Pemodelan Regresi Linear untuk Prediksi Konsumsi Energi Primer Indonesia Menggunakan Hybrid Particle Swarm Optimization dan Continuous Ant Colony Optimization - Faris Febrianto; Candra Dewi; Bayu Rahayudi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Primary energy consumption prediction is an important to project future government energy policy in any country. However, many primary energy consumption prediction often lack of accuracy and data sources. Indonesia primary energy consumption is the biggest than other country in south east asia region and fourth in asia pacific. Indonesia primary energy consumption always increased due to rapid economic growth in last few years, it raised 16% only in three years, 149.31Mtoe in 2010 to 174.24Mtoe in 2013. Indonesia primary energy sources from fossils energy, oil, gas, and coal, otherwise hydro energy, and other renewables energy only 3.33% from total consumption. Our aim is to create primary energy consumption prediction accurately from five input parameter, gross national income, gross domestic product, population, import, and eksport. We use multiple linear regression modelling with find intercept and slope coefficient using hybrid Particle Swarm Optimization and Continuous Ant Colony Optimization. Experiment results shows that linear regression model has average Mean Absolute Percentage Error 10.1% which is good category for primary energy consumption prediction. Hybrid method also compared with regression using standalone Particle Swarm Optimization and standalone Continuous Ant Colony Optimization.