Model Prediksi Manajemen Stok Produk Berbasis Deep Learning Gated Recurrent Unit untuk Optimalisasi Rantai Pasok E-Commerce

Darmawan Lahru Riatma; Yusuf Fadhlilla  Rahman; Trisna Ari  Roshinta; Masbahah Masbahah; Ahmad Faisal  Sani; Rifa  Khoirunisa; Nur Azizul  Haqimi

doi:10.51454/decode.v5i1.1130

Authors

Darmawan Lahru Riatma Diploma Tiga Teknik Informatika Universitas Sebelas Maret
Yusuf Fadhlilla Rahman Diploma Tiga Teknik Informatika Universitas Sebelas Maret
Trisna Ari Roshinta Diploma Tiga Teknik Informatika Universitas Sebelas Maret
Masbahah Masbahah Diploma Tiga Teknik Informatika Universitas Sebelas Maret
Ahmad Faisal Sani Diploma Tiga Teknik Informatika Universitas Sebelas Maret
Rifa Khoirunisa Diploma Tiga Teknik Informatika Universitas Sebelas Maret
Nur Azizul Haqimi Diploma Tiga Teknik Informatika Universitas Sebelas Maret

DOI:

https://doi.org/10.51454/decode.v5i1.1130

Keywords:

Deep Learning, Gated Recurrent Unit (GRU)), Manajemen Stok

Abstract

Perkembangan pesat e-commerce mendorong kebutuhan akan sistem manajemen stok produk yang adaptif dan presisi tinggi untuk menjaga kesinambungan rantai pasok. Ketidakseimbangan antara permintaan konsumen dan ketersediaan stok dapat menyebabkan kerugian signifikan, baik dalam bentuk overstock maupun stockout. Penelitian ini bertujuan untuk merancang model prediksi manajemen stok produk berbasis algoritma deep learning Gated Recurrent Unit (GRU) untuk meningkatkan efisiensi rantai pasok e-commerce. Metode yang digunakan mencakup pengumpulan data historis penjualan, preprocessing data (normalisasi, pembersihan, dan pembagian data pelatihan dan pengujian), perancangan arsitektur model GRU, serta pelatihan dan evaluasi model menggunakan metrik RMSE, MAE, MAPE, dan koefisien determinasi R². Hasil eksperimen menunjukkan bahwa model GRU memberikan performa terbaik pada epoch ke-200 dengan RMSE 622.94, MAE 451.04, MAPE 0.15, dan nilai R² sebesar 0.70. Temuan ini menunjukkan bahwa GRU efektif dalam memodelkan data deret waktu untuk prediksi permintaan produk. Kesimpulannya, model GRU dapat menjadi pendekatan yang inovatif dan andal dalam mendukung keputusan manajerial berbasis data guna meningkatkan efisiensi rantai pasok e-commerce.

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