eprintid: 66498
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/06/64/98
datestamp: 2024-08-14 02:25:27
lastmod: 2024-08-14 02:25:27
status_changed: 2024-08-14 02:25:27
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Agung Rashif Madani, NIM.: 20106050083
title: PENERAPAN TEMPORAL FUSION TRANSFORMER (TFT) UNTUK PREDIKSI DATA MULTIVARIATE DERET WAKTU: STUDI KASUS HARGA SAHAM BBCA, BBRI, BMRI, BBNI, DAN BRIS
ispublished: pub
subjects: 004
divisions: jur_tinf
full_text_status: restricted
keywords: TFT, Transformer, Multivariate, Saham
note: Pembimbing: Nurochman, S.Kom., M.Kom.
abstract: Technology and increased data volume drive Time Series Forecasting research, which is important in stock analysis for competitive advantage. However, challenges such as determining sufficient information and potential bias in model selection need to be addressed. Transformer-based Deep Learning models such as Temporal Fusion Transformer (TFT) have shown high effectiveness in handling multivariate time series data. Nonetheless, comprehensive studies are still needed in the implementation and evaluation of Transformer-based models such as TFT specifically for multivariate time series data prediction in the context of the financial industry.
To overcome this shortcoming, this study proposes the use of TFT to determine the best model hyperparameter configuration and data preprocessing strategy in predicting the stock prices of the five largest banks in Indonesia (BBCA, BBRI, BMRI, BBNI, BRIS). This study considers two types of encoder-decoder, namely 5-1 and 25-5, two data normalization techniques, namely Standard Scaler and Robust Scaler, and three different batch sizes, namely 32, 64, and 128. A total of 12 combinations of TFT model hyperparameters were explored.
The results showed that the optimal combination of data preprocessing, the use of a 25-5 encoder-decoder, normalization with Standard Scaler, and a batch size of 32. The evaluation results were MAE of 31.9601, RMSE of 40.1071, and SMAPE of 0.0074.
date: 2024-07-24
date_type: published
pages: 74
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Agung Rashif Madani, NIM.: 20106050083  (2024) PENERAPAN TEMPORAL FUSION TRANSFORMER (TFT) UNTUK PREDIKSI DATA MULTIVARIATE DERET WAKTU: STUDI KASUS HARGA SAHAM BBCA, BBRI, BMRI, BBNI, DAN BRIS.  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/66498/1/20106050083_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/66498/2/20106050083_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf