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All Journal G-Tech : Jurnal Teknologi Terapan
Shalwa Oktavrilia Kusuma
Airlangga University, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Modeling PLN Inc. Customer Receivables Based on Geographically Weighted Regression Approach Shalwa Oktavrilia Kusuma; Marshanda Aprilia; Toha Saifudin; Sa’idah Zahrotul Jannah
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4439

Abstract

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%.
Co-Authors Marshanda Aprilia Sa’idah Zahrotul Jannah Toha Saifudin