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Erfan Febriantoro
Pascasarjana Teknologi Informasi Institut Sains dan Teknologi Terpadu Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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PEMODELAN PREDIKSI KUANTITAS PENJUALAN MAINAN MENGGUNAKAN LightGBM Erfan Febriantoro; Endang Setyati; Joan Santoso
SMARTICS Journal Vol 9 No 1 (2023): SMARTICS Journal (April 2023)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v9i1.8279

Abstract

The main characteristic of the toy industry is its rapid change and uncertainty. Demand, influenced by certain trends, can change abruptly and suddenly disappear when the next viral product takes over the market. Constant product innovation, short life cycles and high cannibalization rates have the potential to incur higher relative costs compared to other industries in terms of inventory obsolescence, lost sales and reduced prices. Based on these problems, a study was conducted to predict toy sales using the LightGBM algorithm model in a time-series form with a sales dataset of 460 toy items classified into 14 categories within a time span of 1,353 days with a prediction period of 1, 3, and 6 months. This study produced 42 models based on product category and prediction period, with the best RMSE value of 0.0042 in the KARTU toy model, and 3 models for all categories based on the prediction period with the best RMSE value of 0.0380 in the 1 month prediction period.
Co-Authors Endang Setyati Joan Santoso