Analisis Prediksi Penentuan Waktu Dan Jumlah Stok Produk Dalam Penjualan Menggunakan Algoritma Xgboost Regression
Abstract
Penelitian ini mengembangkan sistem predictive analytics untuk peramalan penjualan guna mendukung UMKM di Shopee dalam memperkirakan permintaan produk di masa mendatang. Sistem ini memungkinkan pelaku usaha untuk menentukan waktu dan jumlah pengisian ulang stok yang optimal, sehingga dapat mengatasi tantangan terkait kelebihan dan kekurangan stok yang berdampak pada biaya serta kepuasan pelanggan. Dengan memanfaatkan algoritma XGBoost Regression dalam metodologi CRISP-DM dan data penjualan historis, model dievaluasi menggunakan metrik MAE, RMSE, R², dan MAPE, dan menunjukkan performa yang kuat (R² = 0,901, RMSE = 3,316, MAE = 0,873, dan MAPE <
10%). Hasilnya disajikan melalui grafik regresi yang membandingkan nilai aktual dan prediksi, garis model-fit, serta tabel prediksi yang menampilkan prakiraan penjualan bulanan untuk setiap produk selama periode 12 bulan. Pendekatan analisis prediktif ini terbukti dapat meningkatkan efisiensi manajemen persediaan dan mendukung pengambilan keputusan berbasis data bagi UMKM.
Kata Kunci— Analitik Prediktif, CRISP-DM, Pengisian Ulang Stok, Peramalan Penjualan, Regresi XGBoost, UMKM
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