price. Further, the result is compared between 3 groups of parameters, technical only (TEC), financial statement only (FIN) and combination of both (COM). Our experimental results show that integrating financial statements has a neutral impact on SVR predictions but a positive impact on SVM predictions and the accuracy value of the model in this research reached 83%.

[...] Read more.
Other Articles

Subscribe to receive issue release notifications and newsletters from MECS Press journals