LR-GLASSO Method for Solving Multiple Explanatory Variables of the Village Development Index

Abstract

Sustainable Development Goals (SDGs) are developments that maintain sustainable improvement in society’s economic, social, and environmental welfare. Kemendes PDTT RI has issued the Village Development Index (VDI) to provide information and the status of village progress to support village development to improve the National SDGS. Modelling with multiple explanatory variables causes a high correlation between explanatory variables, multicollinearity, and coefficient estimation results, which have major variance and overfitting in prediction results. The modeling solution uses LASSO and GLASSO. Binary categorical response data uses binary logistic regression (LR), so LR-LASSO and LR-GLASSO are used. North Maluku Province has a VDI ranking that tends to fall in 2018-2022. Based on the mean and variance of the coefficient estimation results and misclassification errors, LR-GLASSO is better than LR-LASSO and LR. LR-GLASSO is recommended for analyzing VDI data because it has many explanatory variables, and the correlation between them is relatively high. The recommendation given by the Indonesian government, if it is to increase the status of VDI in Indonesia, especially in North Maluku province, is to increase the number of electricity users, food and beverage stalls, and other cooperatives. The Indonesian government also needs to pay attention to villages relatively far from the regent's office, between food and beverage stalls, and supporting health centres, because they still need to be developed compared to other villages, and more than 50% of villages are underdeveloped. If the Village SDGs are formulated through increasing VDI status, it will support the achievement of SDGs goals nationally.

Keywords

• logistic regression (lr)
• lr-glasso
• lr-lasso
• multiple explanatory variables
• village development index