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

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

doi:10.35315/dinamik.v30i2.10243

Lerry Yos Santa Angelina Hutabarat Universitas Prima Indonesia
Vella Juliandra Universitas Prima Indonesia
Febryan Pratama Universitas Prima Indonesia
Evta Indra Universitas Prima Indonesia

DOI: https://doi.org/10.35315/dinamik.v30i2.10243

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.

References

[1] N. Abdelmagid, F. Checchi, S. Garry, and A. Warsame, “Defining, measuring and interpreting the appropriateness of humanitarian assistance,” J. Int. Humanit. Action, vol. 4, no. 1, 2019, doi: 10.1186/s41018-019-0062-y.
[2] M. B. Pettersson, M. Kakooei, J. Ortheden, F. D. Johansson, and A. Daoud, “Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa,” IJCAI Int. Jt. Conf. Artif. Intell., vol. 2023-Augus, pp. 6165–6173, 2023, doi: 10.24963/ijcai.2023/684.
[3] M. S. Ummah, “PROVINSI SUMATERA UTARA DALAM ANGKA 2022 Sumatera Utara Province in Figures 2022,” Sustain., vol. 11, no. 1, pp. 1–14, 2019, [Online]. Available: http://scioteca.caf.com/bitstream/handle/123456789/1091/RED2017-Eng-8ene.pdf?sequence=12&isAllowed=y%0Ahttp://dx.doi.org/10.1016/j.regsciurbeco.2008.06.005%0Ahttps://www.researchgate.net/publication/305320484_SISTEM_PEMBETUNGAN_TERPUSAT_STRATEGI_MELESTARI
[4] L. Yang, J. Driscol, S. Sarigai, Q. Wu, H. Chen, and C. D. Lippitt, “Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review,” Remote Sens., vol. 14, no. 14, 2022, doi: 10.3390/rs14143253.
[5] B. Babenko, J. Hersh, D. Newhouse, A. Ramakrishnan, and T. Swartz, “Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico,” no. Nips, pp. 1–4, 2017, [Online]. Available: http://arxiv.org/abs/1711.06323
[6] M. Xie, N. Jean, M. Burke, D. Lobell, and S. Ermon, “Transfer learning from deep features for remote sensing and poverty mapping,” 30th AAAI Conf. Artif. Intell. AAAI 2016, pp. 3929–3935, 2016, doi: 10.1609/aaai.v30i1.9906.
[7] O. Hall, F. Dompae, I. Wahab, and F. M. Dzanku, “A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications,” J. Int. Dev., vol. 35, no. 7, pp. 1753–1768, 2023, doi: 10.1002/jid.3751.
[8] J. Vincent M and P. Varalakshmi, “Agroforestry mapping using multi temporal hybrid CNN+LSTM framework with landsat 8 satellite imagery and google earth engine,” Environ. Res. Commun., vol. 6, no. 6, 2024, doi: 10.1088/2515-7620/ad549f.
[9] B. Singh, M. Ravi, and S. Siddharth, Predicting Inequality of Opportunity and Poverty in India Using Machine Learning.

[10] B. P. Statistik, “Analisis Kemiskinan Makro Indonesia,” pp. 1–2, 2021, [Online]. Available: https://www.bps.go.id/publication/2021/11/30/9c24f43365d1e41c8619dfe4/pe nghitungan-dan-analisis-kemiskinan-makro-indonesia-tahun-2021.html
[11] S. Bhanja and A. Das, “Impact of Data Normalization on Deep Neural Network for Time Series Forecasting,” pp. 5–10, 2018, [Online]. Available: http://arxiv.org/abs/1812.05519
[12] Y. Tang and Y. Wu, “Dynamic monitoring of rural poverty recurrence: a novel early warning system in China,” Cienc. Rural, vol. 55, no. 2, pp. 1–10, 2025, doi: 10.1590/0103-8478cr20240083.
[13] X. Li, “A comparative study of statistical and machine learning models on near-real-time daily CO 2 emissions prediction”.
[14] Q. Zhao and G. Zheng, “Short-term load forecasting based on WD-LSSVM-LSTM model,” Electr. Meas. Instrum., vol. 60, no. 1, pp. 23–28, 2023, doi: 10.19753/j.issn1001-1390.2023.01.004.
[15] P. Manandhar, H. Rafiq, E. Rodriguez-Ubinas, and T. Palpanas, “New Forecasting Metrics Evaluated in Prophet, Random Forest, and Long Short-Term Memory Models for Load Forecasting,” Energies, vol. 17, no. 23, pp. 1–30, 2024, doi: 10.3390/en17236131.