Multivariate Time Series Forecasting pada Penjualan Barang Retail dengan Recurrent Neural Network

Robertus Bagaskara Radite Putra, Hendry Hendry

Abstract


Intisari–Pasar ritel di Indonesia semakin berkembang seiring bertambahnya penduduk dan daya beli. Peluang ini harus dimanfaatkan, namun dalam bisnis ritel, kadangkala terjadi keadaan Out of Stock maupun over stock di dalam toko. Untuk mengatasi hal tersebut, kita bisa mengatasinya dengan melakukan peramalan atau prediksi penjualan yang akan terjadi di masa mendatang. Ada beberapa macam metode untuk melakukan peramalan, namun secara umum terbagi menjadi 2 jenis yaitu metode statistika dan juga computational intelligence. Penelitian ini mencoba untuk melakukan prediksi penjualan barang retail perhari menggunakan metode Recurrent Neural Network (RNN) sebagai bagian dari metode computational intelligence. Dari penelitian ini kita bisa dapatkan hasil bahwa dalam kasus prediksi penjualan ritel, performa akurasi RNN lebih baik dari metode statistika

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DOI: https://doi.org/10.35314/isi.v7i1.2398

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