Prediction Modeling of Toy Sales Quantity Using Light Gradient Boosting Machine

Erfan Febriantoro, Endang Setyati, Joan Santoso


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.


Erfan Febriantoro (Primary Contact)
Endang Setyati
Joan Santoso
Author Biography

Erfan Febriantoro, Pascasarjana Teknologi Informasi Institut Sains dan Teknologi Terpadu Surabaya

Meraih gelar Sarjana Komputer (S.Kom) dari Universitas Muhammadiyah Jember pada tahun 2016. Saat ini penulis melanjutkan studi Pascasarjana Teknologi Informasi Institut Sains Dan Teknologi Terpadu Surabaya (ISTTS). Saat ini menjabat sebagai Direktur CV. Neturmeric Internasional, sebuah perusahaan Teknologi Informasi skala nasional yang berfokus pada layanan sistem informasi perusahaan dagang, manufaktur, retail, dan pemerintahan. Bidang perminatan digital marketing, data visualization, big data, dan database.

E. Febriantoro, E. Setyati, and J. Santoso, “Prediction Modeling of Toy Sales Quantity Using Light Gradient Boosting Machine”, SMARTICS, vol. 9, no. 1, pp. 7–13, Apr. 2023.

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