Widarto, Nabilla Umniyah (2025) Peramalan Harga Komoditi Beras Premium Menggunakan Algoritma K-Nearest Neighbor (K-NN) Di Kota Surabaya. Undergraduate thesis, Universitas Hayam Wuruk Perbanas.
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Abstract
Fluctuations in premium rice prices in Surabaya can affect economic stability and public welfare. This study aims to predict premium rice prices using the K-Nearest Neighbor (K-NN) algorithm by utilizing historical rice price data from six main markets in Surabaya for the period 2014–2024. The data is divided into two parts, namely training data (2014–2020) and testing data (2021–2024). The results of the analysis show that the K-NN model with an optimal K value provides good prediction accuracy, with an average Mean Absolute Percentage Error (MAPE) value below 10% for most markets, indicating the reliability of the model in predicting price patterns. Keputran Market recorded the best results with a MAPE value of less than 10%, while other markets such as Wonokromo and Pucang Anom showed lower accuracy due to more complex price patterns. Evaluation using the Root Mean Square Error (RMSE) metric also confirmed the effectiveness of the model in producing accurate predictions based on training data. This study shows that the K-NN algorithm can be an effective tool to support strategic decision making in maintaining price stability and improving food security. Keywords: Price Prediction; K¬-Nearest Neighbor; MAPE, RMSE; Premium Rice
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Subjects: | 000 - COMPUTER SCIENCE, INFORMATION, GENERAL WORKS > 000 - 009 COMPUTER SCIENCE, INFORMATION, GENERAL WORKS > 000 - COMPUTER SCIENCE, INFORMATION, GENERAL WORKS 000 - COMPUTER SCIENCE, INFORMATION, GENERAL WORKS > 000 - 009 COMPUTER SCIENCE, INFORMATION, GENERAL WORKS > 004 - COMPUTER SCIENCE 000 - COMPUTER SCIENCE, INFORMATION, GENERAL WORKS > 000 - 009 COMPUTER SCIENCE, INFORMATION, GENERAL WORKS > 005 - COMPUTER PROGRAMMING, PROGRAMS & DATA |
| Divisions: | Bachelor of Information Systems |
| Depositing User: | Nabilla Umniyah Widarto |
| Date Deposited: | 04 Mar 2025 02:18 |
| Last Modified: | 04 Mar 2025 02:18 |
| URI: | http://eprints.perbanas.ac.id/id/eprint/13007 |
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