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All Journal Journal of Mathematics Education and Application (JMEA) BAREKENG: Jurnal Ilmu Matematika dan Terapan MAJAMATH: Jurnal Matematika dan Pendidikan Matematika MES: Journal of Mathematics Education and Science Indonesian Journal of Education and Mathematical Science PHI: Jurnal Pendidikan Matematika Al'adzkiya International of Education and Sosial (AIoES) Journal Journal Mathematics Education Sigma [JMES] JUTECH : Journal Education and Technology Education Achievement: Journal of Science and Research Jurnal Abdimas Kartika Wijayakusuma Indo-MathEdu Intellectuals Journal MATH-EDU: Jurnal Ilmu Pendidikan Matematika Tsaqila Jurnal Pendidikan dan Teknologi ALSYS : Jurnal Keislaman dan Ilmu Pendidikan ARZUSIN: Jurnal Manajemen dan Pendidikan Dasar OMEGA: Jurnal Keilmuan Pendidikan Matematika Hanif Journal of Information Systems Jurnal Tadris Matematika (JTMT) Jurnal Pengabdiaan Masyarakat Larisma
Azis, Zainal
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Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : JUTECH : Journal Education and Technology

 1 
IMPLEMENTASI DATA MINING DALAM PREDIKSI HARGA SAHAM BBNI DENGAN PEMODELAN MATEMATIKA MENGGUNAKAN METODE LSTM DENGAN OPTIMASI ADAM Rahman, Alrafiful; Istiyowati, Lucia Sri; Valentinus, Valentinus; Ivan, Ivan; Azis, Zainal
JUTECH : Journal Education and Technology Vol 5, No 2 (2024): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v5i2.4137

Abstract

Stock price prediction plays a crucial role in investment decision-making, allowing investors to maximize profits and minimize risks. This study implements the Long Short-Term Memory (LSTM) method with Adam optimization to predict the stock price of Bank Negara Indonesia (BBNI) based on historical stock price data from the Indonesia Stock Exchange (2001-2023). LSTM is chosen for its ability to handle sequential data and identify long-term patterns in time series. Meanwhile, the Adam optimization algorithm is used to accelerate model convergence and improve prediction accuracy. The data used includes daily stock prices (closing prices), and the research process involves data collection, preprocessing, LSTM model creation, Adam optimization, training, evaluation, and prediction. The experimental results show that the model with a batch size of 64 and 100 epochs yields an R² of 0.9928 and a MAPE of 1.53%, indicating a very high prediction accuracy. With an accuracy of 98.46%, the LSTM model with Adam optimization proves to be effective in predicting stock prices, providing excellent results for applications in investment strategies. This study demonstrates the great potential of applying data mining and machine learning techniques in more informed and data-driven stock market analysis.
Co-Authors Afnaria, Afnaria Akbar, Rahmat Ammy, Putri Maisyarah Annisa, Dewi Sry Ardiningtyas, Mesayu Azmi, Muhammad Bima Darmawati Darmawati Dedy Kiswanto Fajar, Maulana Haloho, Elysha Jefirayani Hasibuan, Ahmad Riady Ikhlas Hamzani Irvan Irvan Irvan, Irvan Ivan Ivan Khoiruddin Matondang Lubis, Tri Resti Andini Lubis, Ulfa Annisa Lucia Sri Istiyowati Marah Doly Nasution Muslihuddin, Rahmat Nasution, Asrindah Nurhayati Nurhayati Nurliana, Maulidya Nurlinda, Epi Nurul Maulida Surbakti Panggabean, Suvriadi Pasha, Leony Ayu Diah Purba, Enniy purba, oktaviana nirmala Puspita, Ananda Putri, Linda Syah Rahim, Rani Rahman, Alrafiful Risna Mira Bella Saragih Rizka Fahruza Siregar RR. Ella Evrita Hestiandari Siregar, Rosliana Siswadi Siswadi Syahlan, Syahlan Tua Halomoan Harahap, Tua Halomoan Valentinus, Valentinus Yusma, Ira Zega, Lipur Ju Priman