ANALISIS PERBANDINGAN KINERJA METODE ARIMA DAN LONG SHORT-TERM MEMORY (LSTM) DALAM PREDIKSI HARGA SAHAM MENGGUNAKAN DATA HISTORIS

DZULWIDAAD, EBPAN RIZKI (2026) ANALISIS PERBANDINGAN KINERJA METODE ARIMA DAN LONG SHORT-TERM MEMORY (LSTM) DALAM PREDIKSI HARGA SAHAM MENGGUNAKAN DATA HISTORIS. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The uncertain and continuous fluctuations in stock prices make price prediction a major challenge in the capital market. This study aims to analyze and compare the performance of the Long Short-Term Memory (LSTM) algorithm and the Autoregressive Integrated Moving Average (ARIMA) method in projecting stock price trends for 2026. The historical data used underwent pre-processing stages, including normalization and the splitting of training and testing datasets. In this modeling, LSTM serves as a deep learning approach to capture nonlinear patterns and long-term dependencies, while ARIMA is utilized as a conventional statistical method for linear patterns and stationary data. The prediction performance of both models was evaluated using MAE, RMSE, and MAPE error metrics. The experimental results indicate that the ARIMA method delivers a significantly superior prediction accuracy compared to the LSTM model in forecasting stock prices for the year 2026. Keywords: Stock Price Forecasting, ARIMA, Long Short-Term Memory (LSTM), Comparative Analysis, Deep Learning Harga saham yang terus berubah secara tidak pasti membuat prediksi nilainya menjadi tantangan besar di pasar modal. Penelitian ini bertujuan untuk menganalisis dan membandingkan performa algoritma Long Short-Term Memory (LSTM) dan Autoregressive Integrated Moving Average (ARIMA) dalam memproyeksikan tren harga saham tahun 2026. Data historis yang digunakan melalui tahap pra-pemrosesan, termasuk normalisasi serta pembagian data latih dan data uji. Dalam pemodelan ini, LSTM digunakan sebagai representasi deep learning untuk menangkap pola nonlinier dan memori jangka panjang, sedangkan ARIMA digunakan sebagai metode statistik konvensional untuk pola linier dan data stasioner. Evaluasi hasil prediksi kedua model diukur menggunakan metrik kesalahan MAE, RMSE, dan MAPE. Hasil eksperimen menunjukkan bahwa metode ARIMA menghasilkan tingkat akurasi prediksi yang lebih unggul secara signifikan dibandingkan model LSTM dalam memprediksi harga saham pada tahun 2026. Kata kunci: Prediksi Harga Saham, LSTM, ARIMA, Analisis Kompratif, Deep Learning

Item Type:	Thesis (S1)
NIM/NIDN Creators:	41522120008
Uncontrolled Keywords:	Prediksi Harga Saham, LSTM, ARIMA, Analisis Kompratif, Deep Learning
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika > 004.6 Interfacing and Communications/Tampilan Antar Muka (Interface) dan Jaringan Komunikasi Komputer > 004.66 Data Transmission Modes and Data Switching Methods/Metode Transmisi Data 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 153 Conscious Mental Process and Intelligence/Intelegensia, Kecerdasan Proses Intelektual dan Mental > 153.1 Memory and Learning/Memori dan Pembelajaran > 153.15 Learning/Pembelajaran
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	08 Sep 2026 23:52
Last Modified:	08 Sep 2026 23:52
URI:	http://repository.mercubuana.ac.id/id/eprint/103718

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