<![CDATA[Micro, Small, and Medium Enterprises (MSMEs) in the clothing industry are one of the key pillars of the economy, contributing significantly to Gross Domestic Product (GDP) and employment. However, MSMEs face considerable challenges related to market competition, shifting consumer trends, and fluctuating demand. Advances in data analytics and machine learning offer solutions to improve sales forecasting accuracy, thereby supporting more effective business strategies. This study aims to develop a sales forecasting model based on Long Short-Term Memory (LSTM) tailored to the characteristics of clothing MSMEs in Indonesia. The research was conducted at Ananda Kids MSME in Purbalingga, using 30,885 daily transaction records collected over 23 months. The dataset included product categories, sales volume, and revenue, which were further processed through normalization, handling of missing values, and the addition of seasonal features. The LSTM model was designed with 128 neurons and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the LSTM model achieved high accuracy for certain product categories. The “Set” and “Children’s Fashion” categories recorded MAPE values below 10%, demonstrating the model’s effectiveness in forecasting stable sales patterns. In contrast, categories with high volatility, such as accessories, produced larger prediction errors. These results highlight that data quality and sales pattern stability are crucial factors in enhancing model performance. Overall, the study demonstrates that the application of LSTM holds significant potential in supporting strategic decision-making for MSMEs through more accurate sales forecasting. Beyond its practical contributions for business actors, the study also provides a basis for the development of digitalization policies for the MSME sector in Indonesia.]]>
Copyrights © 2026
