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All Journal Jurnal Pilar Nusa Mandiri Innotech
Sunendar, Nendi
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Computer Science & IT

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COMPARISON OF ARIMA, LSTM, AND GRU MODELS FOR FORECASTING SALES OF HIT AEROSOL PRODUCTS Sunendar, Nendi; Rianto, Yan
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6412

Abstract

A more accurate forecasting model, such as LSTM, can significantly enhance business efficiency by providing more reliable predictions of future sales, allowing for better inventory management, optimized production schedules, and more precise distribution planning. This leads to reduced costs, minimized stockouts, and improved customer satisfaction. This study evaluates the forecasting performance of ARIMA, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models using sales data from 2021 to 2023. The models are assessed based on Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Results show that LSTM outperforms the other models with a MAPE of 10.76%, followed by ARIMA at 11.23% and GRU at 11.47%. These findings highlight the advantages of deep learning methods, particularly LSTM, in capturing complex patterns and trends in time series data. The study demonstrates the potential of these models to optimize sales forecasting, aiding decision-making processes in production and distribution planning.
Perancangan Business Intelligence Dashboard Sales Performance Sunendar, Nendi
Innotech: Jurnal Ilmu Komputer, Sistem Informasi dan Teknologi Informasi Vol 2 No 2 (2025): Innotech Issue Juli 2025
Publisher : Universitas Siber Indonesia

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Abstract

Businesses need accurate and timely information, especially when it comes to salesman training to address concerns about keputusan, especially for manual management systems, risks associated with data input, and difficulties in conducting thorough analysis of sales to create less-than-ideal strategic plans. Because of this, the goal of the research is to develop and refine a data-based dashboard that can provide information in a clear and understandable manner. This dashboard is built using methodologies that include system analysis, data warehouse design, ETL (Extract, Transform, Load), and system design using ASP.NET and Microsoft SQL Server. The ETL process is crucial for ensuring that data integration, extraction, and transformation occur so that operational data is stored in an understandable manner and dashboards that are developed can show the progress of work.
Co-Authors Rianto, Yan