OPTIMALISASI ALGORITMA LONG SHORT-TERM MEMORY DALAM MEMPREDIKSI HARGA SAHAM (STUDI KASUS: PERBANKAN BADAN USAHA MILIK NEGARA)

Quini Suci Ambarwati, . (2024) OPTIMALISASI ALGORITMA LONG SHORT-TERM MEMORY DALAM MEMPREDIKSI HARGA SAHAM (STUDI KASUS: PERBANKAN BADAN USAHA MILIK NEGARA). Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

The convenience of the internet allows people to use investment products anywhere and anytime. Stocks are one such investment product, where individuals can own a portion of a company. One of the factors influencing stock price fluctuations is technical factors, which are based on historical stock price data. The Long Short-Term Memory (LSTM) algorithm has the advantage of storing data or information for the long-term using gates within the LSTM, including the forget gate, input gate, and output gate. There are three (3) datasets of banking stock prices from Badan Usaha Milik Negara (BUMN), namely Bank BRI, Bank BNI, and Bank Mandiri, with a total of 4479 rows of data for the three banks. From these datasets, the results obtained show the optimal LSTM algorithm values with a Mean Absolute Percentage Error (MAPE) ranging from 0.01 to 0.025 and R2 values ranging from 0.8 to 0.96. Keywords: Stocks, Prediction, LSTM, MAPE, R2

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 2010511050] [Pembimbing 1: Neny Rosmawarni] [Pembimbing 2: Mohamad Bayu Wibisono] {Penguji 1: Indra Permana Solihin] [Penguji 2: Muhammad Panji Muslim]
Uncontrolled Keywords: Stocks, Prediction, LSTM, MAPE, R2
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: QUINI SUCI AMBARWATI
Date Deposited: 30 Jul 2024 05:44
Last Modified: 05 Sep 2024 07:47
URI: http://repository.upnvj.ac.id/id/eprint/31629

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