<![CDATA[The economic condition of a country can be measured using one of the indicators, the inflation rate. Therefore, the inflation needs to be maintained so that its rate can be controlled. To support this, it is necessary to pay attention to several factors that influence the inflation rate. These factors include the amount of exports, imports, narrow money (M1), broad money (M2), the rupiah exchange rate against the USD, interest rates, rice prices in wholesale trade, farmer exchange rates (NTP), world crude oil prices, bank investment credit, GDP, and foreign exchange reserves. In this study, we analyze the significant factors influencing the inflation rate in Indonesia using the best model of the Ridge regression, LASSO regression, and Elastic-Net methods. In this modeling, the γ and λ values from the three methods are optimized first. The data used in this study consist of inflation data in Indonesia and its factors for 2020-2024, sourced from the BPS. Among the three high-dimensional data methods, the LASSO regression is the best method with the smallest MSE for modeling inflation data in Indonesia. The LASSO regression model produces 8 predictor variables that significantly influence inflation data, i.e., imports, M1, interest rates, and world crude oil prices with positive coefficient signs, as well as rice price variables in wholesale trade, NTP, GDP, and foreign exchange reserves with negative coefficient signs.Keywords: inflation; ridge regression; lasso regression; elastic-net.]]>
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