Price Prediction of Chili Commodities in Bandung Regency Using Bayesian Network

Putri Nuvaisiyah, F. Nhita, D. Saepudin
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引用次数: 10

Abstract

Chili is one of the agricultural commodities consumed by Indonesian people. Market data in recent years show that chili prices tend to fluctuate as supply and demand changes. One of the impacts of chili price changes for farmers is the production cost is higher than the selling price. In addition to supply and demand changes, the weather is also indicated as a factor of price changes due to the weather being considered by farmers to grow chili. Price prediction is needed to determine the condition of chili prices in the future to help farmers in making decisions to plant at the right time. One method that can be used to make prediction is Data Mining classification method. In this paper, Bayesian network algorithm was used as Data Mining classification method to predict the price of chili commodity in Bandung Regency based on weather information and classified the price into economic class and not economic class. The result shows that the prediction model obtained by the Bayesian Network gives a system’s performance for precision and recall that is 1 and 0.94 respectively with average accuracy of 85.5% in classifying the price.
基于贝叶斯网络的万隆市辣椒类商品价格预测
辣椒是印尼人民消费的农产品之一。近年来的市场数据显示,辣椒价格往往随着供需变化而波动。辣椒价格变化对农民的影响之一是生产成本高于销售价格。除了供需变化之外,天气也被认为是价格变化的一个因素,因为农民考虑天气来种植辣椒。价格预测是确定未来辣椒价格的必要条件,以帮助农民决定在合适的时间种植。一种可以用来进行预测的方法是数据挖掘分类方法。本文采用贝叶斯网络算法作为数据挖掘分类方法,基于天气信息对万隆县辣椒商品价格进行预测,并将价格分为经济类和非经济类。结果表明,贝叶斯网络预测模型对价格分类的准确率和召回率分别为1和0.94,平均准确率为85.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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