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All Journal AnoaTIK: Jurnal Teknologi Informasi dan Komputer Intechno Journal : Information Technology Journal
Budi Wijaya Rauf
Universitas Halu Oleo
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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IMPLEMENTASI ALGORITMA LONG SHORT-TERM MEMORY PADA SISTEM KLASIFIKASI MAHASISWA BERPOTENSI DROP OUT Niken Mutiara; La Ode Saidi; Budi Wijaya Rauf
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 1 (2025): Juni 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i1.109

Abstract

This research aims to produce a classification system for students who have the potential to drop out. This classification system is expected to help identify students who have the potential to drop out early on in prevention efforts. This research uses academic data in the form of Semester Grade Point Average (IPS) 1-7, Cumulative Grade Point Average Semester 7 (IPKS7), and Cumulative SKS 7, as well as non-academic data including Study Program and Entry Path as classification parameters. The method used is the Long Short-Term Memory (LSTM) algorithm with system development using the CRISP-DM approach. System testing is done using black box testing method and performance evaluation using confusion matrix. The results showed that the classification system developed achieved an accuracy rate of 93% based on confusion matrix evaluation, and all system functionality runs as expected based on the results of black box testing.
Forecasting A Major Banking Corporation Stock Prices Using LSTM Neural Networks Budi Wijaya Rauf
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1888

Abstract

The increasing complexity of stock market predictions necessitates advanced computational techniques to address the unique challenges posed by financial data's non-linear and volatile nature. This study aims to leverage Long Short-Term Memory (LSTM) neural networks to accurately forecast stock prices, using historical data collected from a major banking corporation as a primary source. The LSTM model excels at processing sequential time-series data, allowing it to predict monthly stock closing prices over a one-year horizon with a high degree of precision. Our findings indicate a Root Mean Squared Error (RMSE) of 3.2, underscoring the model's efficiency and reliability in financial forecasting tasks. The novelty of this research lies in the systematic incorporation of preprocessing techniques and fine-tuned hyperparameters to optimize model performance. Furthermore, this study explores the practical implications of implementing LSTM models in real-world trading scenarios, analyzing their adaptability to dynamic market conditions and their potential integration into automated trading systems. These findings contribute to the growing body of knowledge in financial analytics and demonstrate the viability of machine learning-based solutions for accurate and robust market predictions.
Co-Authors La Ode Saidi Niken Mutiara