Eksistensi Metode ARIMA, SARIMA dan LSTM dalam Memprediksi Penjualan
DOI:
https://doi.org/10.26418/justin.v13i4.76892Keywords:
ARIMA, SARIMA, LSTM, Machine Learning, Deep LearningAbstract
Prediksi penjualan merupakan fondasi strategis bagi keberlangsungan bisnis karena ketidakakuratan peramalan dapat menimbulkan kerugian signifikan akibat pengelolaan stok yang tidak optimal dan alokasi sumber daya yang kurang tepat. Penelitian ini bertujuan menganalisis dan membandingkan performa tiga metode berbeda dalam memprediksi penjualan harian: ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal Autoregressive Integrated Moving Average), dan LSTM (Long Short-Term Memory Network). Dataset yang digunakan mencakup data penjualan harian Golden Lamian selama tiga tahun (2020-2022) dengan total 1.916 sampel data. Metodologi penelitian mengikuti kerangka CRISP-DM yang terdiri dari enam tahapan sistematis. Eksperimen menggunakan parameter ARIMA (3,0,3), SARIMA (0,0,1) dengan seasonal order (1,1,2), dan arsitektur LSTM standar. Evaluasi performa menggunakan lima metrik: MSE, RMSE, MAE, MAPE, dan R ². Hasil penelitian menunjukkan bahwa LSTM mengungguli kedua metode lainnya dengan nilai RMSE 0.144, R ² 0.785, dan MSE 13.954. Sebaliknya, SARIMA menunjukkan performa terlemah dengan RMSE 0.235 dan R ² 0.372, yang disebabkan oleh ketidakkonsistenan pola musiman akibat dampak pandemi COVID-19. Temuan ini mengindikasikan bahwa metode deep learning seperti LSTM lebih robust dalam menangani fluktuasi data yang ekstrem dan memberikan akurasi prediksi yang lebih baik untuk implementasi bisnis praktis.References
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