Sakina, Divayanti Febri (2025) PREDIKSI HARGA TELUR AYAM RAS BERBASIS HYBRID MODEL ARIMAX-LSTM PADA WILAYAH JAWA TENGAH. Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
Chicken eggs are included in the food ingredients needed to fulfill nutritional intake and as a source of animal protein for the Indonesian people from the livestock subsector. Broiler egg production in Central Java, one of the main provinces in Indonesia, showed a significant increase with an average of 9.12% per year during the 2021-2023 period. In addition, Central Java also contributes significantly to the national egg demand of 2,072,114.11 tons in 2023. With the imbalance between the amount of production and demand for broiler eggs, it is a product that often experiences price fluctuations. The cause of changes in the price of broiler eggs is influenced by several factors such as the price of its substitute products, namely chicken meat and beef, as well as during and before national holidays which can affect people's purchasing power. To manage the risk of price fluctuations, it is necessary to predict the price of broiler eggs. Studies that are often found related to predicting the price of broiler eggs only rely on price parameters without considering external factors. An appropriate model for predicting chicken egg prices by considering external factors is ARIMAX. However, ARIMAX can only recognize linear patterns. So it is necessary to combine (Hybrid) with a model that can recognize non-linear patterns, namely LSTM modeling. Based on the study conducted, it can be concluded that the Hybrid model produces a MAPE of 0.29%, more accurate than a single ARIMAX (MAPE 1.22%). And produced future forecasts of chicken egg prices in January 2025 ranging from Rp29,924 to Rp30,016/kg. This suggests the Hybrid model can maintain price stability without extreme spikes.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76.6 Computer Programming | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Data Science | ||||||||||||
| Depositing User: | Divayanti Febri Sakina Sakina | ||||||||||||
| Date Deposited: | 25 Jul 2025 02:58 | ||||||||||||
| Last Modified: | 25 Jul 2025 02:58 | ||||||||||||
| URI: | https://repository.upnjatim.ac.id/id/eprint/40811 |
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