OPTIMIZING ARTIFICIAL INTELINGENCE TO PREDICT INDONESIAN GREEN
BANKING STOCK
Dublin Core
Title
OPTIMIZING ARTIFICIAL INTELINGENCE TO PREDICT INDONESIAN GREEN
BANKING STOCK
BANKING STOCK
Subject
Artificial Intelligence, Machine Learning, Deep Learning, Green Investment,
SRI KEHATI, LSTM, Phyton
SRI KEHATI, LSTM, Phyton
Description
The world is turning green, from waste recycling to wind and solar power generation,
which supports the significance of green investments. Everyone is aware of the negative effects of
climate change, and the majority of people are very interested in finding solutions. In other words,
making green investments may be a good strategy to lessen the environmental burden that humans
have caused.
In order to address the aforementioned issues, this project will create a hybrid machine
learning system for the Green Banking Stock which included in SRI KEHATI index, an Indonesian
green index, using the Long Short Term Memory (LSTM) Method in order to predict the index
movement using Phyton programming language.
The study's findings demonstrate that the software's predictions have a tolerable error rate.
Median Absolute Error, Mean Absolute Percentage Error, and Median Absolute Percentage Error
are the three different error metrics that are utilized.
which supports the significance of green investments. Everyone is aware of the negative effects of
climate change, and the majority of people are very interested in finding solutions. In other words,
making green investments may be a good strategy to lessen the environmental burden that humans
have caused.
In order to address the aforementioned issues, this project will create a hybrid machine
learning system for the Green Banking Stock which included in SRI KEHATI index, an Indonesian
green index, using the Long Short Term Memory (LSTM) Method in order to predict the index
movement using Phyton programming language.
The study's findings demonstrate that the software's predictions have a tolerable error rate.
Median Absolute Error, Mean Absolute Percentage Error, and Median Absolute Percentage Error
are the three different error metrics that are utilized.
Creator
Widhiyo Sudiyono, S.T. , M.A.B
Source
https://jurnal.stie-aas.ac.id/index.php/IJEBAR
Date
2023
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Citation
Widhiyo Sudiyono, S.T. , M.A.B, “OPTIMIZING ARTIFICIAL INTELINGENCE TO PREDICT INDONESIAN GREEN
BANKING STOCK,” Repository Horizon University Indonesia, accessed April 20, 2025, https://repository.horizon.ac.id/items/show/7641.
BANKING STOCK,” Repository Horizon University Indonesia, accessed April 20, 2025, https://repository.horizon.ac.id/items/show/7641.