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IMPLEMENTATION OF LONG SHORT-TERM MEMORY MODEL IN WEB-BASED STOCK PRICE PREDICTION

IMPLEMENTASI MODEL LONG SHORT-TERM MEMORY PADA PREDIKSI HARGA SAHAM BERBASIS WEB

AnoaTIK: Jurnal Teknologi Informasi dan Komputer · Vol. 3 No. 2 (2026) · pp. 70–76

DOI
10.33772/anoatik.v3i2.116
ISSN
2987-7652
Published
2026-01-18
Publisher
Program Studi Ilmu Komputer FMIPA-UHO
Language
ind
License
creativecommons.org/licenses/by-sa/4.0

Abstract

English

This study aims to develop a web-based stock price prediction system using the Long Short-Term Memory (LSTM) algorithm to forecast the highest and lowest prices of stocks listed in the LQ45 index. LSTM was chosen for its ability to recognize long-term patterns in time series data and its more stable performance compared to methods such as ARIMA and GRU. The system features an interactive interface and user activity logging to enhance usability and user experience. Evaluation results show that the LSTM model performs well, with MAPE below 3% and RMSE values varying according to stock volatility. The best results were achieved by ACES, with RMSE values of 28,772 (High) and 27,142 (Low), and MAPE of 2,19% and 2,2%, while AMMN had the highest error rates with RMSE values of 247,154 and 281,926, and MAPE of 2,42% and 2,79%. The system successfully delivers real-time predictions through a responsive and user-friendly web interface.

Bahasa Indonesia

Penelitian ini bertujuan membangun sistem prediksi harga saham berbasis web menggunakan algoritma Long Short-Term Memory (LSTM) untuk memprediksi harga tertinggi dan terendah saham-saham dalam indeks LQ45. LSTM dipilih karena kemampuannya mengenali pola jangka panjang dalam data runtun waktu dan menunjukkan performa lebih stabil dibandingkan metode seperti ARIMA dan GRU. Sistem dilengkapi antarmuka interaktif serta fitur pencatatan log aktivitas pengguna untuk meningkatkan kemudahan akses dan pengalaman pengguna. Hasil evaluasi menunjukkan performa prediksi yang cukup baik, dengan MAPE di bawah 3% dan nilai RMSE yang bervariasi tergantung volatilitas saham. Saham ACES mencatat hasil terbaik dengan RMSE masing-masing 28,772 (High) dan 27,142 (Low), serta MAPE 2,19% dan 2,2%, sedangkan saham AMMN menunjukkan tingkat kesalahan tertinggi dengan RMSE sebesar 247,154 dan 281,926, serta MAPE 2,42% dan 2,79%. Sistem mampu menyajikan hasil prediksi secara real-time melalui tampilan web yang responsif dan mudah digunakan.

