Analisis dan Prediksi Tingkat Kemiskinan di Sumatera Utara menggunakan Model LSTM berbasis Google Earth Engine

Hutabarat; Lerry Yos Santa Angelina; Juliandra; Vella; Pratama; Febryan; Indra; Evta

Dinamik

Universitas Stikubank

📄 Abstract

This study analyzes the prediction of poverty levels in North Sumatra Province by applying the Long Short-Term Memory (LSTM) method based on time series integrated with Google Earth Engine (GEE). Historical poverty data of districts/cities were obtained from the Central Statistics Agency (BPS) and processed using Python in Google Colab for LSTM model training. The prediction results are visualized spatially in the form of thematic maps through GEE to identify areas with high poverty rates. The evaluation model was carried out by calculating MAE, RMSE, MAPE, and prediction accuracy, with most areas having an accuracy above 80%. These findings indicate that this approach is effective in mapping poverty trends and supporting data-driven policies. This predictive model can be the basis for more targeted social interventions and strategies for developing inclusive and sustainable regional development.

ℹ️ Informasi Publikasi

Tanggal Publikasi

09 July 2025

Volume / Nomor / Tahun

Volume 30, Nomor 2, Tahun 2025

📝 HOW TO CITE

Hutabarat, Lerry Yos Santa Angelina; Juliandra, Vella; Pratama, Febryan; Indra, Evta, "Analisis dan Prediksi Tingkat Kemiskinan di Sumatera Utara menggunakan Model LSTM berbasis Google Earth Engine," Dinamik, vol. 30, no. 2, Jul. 2025.

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