Predicting Time Series Sales Transaction Value  at E-Stall Crunchy Merchants Using Long-Short-Term Memory

Santoso Sant; Andika Dwi Asmoro Wicaksono; Rizky Zakariyya Rasyad; Arif Setiadi; Ade Oktarino; Ende Ende; Hasanudin Hasanudin

doi:10.18535/ijsrm/v14i01.em01

Abstract

The utilization of sales data as a foundation for strategic decision-making plays a crucial role in supporting the operational success of Merchant E-Stall Crunchy. However, the high variability and volume of daily transaction data create challenges in accurately identifying sales patterns. To address this issue, this research proposes the application of a deep learning approach—Long Short-Term Memory (LSTM)—to perform time series–based prediction of sales transaction values. The objective of this study is to develop a predictive model capable of uncovering historical patterns, seasonal trends, and demand fluctuations, thereby providing insights that support managerial decision-making. The Design Science Research methodology is employed, covering problem identification, model development, implementation, and evaluation. The dataset consists of daily sales transactions from E-Stall Crunchy, including transaction dates, product types, and sales values. The LSTM model is implemented to predict daily transaction values, and its performance is evaluated using MAE and RMSE metrics. The expected outcome includes an accurate prediction model and analytical recommendations that can support strategies for improving sales performance and operational efficiency.

Keywords

Time SeriesSales PredictionLSTMDeep LearningE-Stall Crunchy

References

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[refR-2] Meitasari, Devina Finny, and Kukuh Sinduwiatmo. 2024. “Persepsi Konsumen Terhadap Kualitas Layanan Online GoFood.” (1): 89–99.Google Scholar ↗

[refR-3] Ahmed, Dozdar Mahdi, Masoud Muhammed Hassan, and Ramadhan J Mstafa. 2022. “A Review on Deep Sequential Models for Forecasting Time Series Data.” 2022(Dl).Google Scholar ↗

[refR-4] Vangibhurathachhi, S. K. (2025). Enhancing Time Series Forecasting using DRL with attention-based neural architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 92-99.Google Scholar ↗

[refR-5] Fadillah, Z. I. (2025). Strategi Inovasi dan Transformasi Digital dalam Meningkatkan Daya Saing Bisnis Di Era Industri 4.0. JMEB Jurnal Manajemen Ekonomi & Bisnis, 3(01).Google Scholar ↗

[refR-6] Santoso, S., Suwaryo, N., Nurali, N., Gunarso, S., Tugiman, T., Paryadi, A., & Hidayah, S. (2025). Optimalisasi Analisis Penjualan dan Prediksi Permintaan Pada UMKM dengan Business Intelligence di Kelurahan Pematang Sulur. I-Com: Indonesian Community Journal, 5(2), 939-952.Google Scholar ↗

[refR-7] Mojtahedi, F Fazel, and N Yousefpour S H Chow. 2025. “Deep Learning for Time Series Forecasting : Review and Applications in Geotechnics and Geosciences.” Archives of Computational Methods in Engineering 32(6): 3415–45. https://doi.org/10.1007/s11831-025-10244-5.DOI ↗Google Scholar ↗

[refR-8] Wahyuningtyas, U. A., & Fadil, C. (2024). Strategi Ekonomi dalam Mengatasi Keterbatasan Data untuk Meningkatkan Proses Prediksi Penjualan TV. Jurnal Ilmiah Wahana Pendidikan, 10(16), 865-871.Google Scholar ↗

[refR-9] Ferdowsy, Faria. 2024. “Deep Learning-Based Time Series Prediction Techniques.” (February).Google Scholar ↗

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