TY - GEN
T1 - Explainable AI Prediction of Cooking Oil Prices over Time
AU - Azzuri, Fachrizal
AU - Darfiansa, Lazuardy Syahrul
AU - Grananta, Rahma Syndu
AU - Permatasari, Ayu
AU - Yudistira, Novanto
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/11/22
Y1 - 2022/11/22
N2 - Cooking Oil is an essential Commodity in household needs, so an uncertain price increase will significantly impact the Community's Scope. This study aimed to determine the factors of high and low prices of Cooking Oil Based on Other Commodities. Therefore, this research was conducted based on predicting an increase in cooking oil prices from time to time determined by other commodities. We use Long Short-Term Memory (LSTM), one of the Machine Learning methods that can predict based on time series data efficiently (remembering a collection of information that has been stored for a long time and deleting information that is no longer relevant). Then it is implemented into the Explainable Artificial Intelligence (XAI) model, namely SHapley Additive ExPlanations (SHAP) using Random Forest, which is used in the explanation problem of machine learning models that can handle data sets containing Continue Variables as in the case of regression and categorical variables as in the case of classification. On Commodities that affect the Increase in Cooking Oil Prices.
AB - Cooking Oil is an essential Commodity in household needs, so an uncertain price increase will significantly impact the Community's Scope. This study aimed to determine the factors of high and low prices of Cooking Oil Based on Other Commodities. Therefore, this research was conducted based on predicting an increase in cooking oil prices from time to time determined by other commodities. We use Long Short-Term Memory (LSTM), one of the Machine Learning methods that can predict based on time series data efficiently (remembering a collection of information that has been stored for a long time and deleting information that is no longer relevant). Then it is implemented into the Explainable Artificial Intelligence (XAI) model, namely SHapley Additive ExPlanations (SHAP) using Random Forest, which is used in the explanation problem of machine learning models that can handle data sets containing Continue Variables as in the case of regression and categorical variables as in the case of classification. On Commodities that affect the Increase in Cooking Oil Prices.
KW - Cooking Oil
KW - LSTM
KW - Random Forest
KW - SHAP
UR - https://www.scopus.com/pages/publications/85146923375
U2 - 10.1145/3568231.3568240
DO - 10.1145/3568231.3568240
M3 - Conference contribution
AN - SCOPUS:85146923375
T3 - ACM International Conference Proceeding Series
SP - 47
EP - 56
BT - SIET 2022 - Proceedings of 7th International Conference on Sustainable Information Engineering and Technology 2022
PB - Association for Computing Machinery
T2 - 7th International Conference on Sustainable Information Engineering and Technology, SIET 2022
Y2 - 22 November 2022
ER -