Time Series Modeling for Unemployment Prediction in Indonesia Using ARIMA (Autoregressive Integrated Moving Average) Approach
DOI:
https://doi.org/10.37476/presed.v3i1.94Kata Kunci:
Unemployment Forecasting, Time Series Analysis, Arima Approach, Economic Policy, Econometric ForecastingAbstrak
Penelitian ini bertujuan untuk mengembangkan model deret waktu yang efektif untuk memprediksi tingkat pengangguran di Indonesia menggunakan pendekatan ARIMA (Autoregressive Integrated Moving Average). Analisis dilakukan menggunakan data historis pengangguran dari Februari 2010 hingga Februari 2020. Setelah mengevaluasi berbagai konfigurasi model ARIMA, penelitian ini mengidentifikasi ARIMA (2,0,2) sebagai model dengan kinerja terbaik. Model ini menunjukkan akurasi peramalan yang unggul, dengan RMSE sebesar 0,1629445, MAE sebesar 0,1376129, dan MAPE sebesar 2,317308, serta nilai R-square yang tinggi. Hasil perkiraan model ARIMA (2,0,2) untuk Februari 2021 menunjukkan tingkat pengangguran sebesar 5,070741, dengan batas bawah 4,59359 dan batas atas 5,547891. Temuan ini memberikan wawasan berharga bagi pembuat kebijakan dan pemangku kepentingan pasar tenaga kerja di Indonesia, memungkinkan mereka untuk membuat keputusan yang tepat dan menerapkan strategi yang terarah untuk menangani tantangan pengangguran secara lebih efektif. Penerapan pendekatan ARIMA yang berhasil dalam penelitian ini menyoroti potensinya sebagai alat yang tangguh dan dapat diandalkan untuk peramalan pengangguran dalam konteks Indonesia, sekaligus memberikan kontribusi pada literatur yang ada tentang pemodelan deret waktu untuk analisis pasar tenaga kerja.
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