Modeling PLN Inc. Customer Receivables Based on Geographically Weighted Regression Approach

Shalwa Oktavrilia Kusuma (Airlangga University, Indonesia)
Marshanda Aprilia (Airlangga University, Indonesia)
Toha Saifudin (Airlangga University, Indonesia)
Sa’idah Zahrotul Jannah (Airlangga University, Indonesia)

G-Tech : Jurnal Teknologi Terapan
Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024

PLN Inc. implements a postpaid service that has resulted in many customer receivables issues. Customer receivables disrupt PLN Inc.'s cash flow, requiring the government to inject funds from the state budget. If the state budget experiences a deficit, it can increase the national debt. National debt impacts the achievement of SDG goals, namely sustainable economic growth (SDG 8), reducing inequalities (SDG 10), and financing infrastructure that supports development (SDG 9). The largest receivables occur on the island of Java, where many companies have high electricity consumption, while outside Java, electricity consumption is lower due to the scarcity of companies. This indicates a spatial influence on the size of PLN Inc.'s customer receivables, so this research was conducted using the Geographically Weighted Regression (GWR) method. The study found that the best weighting was fixed Gaussian with an R² value of 96.94%, which is better than the global regression value of 44.34%.