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Optimasi Strategi Repeat Buyer pada E-commerce Indonesia Melalui Pendekatan Dynamic Programming untuk Bundling Product Multi-Kategori
Abstract
Penelitian ini bertujuan untuk mengoptimalkan strategi peningkatan repeat buyer pada e-commerce di Indonesia melalui penyusunan rekomendasi bundling product multi-kategori berbasis pendekatan komputasional. Pendekatan yang digunakan adalah Dynamic Programming melalui model optimasi Knapsack yang dikombinasikan dengan analisis Threshold Standard Deviation untuk menyaring kategori produk berdasarkan kedekatan demografis pelanggan. Proses penelitian meliputi tahap preprocessing data, pemodelan parameter bobot dan profit, optimasi kombinatorial, serta penentuan prioritas rekomendasi berbasis customer profiling. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan rekomendasi bundling yang relevan dan terpersonalisasi berdasarkan usia dan riwayat transaksi pelanggan. Dynamic Programming menunjukkan performa yang lebih stabil dan efisien pada kompleksitas data yang lebih tinggi, meskipun pada dataset kecil Brute Force memiliki waktu eksekusi lebih cepat. Secara keseluruhan, pendekatan yang diusulkan dinilai mampu meningkatkan akurasi rekomendasi serta mendukung strategi pemasaran untuk mendorong loyalitas pelanggan.
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DOI: https://doi.org/10.46576/device.v7i1.8956
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