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All Journal Jurnal Teknik Informatika (JUTIF) JPNM : Jurnal Pustaka Nusantara Multidisiplin
Navila, Ilma
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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The Persebaran Produksi Tanaman Biofarmaka Menurut Kecamatan di Kota Bogor Tahun 2020-2022 Navila, Ilma
JPNM Jurnal Pustaka Nusantara Multidisiplin Vol. 2 No. 1 (2024): February: Jurnal Pustaka Nusantara Multidisiplin
Publisher : SM Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59945/jpnm.v2i1.105

Abstract

Indonesia has a variety of plants that are often used by the community as traditional medicine. Therefore, it is necessary to conduct research on the distribution of medicinal plants based on geographic information systems that aim to determine the distribution of medicinal plants (biopharmaceuticals), in addition to providing information and adding insight to the public about the distribution of medicinal plants in sub-districts in the city of Bogor. The method used in this study involved collecting spatial data.
Banking Stock Price Prediction Dashboard Using Long Short-Term Memory Navila, Ilma; Nada, Noora Qotrun; Renaldy, Ramadhan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5802

Abstract

The high volatility of banking sector stocks (BBCA, BBRI, BMRI) and the limitations of conventional forecasting methods in handling non-linear data necessitate robust and adaptive predictive models. This study aims to develop an integrated stock price prediction system utilizing a Stacked Long Short-Term Memory (LSTM) architecture embedded within a Flask-based interactive web dashboard. Adopting the CRISP-DM framework, the model was trained using daily and hourly historical data from Yahoo Finance to accommodate both short-term and medium-term forecasting. Backtesting evaluation demonstrated that the LSTM model achieved Mean Absolute Percentage Error (MAPE) values below 2% for daily single-step predictions and below 0.5% for hourly intraday predictions. Furthermore, in a 7-period recursive projection, the proposed LSTM proved highly robust in mitigating error accumulation compared to Linear Regression and Support Vector Regression (SVR), successfully maintaining MAPE values below 5% for all issuers. The implementation of this dashboard system provides a significant impact on financial informatics by bridging advanced deep learning predictive algorithms into a practical, real-time decision support system for investment analysis.
Co-Authors Noora Qotrun Nada, Noora Qotrun Renaldy, Ramadhan