Prediksi Harga Saham Menggunakan Improved Multiple Linear Regression untuk Pencegahan Data Outlier

Izzah, Abidatul; Widyastuti, Ratna

doi:10.22219/kinetik.v2i3.268

Cited by 16 publications

(18 citation statements)

References 3 publications

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“…Prediksi atau peramalan adalah memperkirakan sesuatu yang akan terjadi dimasa mendatang, jadi sesuatu yang di prediksi dari hasil akan ada dimasa yang dating [12]. Prediksi merupakan salah satu cabang machine learning yang metode penyelesaian masalahnya menggunakan supervised learning dan menggunakan time series atau rentetan waktu sebagai datanya [13].…”

Section: Pendahuluanunclassified

Prediksi Harga Saham Bank Bri Menggunakan Algoritma Linear Regresion Sebagai Strategi Jual Beli Saham

Putra¹,

Ramadhani²,

Burhanuddin³

2022

JournalofDinda.DataScience.IT.DataAnalytics

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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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Section: Pendahuluanunclassified

Prediksi Harga Saham Bank Bri Menggunakan Algoritma Linear Regresion Sebagai Strategi Jual Beli Saham

Putra¹,

Ramadhani²,

Burhanuddin³

2022

JournalofDinda.DataScience.IT.DataAnalytics

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“…Keduanya merupakan kuantitatif data. β adalah koefisien, maka MLR dinotasikan pada (6) (6) IV. KESIMPULAN Variabel yang digunakan untuk menjadi data masukan prediksi harga saham dapat bermacam-macam.…”

Section: Hasil Dan Pembahasanunclassified

Studi Literatur Mengenai Prediksi Harga Saham Menggunakan Machine Learning

Hindrayani

Diyasa

Riyantoko

et al. 2020

santika

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Prediksi harga saham tidak hanya menarik bagi para ekonom namun juga menarik dibahas untuk para analis. Harga saham yang berfluktuasi dan terpengaruhi oleh banyak faktor menyebabkan prediksi dengan berbagai macam teknik dilakukan. Salah satu metode yang dapat memprediksi harga saham adalah dengan Machine Learning. Pada penelitian ini membahas mengenai studi literatur variabel masukan, metode, dan hasil dari literature terdahulu. Harga pembukaan, penutupan, terendah, dan tertinggi saham harian merupakan variabel masukan yang paling banyak digunakan. Metode yang paling banyak digunakan adalah jaringan syaraf tiruan. Metode tersebut juga menghasilkan tingkat kesalahan yang relatif kecil.

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“…The advantage of the Multiple Regression method is that it predicts based on interactive patterns from relevant data in the past, and usually, line patterns will form in the model. Multiple Regression Method is generated from analyzing the pattern of relationships between variables [10]. Research [11] states that the Multiple Regression method has 50% accuracy in predicting demand for bicycle rental, and research [12] has 84.5% accuracy in predicting property prices with several parameters.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia

Saputra

Saadah

Yunanto

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Prediksi Harga Saham Menggunakan Improved Multiple Linear Regression untuk Pencegahan Data Outlier

Cited by 16 publications

References 3 publications

Prediksi Harga Saham Bank Bri Menggunakan Algoritma Linear Regresion Sebagai Strategi Jual Beli Saham

Prediksi Harga Saham Bank Bri Menggunakan Algoritma Linear Regresion Sebagai Strategi Jual Beli Saham

Studi Literatur Mengenai Prediksi Harga Saham Menggunakan Machine Learning

Analysis of Random Forest, Multiple Regression, and Backpropagation Methods in Predicting Apartment Price Index in Indonesia

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