Afridayani
Master of Data Science and Artificial Intelligence, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Indonesia

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Performance Analysis of the Multivariate Multiple Linear Regression Algorithm for Economic Growth Based on Oil Palm Plantation Land Expansion in North Sumatra Province Afridayani; Erna Budhiarti Nababan; Baihaqi Siregar
Green Intelligent Systems and Applications Volume 6 - Issue 2 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i2.1294

Abstract

Oil palm plantations are one of the main sectors contributing to regional economic development in North Sumatra Province. This study aimed to analyze the performance of the Multivariate Multiple Linear Regression (MMLR) algorithm in modeling the relationship between oil palm plantation expansion and regional economic indicators. Secondary data from five oil palm-producing regencies covering the period 2013–2023 were obtained from the Central Statistics Agency (BPS). The independent variables consisted of plantation area and oil palm production, whereas the dependent variables were Gross Regional Domestic Product (GRDP) and per capita income. The dataset was divided into training (2013–2019) and testing (2020–2023) subsets. Model performance was evaluated using the coefficient of determination (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results showed that the MMLR model was able to capture the relationship between oil palm plantation expansion and regional economic indicators, although its predictive performance varied across regencies. These findings indicated that the model provided useful insights into the contribution of the oil palm plantation sector to regional economic development in North Sumatra.