Poverty Analysis in Indonesia Based on AI (Artificial Intelligence) with ARIMA Method - Al Aziz, Moh. Sofwan Kastir; Rahman, Hairul; Hidayat, Nurul; Arifin, Fauzi; Ainorrofiqie, Ainorrofiqie (MediaTrend, 2025)
Poverty in Indonesia is a complex and multidimensional problem, because poverty levels can be an indicator of success for the country in terms of both its development and economy. This is an important point for the government to predict or predict poverty so that it can provide a more appropriate alternative. Based on this problem, a method is needed, namely using the forecasting method (Forcasting). In this study, researchers used a model from Box Jenkins, namely the Auto Regressive Moving Average (ARIMA) to predict the future level of poverty in Indonesia. AI (Artificial Intelligence) is not widely used in terms of economy, especially on poverty issues, but AI is one of the alternatives for countries to overcome poverty. Quantitative research in this study uses the ARIMA (Auto Regressive Moving Average) analysis. The poverty dataset used is sourced from the Central Bureau of Statistics (BPS) with test data from 2010 to 2023 to 2029. AI can provide alternative recommendations to deal with poverty in the future and in addition to looking at the forecast (Forcasitng) of poverty levels for the next 5 years. Conclusion: The government can make AI one of the tools to find solutions in the form of alternatives to solve poverty problems. Thus, through AI, it can be considered by the government in providing policies for poverty problems