Keywords

Collaboration network of the authors

4 authors · 4 co-authorship links

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  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
LM Bahtiar AksaraUniversitas Halu Oleo1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://anoatik.uho.ac.id/index.php/atik/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">IMPLEMENTATION OF LONG SHORT-TERM MEMORY MODEL IN WEB-BASED STOCK PRICE PREDICTION</dc:title>
	<dc:title xml:lang="id-ID">IMPLEMENTASI MODEL LONG SHORT-TERM MEMORY PADA PREDIKSI HARGA SAHAM BERBASIS WEB</dc:title>
	<dc:creator>Muslimin, Fadhillah</dc:creator>
	<dc:creator>Tenriawaru, Andi</dc:creator>
	<dc:creator> Tohamba, Muhammad Riansyah</dc:creator>
	<dc:subject xml:lang="id-ID">Prediksi Harga Saham</dc:subject>
	<dc:subject xml:lang="id-ID">LSTM</dc:subject>
	<dc:subject xml:lang="id-ID">MAPE</dc:subject>
	<dc:subject xml:lang="id-ID">Data Time Series</dc:subject>
	<dc:subject xml:lang="id-ID">Sistem Berbasis Web</dc:subject>
	<dc:subject xml:lang="en-US">Stock Price Prediction</dc:subject>
	<dc:subject xml:lang="en-US">LSTM</dc:subject>
	<dc:subject xml:lang="en-US">Time Series Data</dc:subject>
	<dc:subject xml:lang="en-US">MAPE</dc:subject>
	<dc:subject xml:lang="en-US">Web-Based System</dc:subject>
	<dc:description xml:lang="en-US">This study aims to develop a web-based stock price prediction system using the Long Short-Term Memory (LSTM) algorithm to forecast the highest and lowest prices of stocks listed in the LQ45 index. LSTM was chosen for its ability to recognize long-term patterns in time series data and its more stable performance compared to methods such as ARIMA and GRU. The system features an interactive interface and user activity logging to enhance usability and user experience. Evaluation results show that the LSTM model performs well, with MAPE below 3% and RMSE values varying according to stock volatility. The best results were achieved by ACES, with RMSE values of 28,772 (High) and 27,142 (Low), and MAPE of 2,19% and 2,2%, while AMMN had the highest error rates with RMSE values of 247,154 and 281,926, and MAPE of 2,42% and 2,79%. The system successfully delivers real-time predictions through a responsive and user-friendly web interface.</dc:description>
	<dc:description xml:lang="id-ID">Penelitian ini bertujuan membangun sistem prediksi harga saham berbasis web menggunakan algoritma Long Short-Term Memory (LSTM) untuk memprediksi harga tertinggi dan terendah saham-saham dalam indeks LQ45. LSTM dipilih karena kemampuannya mengenali pola jangka panjang dalam data runtun waktu dan menunjukkan performa lebih stabil dibandingkan metode seperti ARIMA dan GRU. Sistem dilengkapi antarmuka interaktif serta fitur pencatatan log aktivitas pengguna untuk meningkatkan kemudahan akses dan pengalaman pengguna. Hasil evaluasi menunjukkan performa prediksi yang cukup baik, dengan MAPE di bawah 3% dan nilai RMSE yang bervariasi tergantung volatilitas saham. Saham ACES mencatat hasil terbaik dengan RMSE masing-masing 28,772 (High) dan 27,142 (Low), serta MAPE 2,19% dan 2,2%, sedangkan saham AMMN menunjukkan tingkat kesalahan tertinggi dengan RMSE sebesar 247,154 dan 281,926, serta MAPE 2,42% dan 2,79%. Sistem mampu menyajikan hasil prediksi secara real-time melalui tampilan web yang responsif dan mudah digunakan.</dc:description>
	<dc:publisher xml:lang="id-ID">Program Studi Ilmu Komputer FMIPA-UHO</dc:publisher>
	<dc:date>2026-01-18</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://anoatik.uho.ac.id/index.php/atik/article/view/116</dc:identifier>
	<dc:identifier>10.33772/anoatik.v3i2.116</dc:identifier>
	<dc:source xml:lang="en-US">AnoaTIK: Jurnal Teknologi Informasi &amp; Komputer; Vol. 3 No. 2 (2025): Desember 2025; 70-76</dc:source>
	<dc:source xml:lang="id-ID">AnoaTIK: Jurnal Teknologi Informasi dan Komputer; Vol 3 No 2 (2025): Desember 2025; 70-76</dc:source>
	<dc:source>2987-7652</dc:source>
	<dc:source>10.33772/anoatik.v3i2</dc:source>
	<dc:language>ind</dc:language>
	<dc:relation>https://anoatik.uho.ac.id/index.php/atik/article/view/116/41</dc:relation>
	<dc:rights xml:lang="id-ID">Hak Cipta (c) 2026 Fadhillah Muslimin, Andi Tenriawaru, Muhammad Riansyah  Tohamba</dc:rights>
	<dc:rights xml:lang="id-ID">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>