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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization
Ekachandra, Kristian
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Computer Science & IT Engineering

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Optimizing long short-term memory hyperparameter for cryptocurrency sentiment analysis with swarm intelligence algorithms Ekachandra, Kristian; Kristiyanti, Dinar Ajeng
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2753-2764

Abstract

This study investigates the application of swarm intelligence algorithms, specifically particle swarm optimization (PSO), ant colony optimization (ACO), and cat swarm optimization (CSO), to optimize long short-term memory (LSTM) networks for sentiment analysis in the context of cryptocurrency. By leveraging these optimization techniques, we aimed to enhance both the accuracy and computational efficiency of LSTM models by fine-tuning critical hyperparameters, notably the number of LSTM units. The study involved a comparative analysis of LSTM models optimized with each algorithm, evaluating performance metrics such as accuracy, loss, and execution time. Results indicate that the PSO-LSTM model achieved the highest accuracy at 86.08% and the lowest loss at 0.57, with a reduced execution time of 58.43 seconds, outperforming both ACO-LSTM and CSO-LSTM configurations. These findings underscore the effectiveness of PSO in tuning LSTM parameters and emphasize the potential of swarm intelligence for enhancing neural network performance in real-time sentiment analysis applications. This research contributes to advancing optimized deep learning techniques in high dimensional data environments, with implications for improving cryptocurrency sentiment predictions.
Comparison of Salp Swarm Algorithm and Particle Swarm Optimization as Feature Selection Techniques for Recession Sentiment Analysis in Indonesia Kristiyanti, Dinar Ajeng; Sanjaya, Samuel Ady; Irmawati, Irmawati; Ekachandra, Kristian; Suhali, Jason; Hairul Umam, Akhmad
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3102

Abstract

Amidst global economic uncertainty, this study focuses on Twitter sentiment during the global recession issue on social media, especially in Indonesia. By utilizing sentiment analysis, this study uses machine learning algorithms such as Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) which are still less than optimal on high-dimensional Twitter data. The purpose of this study is to improve the accuracy of conventional machine learning using basic metaheuristic algorithms, namely the Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO) as feature selection. From January to May 2023, this study captures the evolving sentiment in response to economic conditions. Data preprocessing, including labeling through the TextBlob and VADER libraries, sets the stage for the analysis. Performance is compared based on labeling techniques, feature selection, and classification algorithms. Specifically, when applied to VADER labeled data without feature selection, the SVM model achieves an outstanding accuracy of 83% and an F1 score of 67%—notably, the application of SSA and PSO results in a reduction in model accuracy by 1%. However, the application of SSA and PSO slightly reduced the model accuracy performance by 1%. On the TextBlob labeled data, SVM showed an outstanding performance (80% accuracy, 77% F1 score). Interestingly, PSO on TextBlob data with SVM significantly decreased the model's performance. These findings contribute significantly to understanding the intricacies of sentiment dynamics during economic uncertainty on social media platforms, with SVM emerging as a strong choice for practical sentiment analysis.
Co-Authors Dinar Ajeng Kristiyanti, Dinar Ajeng Hairul Umam, Akhmad Irmawati Irmawati Sanjaya, Samuel Ady Suhali, Jason