Analisis prediktif harga saham dengan menggunakan metode arima dan arima-lstm = Predictive analysis of stock prices using arima and arima-lstm methods

Tauhalomoan, Patrick Mangara (2024) Analisis prediktif harga saham dengan menggunakan metode arima dan arima-lstm = Predictive analysis of stock prices using arima and arima-lstm methods. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Pergerakan harga saham dalam prakteknya cenderung memiliki pergerakan yang acak. Hal ini membuat para investor kesulitan dalam membuat sebuah keputusan investasi. Oleh sebab itu, peramalan harga saham akan menjadi alat bantu ataupun referensi para investor dalam membuat keputusan investasi. Peramalan harga saham dapat dibuat dengan beragam metode seperti model deret waktu, kecerdasan buatan seperti artificial neural network, regresi, dan beragam metode lainnya. Dalam meramalkan harga saham tersebut, penelitian ini akan menggunakan dua metode, yaitu ARIMA dan hybrid ARIMA-LSTM dalam memprediksi harga saham harian di Indonesia. Penelitian ini akan melakukan pemodelan ARIMA dan ARIMA-LSTM, untuk kemudian dilakukan prediksi terhadap harga saham harian di Indonesia melalui kedua metode tersebut. Saham harian yang diteliti merupakan periode Januari 2014 sampai dengan Desember 2023. Setelah hasil prediksi dari kedua metode diperoleh, maka akan dilakukan perbandingan untuk mengetahui metode yang terbaik. Pada penelitian ini, diperoleh hasil bahwa lima dari enam saham yang diprediksi membuktikan bahwa metode ARIMA-LSTM lebih unggul dalam memprediksi harga saham harian di Indonesia. Hal ini dapat dilihat dari nilai MSE, RMSE, dan MAPE yang dihasilkan pada kedua model. / The movement of stock prices in practice tends to exhibit random fluctuations, making it difficult for investors to make investment decisions. Therefore, stock price forecasting serves as a tool or reference for investors in making investment decisions. Stock price forecasting can be carried out using various methods such as time series models, artificial intelligence techniques like artificial neural networks, regression, and various other methods. In this research, two methods, namely ARIMA and hybrid ARIMA-LSTM, are employed to predict daily stock prices in Indonesia. The study involves modeling ARIMA and ARIMA-LSTM, followed by predictions of daily stock prices in Indonesia using both methods. The daily stock data under investigation are from January 2014 to December 2023. After obtaining the prediction results from both methods, a comparison is conducted to determine the superior method. The study reveals that five out of six stocks predicted demonstrate the superiority of the ARIMA-LSTM method in predicting daily stock prices in Indonesia. This thing can be seen from the values of MSE, RMSE, and MAPE generated by both models.

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