Prediksi Volume Penjualan pada Retail Sembako Menggunakan Pemodelan XGBoost
Keywords:
XGBoost, Prediksi Penjualan, Manajemen Inventaris, Retail SembakoAbstract
Manajemen inventaris pada retail sembako menghadapi tantangan ketidakakuratan ramalan permintaan yang menyebabkan overstock atau stockout, sehingga mengganggu efisiensi operasional. Penelitian ini bertujuan mengembangkan model prediksi volume penjualan menggunakan algoritma Extreme Gradient Boosting (XGBoost) untuk meningkatkan akurasi manajemen stok. Metode yang digunakan meliputi akuisisi dan preprocessing data penjualan historis selama satu tahun dari Kaggle.com untuk empat kategori produk: Air Mineral, Minyak Goreng, Susu, dan Roti. Data tersebut kemudian dibagi menjadi data latih (80%) dan data uji (20%) untuk melatih model XGBoost. Kinerja prediktif model dievaluasi secara kuantitatif menggunakan metrik Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan bahwa model XGBoost mampu memprediksi volume penjualan dengan tingkat akurasi yang bervariasi. Performa terbaik dicapai pada produk dengan permintaan stabil seperti Roti (RMSE 5.54) dan Minyak Goreng (RMSE 6.78) , sementara performa lebih rendah teridentifikasi pada produk dengan permintaan sangat fluktuatif seperti Air Mineral (RMSE 16.04) dan Susu (RMSE 12.24). Sebagai solusi praktis untuk menutupi kelemahan prediksi, strategi penambahan alert range terbukti mampu meningkatkan tingkat pemenuhan permintaan secara signifikan. Sebagai contoh, penambahan 15 unit pada prediksi Roti mampu mencapai pemenuhan permintaan hingga 100% , sementara penambahan 20 unit pada Susu meningkatkan pemenuhan hingga 97,22%. Kombinasi antara prediksi XGBoost dan strategi alert range terbukti memiliki potensi besar dalam mengoptimalkan manajemen inventaris.
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