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All Journal Brilliance: Research of Artificial Intelligence
Winjarwati, Yuni Wahyu
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Decision Sciences, Operations Research & Management Mathematics Other

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Comparison of Two Linear Regression Models for Predicting the Literacy Development Index in Indonesia Wardani, Iffatu; Jiwandono, Kunto; Pradanti, Okta Dyah; Winjarwati, Yuni Wahyu; Aghashie, Stevano Aji
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7083

Abstract

This study examines four suspected factors that have correlation and influence Community Literacy Development Index (IPLM). The four factors data was taken from each province in Indonesia i.e. the number of accredited libraries, the level of people’s reading interest, proportion of population living below 50% of the median income, and high school completion rate. To determine whether these four factors truly affect IPLM, a regression model analysis was conducted. The machine learning models discussed in this study are simple linear regression and multiple linear regression. One multiple linear regression model was used to integrate all four factors together. Four simple linear regression models were applied to assess each factor individually in relation to IPLM. From all these regression models, the adjusted R-squared values were compared. The analysis revealed that the level of people’s reading interest factor has a higher adjusted R-squared value in the simple linear regression (0.3828) compared to the multiple linear regression (0.3235). In contrast, the other three factors show lower adjusted R-squared values in their simple linear regressions than in the multiple linear regression. The conclusion is the reading interest factor best used to predict IPLM without involving the other factors. Meanwhile, the remaining three factors should be used collectively when predicting IPLM values.
Co-Authors Aghashie, Stevano Aji Jiwandono, Kunto Pradanti, Okta Dyah Wardani, Iffatu