Pengembangan Model Prediksi Permintaan Baby Product pada Merchant Shopee Menggunakan Support Vector Regression
DOI:
https://doi.org/10.58344/locus.v5i5.5689Keywords:
Grid Search, Perencanaan Stok, Prediksi Permintaan, Machine Learning, Support Vector Regression (SVR)Abstract
Pasar popok dan susu bayi di Indonesia menunjukkan tren pertumbuhan yang signifikan dengan proyeksi nilai masing-masing mencapai USD 2,39 miliar dan USD 5,35 miliar pada tahun 2029. Penelitian ini bertujuan untuk membangun model prediksi permintaan yang akurat menggunakan metode Support Vector Regression (SVR) guna mengantisipasi lonjakan atau penurunan permintaan serta memberikan rekomendasi strategi manajemen inventori dan pemasaran yang efektif. Variabel yang digunakan mencakup fitur transaksional (SKU, kategori, harga, berbagai jenis potongan harga/voucher, koin Shopee, dan diskon ongkir) serta faktor eksternal (hari libur nasional dan event Shopee). Data diproses melalui tahap cleaning, feature engineering, dan normalisasi, kemudian dilakukan uji perbandingan dimana uji coba pertama menggunakan data latih (70%) dan data uji (30%), lalu dilakukan uji coba kedua dengan menggunakan data latih (80%) dan data uji (20%). Optimasi model dilakukan menggunakan kernel RBF dengan teknik Grid Search untuk menemukan hyperparameter paling optimal. Hasil penelitian menunjukkan bahwa model pada uji coba kedua menggunakan perbandingan data latih (80%) dan data uji (20%) menghasilkan hasil yang lebih baik dimana mampu menjelaskan sekitar 52% variabilitas data permintaan dengan nilai MAE sebesar 1,34 unit dan RMSE sebesar 3,73 unit. Kesimpulannya, model ini cukup andal dalam menangkap pola umum dan tren fluktuasi permintaan di pasar yang volatil. Implementasi model dalam bentuk dashboard interaktif memberikan manfaat praktis bagi merchant untuk melakukan simulasi skenario promosi dan mengoptimalkan pengelolaan stok, sehingga dapat meningkatkan efisiensi operasional dan profitabilitas bisnis jangka panjang.
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