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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Teknik Elektro dan Komputer TRIAC Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) JHCLS SmartComp INJECT Interdisciplinary Journal of Communication
Christian Sri kusuma Aditya, Christian Sri kusuma
Teknik Informatika, Institut Teknologi Sepuluh Nopember
Religion Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

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Optimizing Indonesian Banking Stock Predictions with DBSCAN and LSTM Purwandhani, Septiannisa Alya Shinta; Sajiatmoko, Aletta Agigia Novta; Aditya, Christian Sri Kusuma
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4439

Abstract

Investing in the stock market is challenged by high volatility, which often leads to inaccurate price predictions. Prediction models often struggle to handle the fluctuation phenomenon and produce unstable forecasts. This study aims to predict stock prices in three banks, namely PT Bank Central Asia Tbk (BBCA), PT Bank Rakyat Indonesia (Persero) Tbk (BBRI), and PT Bank Mandiri (Persero) Tbk (BMRI) using Long Short-Term Memory (LSTM) with the integration of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for anomaly detection. DBSCAN is applied with an epsilon (ε) of 0.5 and a minimum of 5 samples using Euclidean distance. The LSTM model consists of two hidden layers with 50 units, optimized using Adam, and applying the Mean Squared Error (MSE) loss function. The results show that DBSCAN improves prediction accuracy under several conditions. For BBCA stock, the lowest MSE was 0.003 at the 2nd fold with DBSCAN compared to 0.006 without DBSCAN. For BMRI stock achieved an MSE of 0.003 at the 4th fold with DBSCAN, while the 5th fold without DBSCAN obtained 0.000. For BBRI stock showed the best MSE of 0.003 at the 2nd fold with DBSCAN and the 5th fold without DBSCAN. These results show that the integration of DBSCAN can improve prediction especially when extreme price fluctuations occur. This research contributes to the development of stock price prediction methods that can be one of the benchmarks for investors before making decisions so that they do not experience losses.
Co-Authors Agus Zainal Arifin AINA, LISTYA NUR Aji, Bayu Puspito Alif Akbar Fitrawan, Alif Akbar ASHURI, PUTRI INTAN CAHYANI, INDAH ARDHIA Chandranegara, Didih Rizki Chastine Fatichah Diana Purwitasari Evi Dwi Wahyuni Ferdiansyah, Danny Firdausita, Nuris Sabila Fuad Nasvian, Moch Gayatri, Agnes Ayu Maharani Dyah Hamdan Nafiatur Rosyida Handoko, Chanavaro Bayu Hani’ah, Mamluatul K., Halif Fachrizal Erliawan Kusumo, Wisnu Prayogo Lailiyah, Asmaul Nur Putri Hidayah Purwandhani, Septiannisa Alya Shinta S, Vinna Rahmayanti Sajiatmoko, Aletta Agigia Novta Salma, Rania Alfita Setyarini, Dela Ananda Sunan, Rohid Aji Ujilast, Novia Adelia Wicaksono, Galih Wasis Yuda Munarko Yufis Azhar