Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear Regression Algorithm

Suryani, Des; Fadhilla, Mutia; Labellapansa, Ause

doi:10.29207/resti.v6i6.4590

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…The regression analysis model aims to provide parameter levels based on variables (X) and (Y). This model can be proven by the regression equation presented in (2) [52].…”

Section: Multiple Linear Regression (Mrl)mentioning

confidence: 99%

Machine learning classification analysis model community satisfaction with traditional market facilities as public service

Syahputra

Yanto

Putra

et al. 2023

IJ-AI

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Traditional markets are public service facilities that can be utilized by the community. The market function is used place where sellers and buyers meet in conducting transactions. This study aims to build a machine learning classification analysis model in measuring community satisfaction with traditional market facilities. The analytical methods used include Fuzzy. multiple linear regression (MRL), artificial neural network (ANN), and decision tree (DT). Fuzzy is used to generate a pattern of rules in determining the level of satisfaction. MRL serves to measure and test the correlation of rules that have been formed. The ANN method is used to carry out the classification analysis process based on learning. In the final stage. DT is used to describe the decision tree of the analysis process. This study presents the results of machine learning analysis which is very good in determining satisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzy logic which can develop a classification rule pattern of 32 patterns. MRL also shows a significant correlation level of 81.1% based on the indicator variables. Overall, the machine learning classification analysis model can provide knowledge to be considered in the management of traditional markets as public service facilities.

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“…The regression analysis model aims to provide parameter levels based on variables (X) and (Y). This model can be proven by the regression equation presented in (2) [52].…”

Section: Multiple Linear Regression (Mrl)mentioning

confidence: 99%

Machine learning classification analysis model community satisfaction with traditional market facilities as public service

Syahputra

Yanto

Putra

et al. 2023

IJ-AI

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“…Salah satunya dilakukan oleh [3], dalam studitersebut dilakukan peramalan terhadap harga beberapa jenis minyak mentah Indonesia menggunakan ARIMA dan diperoleh nilai MAPE sebesar 15%-23%. Penelitian lain dilakukan oleh [4] yang menggunakan Multiple Linear Regression untuk melakukan peramalan pada beberapa jenis minyak mentah di Indoensia dan diperoleh bahwa model tersebut baik digunakan untuk peramalan minyak mentah SCI dengan nilai MAPE sebesar 9%…”

Section: Pendahuluanunclassified

Penerapan Metode Fuzzy Time Series (FTS) Cheng dan Markov-Chain untuk Peramalan Indonesia Crude Oil Price (ICP)

Fakhriyana,

Brilliant

2024

IJAS

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In Indonesia, crude oil plays a significant role in the country’s economy as it serves as a source of income and meets the country's energy needs. Therefore, fluctuations in crude oil prices have a significant impact on the economic activities of the society. Forecasting the price of Indonesian crude oil is thus crucial. The international price of crude oil in Indonesia is known as the Indonesian Crude Oil Price (ICP). One commonly used statistical method for forecasting is the ARIMA method. However, the ARIMA method has certain assumptions that need to be fulfil, and many real-world data cannot meet these assumptions. Hence, forecasting using the Fuzzy Time Series (FTS) method, which does not rely on assumptions, is employed. Some popular FTS methods include the Cheng FTS method and the Markov Chain FTS method. This study implements the Cheng FTS and Markov Chain FTS methods on the ICP data from May 2018 to June 2023 to determine the most appropriate method for forecasting. The analysis results using the Cheng FTS method on the testing data yield a Mean Absolute Percentage Error (MAPE) value of 4,083%, while the Markov Chain FTS method has MAPE value of 4,585%. The Cheng FTS method selected as the appropriate model for forecasting the ICP data since it has a smaller MAPE value. Using the Cheng FTS method, the predicted ICP value for July 2023 is US$72,907 per barrel.Keywords: ICP; FTS Cheng; FTS Markov Chain; MAPE

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“…Eagle Industry Indonesia resulted in RMSE 36,241.241 was by made (Miftahuljannah et al, 2023). Research using Multiple Linear Regression to predict Indonesia Crude Oil Prices was made by (Des Suryani et al, 2022) resulted in MAPE values of 9% for SLC, 45% for Attaka, 126% for Duri, 33% for Belida, 150% for Banyu, and 50% for SC. Research on the stock market resulted in Linear Regression was made by (Kumar, 2022) accuracy of 85% and Support Vector Machine accuracy of 73%.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Comparison Between Linear Regression, Random Forest, and Logistic Regression Methods in Predicting Crude Palm Oil (CPO) Price

Wijaya,

Fauziah

2023

Brilliance

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This study compared the predictive capabilities of linear regression, random forest, and logistic regression models for forecasting crude palm oil prices. Utilizing historical data from 02/11/2020 to 13/11/2023, the dataset underwent training and testing with three scenarios: 90:10, 80:20, and 70:30. Evaluation metrics, including RMSE, MSE, and MAPE, assessed model performance. Each method had unique strengths and weaknesses, and the choice depended on application needs. The goal was to improve decision accuracy in predicting crude palm oil price trends. In the 90:10 scenario, random forest outperformed linear and logistic regression, yielding smaller MSE (43948.56), MAE (80.37), and RMSE (209.64). Similarly, in the 80:20 scenario, random forest had smaller MSE (137787.61), MAE (106.38), and RMSE (371.20). In the 70:30 scenario, random forest showed smaller MSE (107582.32), MAE (104.13), and RMSE (328). Overall, random forest consistently demonstrated better performance than linear and logistic regression.

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Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear Regression Algorithm

Cited by 4 publications

References 11 publications

Machine learning classification analysis model community satisfaction with traditional market facilities as public service

Machine learning classification analysis model community satisfaction with traditional market facilities as public service

Penerapan Metode Fuzzy Time Series (FTS) Cheng dan Markov-Chain untuk Peramalan Indonesia Crude Oil Price (ICP)

Analysis of the Comparison Between Linear Regression, Random Forest, and Logistic Regression Methods in Predicting Crude Palm Oil (CPO) Price

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