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All Journal Jurnal Transformatika JOIN (Jurnal Online Informatika) Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Journal of Dinda : Data Science, Information Technology, and Data Analytics JUSTIN (Jurnal Sistem dan Teknologi Informasi) Nuansa Informatika Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Harani, Nisa Hanum
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Journal : Building of Informatics, Technology and Science

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Pendekatan LSTM Berbasis Deep Learning dalam Memprediksi Fluktuasi Harga Cabai Pertiwi, Aryka Anisa; Harani, Nisa Hanum; Prianto, Cahyo
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8100

Abstract

The significant fluctuation in chili prices in Indonesia leads to economic instability, particularly for consumers and market stakeholders. This study aims to develop a daily chili price prediction model using the Long Short-Term Memory (LSTM) algorithm based on deep learning, designed to capture seasonal patterns and long-term dependencies in historical data. The research adopts the CRISP-DM approach, encompassing business understanding, data processing, model training, and implementation into a web-based dashboard. The dataset, collected from Pagar Alam City between 2022 and 2024, includes features such as previous prices, chili sub-variants, sinusoidal time transformations, and market conditions. The LSTM regression model demonstrated high performance, achieving an R² score of 0.9567, a MAE of 1,402.92, and an RMSE of 2,595.98. Additionally, a classification model was developed to predict price status (increase, decrease, stable) as a decision-support tool. The deployment of this system into an interactive dashboard enables real-time price predictions. These results indicate that the LSTM-based approach is not only technically accurate but also offers a practical solution for commodity price monitoring and decision-making in the food sector.
Stock Price Prediction Using LSTM and XGBoost with Social Media Sentiment Harani, Nisa Hanum; Marismati, Marismati
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8284

Abstract

The influence of social media on financial markets is growing and motivates research on the predictive role of sentiment in stock price movements. Bank Negara Indonesia (BBNI) is part of the Danantara holding company, and BBNI's strategic position is an important indicator for measuring the performance of the broader financial ecosystem in Indonesia. This study analyzes the influence of social media sentiment on the stock price prediction of Bank Negara Indonesia (BBNI), which is part of the state-owned holding company Danantara. Historical market data is combined with sentiment indicators obtained from public conversations on X/Twitter. Daily sentiment features are then integrated with market variables, including OHLCV data, to form a combined dataset. Two machine learning approaches were employed: Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost). The results revealed contrasting patterns between the two models. The LSTM Baseline consistently produced RMSE around (≈46–65) across all scenarios. However, XGBoost-Extended is the best-performing and recommended model for sentiment-integrated prediction with RMSE (≈30–40).
Co-Authors Adiningrum, Nur Tri Ramadhanti Andarsyah, Roni Andriyanto, Achmad Annisah, Wulan Nur Damayanti, Fanny Shafira Fatonah, Rd. Nuraini Siti Ferdinan, Dani Habibi, Roni Hutapea, Juwita Stefany Ilyas Tri Khaqiqi, M Latifah, Isnaenti Nur Marismati, Marismati Nugraha, Fikri Aldi Pertiwi, Aryka Anisa Prianto, Cahyo Rahayu, Woro Isti Salsabila, Helmi Setyawan, M. Yusril Helmi Setyawan, Muhammad Yusril Helmi Syafrial Fachri Pane, Syafrial Fachri Tri Khaqiqi, M. Ilyas Trigreisian, Alwizain Almas Zain, Rofi Nafiis Zuhri, Burhanudin