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All Journal EKSAKTA: Journal of Sciences and Data Analysis Jurnal Fisika Unand Variance : Journal of Statistics and Its Applications International Journal of Electronics and Communications Systems Bulletin of Applied Mathematics and Mathematics Education Southeast Asian Business Review Jurnal Pendidikan MIPA
Muthoharoh, Luluk
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Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mathematics Physics

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Application of Multiple Linear Regression Models for prediction of rice production yields in Central Lampung Yani, Nadia Fitri; Muthoharoh, Luluk; Winardi, Abdy
Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v5i2.14559

Abstract

Rice production is a crucial component of agricultural sustainability and food security in Indonesia, particularly in Central Lampung. This study aims to analyze the influence of planting area and harvested area on rice production using a multiple linear regression approach. The analysis employs secondary time-series data and applies an ordinary least squares (OLS) method with a logarithmic transformation of the dependent variable to address heteroskedasticity issues. Descriptive statistics and classical assumption tests, including normality, multicollinearity, heteroskedasticity, and autocorrelation tests, were conducted to ensure model validity. The results indicate that harvested area has a statistically significant positive effect on rice production, while planting areas shows a negative but statistically insignificant effect. The regression model demonstrates strong explanatory capability with an R-squared value of 81.27% and is statistically significant based on the F-test. Model evaluation using in-sample error metrics yields a Mean Absolute Error (MAE) of 19,344.89, a Root Mean Squared Error (RMSE) of 46,738.41, and a Mean Absolute Percentage Error (MAPE) of 48.20%, indicating that the model effectively captures general production trends but has limited accuracy for precise quantitative forecasting. These findings suggest that harvested area plays a dominant role in determining rice output, while further improvements in predictive performance may be achieved by incorporating additional explanatory variables and exploring alternative modeling techniques.
Co-Authors Abdullah, Khoirul Mizan Abednego, Michael Ahmad Luky Ramdani Alvionita, Mika Ariyanto, Adisty Syawalda Athaulloh, M Farhan Biran, Christian Arvianus Nathanael Dian Maya Sari Fitri April Yanti, Fitri April Hati, Berliyana Kesuma Hendri Noperi Ira Safitri Ishak, Shahnaz Salsabila Kartika, Veni Zahara Khoarizmy, Gymnastiar Al Mika Alvionita Sitinjak Mochammad Imron Awalludin Mubarok, Husni Na’fa Muhammad Dhoni Apriyadi Muhammad Kristiawan Muslim, Anwar Nadia Fitri Yani Napitupulu, Leonard Andreas Neliyana, Rahma Pamungkas, Muhammad Putra Presilia, Presilia Rahmat Heru Setianto Reny Dwi Riastuti Sabila, Viyonisa Syafa Sari, Reni Permata Satria, Eggie Sharov, Sergii Sihaloho, Ryantoni Sihombing, Ericson Chandra Sitinjak, Mika Alvionita Surya, Annisa Cahyani Winardi, Abdy Zamzami, Balqis Dwian Fitri