Analisis Prediksi Kenaikan Harga Daging Sapi Dan Daging Ayam Di Bandung
Keywords:
Chicken meat, Beef, LTS method, Price increaseAbstract
This study examines the comparison between chicken and beef prices in Indonesia over the period from 2017 to 2021. Data sourced from a GitHub repository were analyzed using the Least Trimmed Squares (LTS) method to mitigate the influence of outliers and ensure robust results. The analysis revealed that beef prices consistently remained higher than chicken prices. Additionally, beef prices demonstrated greater stability, exhibiting lower fluctuations compared to the more volatile chicken prices. The analysis was implemented using the Python programming language, which facilitated data processing and the application of the LTS method. These findings have significant implications for various stakeholders in the meat supply chain, including producers, distributors, and consumers, aiding in better planning and decision-making.
References
ROUSSEEUW, P. J., & HUBERT, M. (2018). Anomaly detection by robust statistics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), e1236. ROUSSEEUW, P.J., & LEROY, A.M. (1987). Robust Regression and Outlier Detection. New York: Wiley.
CROUX, C., FILZMOSER, P., & OLIVEIRA, M. R. (2014). Algorithms for projection–pursuit robust principal component analysis. Chemometrics and Intelligent Laboratory Systems, 129, 45-52. VENABLES, W. N., & RIPLEY, B. D. (2002). Modern Applied Statistics with S. Fourth Edition. Springer.
KURNAZ, F. S., HOFFMANN, R., & FILZMOSER, P. (2018). Robust sparse principal component analysis. The Annals of Applied Statistics, 12(4), 2347-2371.
