eprintid: 76833
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/07/68/33
datestamp: 2026-06-19 07:12:44
lastmod: 2026-06-19 07:12:44
status_changed: 2026-06-19 07:12:44
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Amanda Riyas Utami, NIM.: 22106010003
title: PERBANDINGAN METODE LONG SHORT-TERM MEMORY (LSTM) DAN GATED RECURRENT UNIT (GRU) DENGAN PENDEKATAN UNIVARIAT DAN MULTIVARIAT UNTUK MEMPREDIKSI HARGA SAHAM (STUDI KASUS : HARGA SAHAM BMRI PERIODE 2015-2024)
ispublished: pub
subjects: 515.6
divisions: jur_mat
full_text_status: restricted
keywords: Deep Learning, GRU, LSTM, Prediksi Harga Saham
note: Sri Utami Zuliana, S.Si., M.Sc., Ph.D.
abstract: A time series is a sequence of observations that are time-oriented or arranged
chronologically for an observed variable. Time series data are widely used
in analysis and forecasting, including stock price analysis. Stock price data are generally
non-stationary, fluctuating, highly volatile, and tend to form nonlinear patterns.
These characteristics cause the assumptions of classical time series methods,
particularly Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU),
which are used to predict stock prices using univariate and multivariate approaches.
The data used in this study were the stock price data of PT Bank Mandiri Tbk
(BMRI) from 2015 to 2024. The research stages included Exploratory Data Analysis
(EDA), preprocessing, modeling, and architecture optimization, with evaluation
conducted using Mean Squared Error (MSE) and Mean Absolute Error (MAE). The
results showed that both LSTM and GRU models with univariate and multivariate
approaches were able to predict stock prices with movements relatively similar to
the actual data. The univariate GRU model was identified as the best architecture
consisting of a sliding window of 30, one hidden layer, 128 units, dropout of 0.1,
batch size of 32, and MSE and MAE values of 0.0012 and 0.0270, respectively. In
addition, GRU demonstrated higher sensitivity than LSTM, resulting in more aggressive
predictions of price declines, while LSTM produced predictions that were
more realistic.
date: 2026-05-12
date_type: published
pages: 172
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Amanda Riyas Utami, NIM.: 22106010003  (2026) PERBANDINGAN METODE LONG SHORT-TERM MEMORY (LSTM) DAN GATED RECURRENT UNIT (GRU) DENGAN PENDEKATAN UNIVARIAT DAN MULTIVARIAT UNTUK MEMPREDIKSI HARGA SAHAM (STUDI KASUS : HARGA SAHAM BMRI PERIODE 2015-2024).  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/76833/1/22106010003_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/76833/2/22106010003_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf