Analisis Komparatif Ridge Regression dan Support Vector Regression pada Prediksi Inflasi Indonesia

Afifah Faiqatuzzahra, Olivia Intan Puspita, Yasmine Alifta, Diah Septiani

Abstract


Inflasi merupakan indikator makroekonomi krusial yang mencerminkan stabilitas perekonomian, sehingga ketersediaan model prediksi yang akurat sangat penting sebagai pendukung pengambilan keputusan ekonomi. Penelitian ini bertujuan untuk membandingkan kinerja metode Ridge Regression dan Support Vector Regression (SVR) dalam memprediksi inflasi Month-to-Month (MtM) di Indonesia. Kedua metode diterapkan pada data deret waktu historis inflasi yang telah ditransformasikan menggunakan fitur lag untuk menangkap pola ketergantungan nilai masa lalu. Evaluasi dan perbandingan performa model dilakukan menggunakan metrik Mean Absolute Error (MAE), Mean Squared Error (MSE), dan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan bahwa SVR memberikan performa prediksi yang lebih baik dibandingkan Ridge Regression pada data pengujian, dengan penurunan tingkat kesalahan masing-masing sebesar 2,86% pada MAE, 6,98% pada MSE, dan 3,54% pada RMSE. Temuan ini menunjukkan bahwa SVR lebih efektif dalam mempelajari pola historis inflasi Indonesia sehingga menghasilkan tingkat akurasi prediksi yang lebih baik dibandingkan Ridge Regression pada dataset yang digunakan dalam penelitian ini.


Keywords


Inflasi; Time Series Forecasting; Ridge Regression; Support Vector Regression (SVR); Machine Learning

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