Janur Syah Putra, R. Ramadhani, Auliya Burhanuddin
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引用次数: 2

摘要

股票是证明投资者在公司所有权的证券。股票具有波动性,这使得股票难以预测。股票预测是对股票价格,特别是对未来将出现的印尼人民银行公司的股票价格进行估计,以增加投资者在进行投资决策时的获利机会。在2019冠状病毒病大流行期间,“一带一路”银行的股价在四个月内经历了大幅起伏,这说明了该股对事件的敏感性。因此,对股票价格进行预测,降低投资者接受的风险是非常重要的。预测本身需要时间序列数据。时间序列是按时间顺序收集的数据。用于时间序列数据的方法是线性回归,因为这种方法可以处理时间序列数据。基于这些问题,本文将运用线性回归方法对印尼人民银行公司进行股票预测研究。从2008年1月1日至2020年6月1日期间,印尼人民银行的股价数据来自investing.com网站。数据的处理从预处理开始,确定属性,去除不必要的属性,改变数据类型的内容,然后对拆分数据进行处理,将数据集划分为训练数据和测试数据。本研究中使用的属性为Date和Price,使用的数据分布为60:40、65:35、70:30、75:25和80:20。最佳比例为80:20,训练和测试准确率分别为0.89和0.91,然后将每个训练数据和测试数据输入线性回归模型进行预测。使用MAPE计算预测的误差结果,训练数据的误差百分比为13.751%,测试数据的误差百分比为13.773%,整体数据的误差百分比为13.755%。MAPE结果表明,线性回归方法可以用来预测BRI银行的股价。
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Prediksi Harga Saham Bank Bri Menggunakan Algoritma Linear Regresion Sebagai Strategi Jual Beli Saham
Shares are securities as proof of ownership of investors in a company. Stocks have a volatile nature, this makes stocks difficult to predict. Stock prediction is an effort to estimate the stock price, especially in the Bank Rakyat Indonesia company that will appear in the future, and to increase investors' profit opportunities in making investment decisions. During the COVID-19 pandemic, Bank BRI's shares experienced significant ups and downs in four months, which illustrates the sensitivity of the stock to an event. Therefore, it is important to predict stock prices to reduce the risk accepted by investors. The prediction itself requires time series data. Time series is data that is collected sequentially from time to time. The method used for time series data is Linear Regression because this method can handle time-series data. Based on these problems, stock prediction research will be conducted at the Bank Rakyat Indonesia company using the Linear Regression method. Bank Rakyat Indonesia share price data were obtained from the investing.com website from the period starting on January 1, 2008, to June 1, 2020. The data is processed starting from preprocessing to determine attributes, remove unnecessary attributes, and change the contents of the data type, then process split data to divide the dataset into training and test data. The attributes used in this study are Date and Price and the distribution of the data used is 60:40, 65:35, 70:30, 75:25, and 80:20. The best ratio is at 80:20 which produces train and test accuracy of 0.89 and 0.91, Then each training data and testing data are entered into the linear regression model for prediction. The error results from the predictions were calculated using MAPE and yielded a percentage of 13.751% for training data, 13.773% for test data, and 13.755% for overall data. The MAPE results indicate that the linear regression method can be used to predict the stock price of BRI Bank.
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