Jati, Putri Kharlina (2025) Analisis Peramalan Permintaan Produk dengan Algoritma Backpropagation Neural Network Pada PT Herba Emas Wahidatama. Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
PT Herba Emas Wahidatama faces challenges in managing unbalanced stock, caused by variations in product demand, ranging from high demand (fast-moving) products, stable demand (regular) products, to low demand (slow-moving) products. This imbalance often results in excess or shortage of inventory, which affects the company's operational efficiency. To overcome this problem, this study uses a product demand forecasting method with a backpropagation neural network algorithm using historical demand data from 2021 to 2023. The forecasting results show that the backpropagation neural network algorithm model is capable of producing high accuracy with a low Mean Absolute Percentage Error (MAPE) value. In addition, the effectiveness of this method was tested by comparing the actual results with the forecast targets, as shown in the data for products X, Y, and Z. Based on these results, it was found that the backpropagation neural network algorithm model is very effective, especially in predicting products with high and low demand, with an effectiveness rate of 100%. Therefore, this algorithm can be used as a decision-making tool in inventory management to be more efficient, while minimizing the risk of stock shortages or excess stock
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | T Technology > TS Manufactures > TS 155-194 Production Management, Operations Management | ||||||||||||
| Divisions: | Faculty of Agriculture > Departement of Agribusiness | ||||||||||||
| Depositing User: | Putri Kharlina Jati | ||||||||||||
| Date Deposited: | 21 Nov 2025 03:20 | ||||||||||||
| Last Modified: | 21 Nov 2025 03:20 | ||||||||||||
| URI: | https://repository.upnjatim.ac.id/id/eprint/46915 |
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