Sistem Prediksi Konsumsi Energi Listrik Subsidi dan Non Subsidi Berbasis Web dengan Metode RNN (Kasus Kota Sukabumi)

Insany, Gina Purnama
Sujjada, Alun
Lidena, Salwa Dwi
Fadilah, Muhammad Sahrul
Wilianti, Refi

Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Vol 10, No 2 (2025): Edisi Agustus

Sukabumi City experiences an annual increase in electricity consumption, especially in subsidized and non-subsidized categories. However, the energy distribution planning process remains manual and reactive. This research developed a web-based electricity consumption prediction system using the Recurrent Neural Network (RNN) method integrated with the Laravel framework. The development process applied the Rapid Application Development (RAD) method and system modeling using UML. The RNN model achieved a prediction accuracy of 92.4% with an MAE of 12.38 kWh and RMSE of 16.12 kWh. The application provides interactive prediction visualizations to support more efficient energy planning.