Implementasi Model XGBoost untuk Prediksi Jumlah Transaksi dan Total Pendapatan di Jaringan Restoran CV Balibul
DOI:
https://doi.org/10.25124/jnst.v2i2.8750Keywords:
XGBoost, Prediksi Transaksi, Total Pendapatan, Bayesian Optimization, CV BalibulAbstract
Penelitian ini bertujuan untuk menerapkan model XGBoost dalam memprediksi jumlah transaksi dan total pendapatan di
jaringan restoran CV Balibul. Model XGBoost menggunakan teknik gradient tree boosting untuk meningkatkan akurasi
prediksi dari data transaksi harian yang diolah menggunakan library Pandas. Optimisasi parameter untuk model dilakukan
dengan metode Bayesian Optimization, dan evaluasi model menggunakan metrik R2, RMSE, MAPE, dan Pattern Similarity.
Hasil penelitian menunjukkan bahwa model XGBoost dapat memprediksi jumlah transaksi dan total pendapatan dengan
tingkat akurasi yang masuk akal, di mana shift 1 memiliki nilai error yang lebih kecil dibandingkan shift 2.
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Published
2025-01-06
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