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All Journal International Journal of Educational Review, Law And Social Sciences (IJERLAS) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Penelitian Teknologi Informasi dan Sains
Saidan Sany Lubis
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Journal : Jurnal Penelitian Teknologi Informasi dan Sains

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Analisis Big Data untuk Prediksi Permintaan Produk dalam E-commerce Sumita Wardani; Saidan Sany Lubis; Rico Wijaya Dewantoro
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 3 No. 1 (2025): JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v3i1.3066

Abstract

The rapid development of e-commerce has generated huge volumes of data, opening up opportunities to analyze product demand patterns more accurately. This research aims to develop a product demand prediction model based on big data analysis. The data used includes sales transactions, product searches, customer reviews, and external factors such as seasons and promotions. The main methods used are machine learning techniques such as random forest regression and neural networks to build predictive models, with data extraction, transformation, and analysis processes carried out using big data platforms such as Hadoop and Spark. The resulting model is evaluated using accuracy metrics, such as mean absolute error (MAE) and root mean square error (RMSE), to measure prediction performance. The results show that the use of big data in product demand prediction can increase the accuracy of inventory planning and stock management by up to 25% compared to conventional methods. These findings make a significant contribution to the optimization of e-commerce operations, especially in more efficient and timely data-driven decision-making.
Co-Authors Rico Wijaya Dewantoro Sumita Wardani Vivi Gusrini Rahmadani Pohan Yeni Absah