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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Win, Thinzar Aung
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Optimizing Latent Space Representation for Tourism Insights: A Metaheuristic Approach Win, Thinzar Aung; Sunat, Khamron
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21419

Abstract

In the modern digital era, social media platforms with travel reviews significantly influence the tourism industry by providing a wealth of information on consumer preferences and behaviors. However, these textual reviews' complex and varied nature poses analytical challenges. This research employs advanced Natural Language Processing (NLP) techniques to process and analyze vast amounts of travel data efficiently, tackling the challenges posed by the diverse and detailed content in the tourism field. We have developed an innovative text clustering methodology that combines BERT's deep linguistic analysis capabilities (Bidirectional Encoder Representations from Transformers) with the thematic organization strengths of LDA (Latent Dirichlet Allocation). This hybrid model, further refined with the dimensionality reduction capabilities of ELM-AE and the optimization precision of PPSO (Phasor Particle Swarm Optimization), yields concise, contextually enriched text representations. Such refined data representations enhance the accuracy of K-means clustering, facilitating nuanced topic identification within the complex domain of travel reviews. This approach streamlines feature extraction and ensures rapid training and minimal loss, underscoring the model's effectiveness in distilling and reconstructing textual features. Our application of this hybrid LDA-BERT model to analyze TripAdvisor reviews of Thailand's shopping destinations reveals meaningful insights, significantly aiding in understanding customer experiences. Despite its contributions, this study acknowledges limitations, including biases in user-generated content and the intricacies of accurately interpreting sentiments and contexts within reviews. This research marks a significant step forward in utilizing NLP for tourism industry analysis, providing a pathway for future investigations to build upon.
Revolutionizing Anemia Classification with Multilayer Extremely Randomized Tree Learning Machine for Unprecedented Accuracy Saputra, Dimas Chaerul Ekty; Muryadi, Elvaro Islami; Futri, Irianna; Win, Thinzar Aung; Sunat, Khamron; Ratnaningsih, Tri
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1379

Abstract

Anemia is a prevalent global health issue that is characterized by a deficit in red blood cells or low levels of hemoglobin. This condition is influenced by various causes, including nutritional inadequacies, chronic diseases, and genetic predisposition. The incidence of the phenomenon exhibits variation across different geographical regions and demographic groups. This pioneering research investigates the identification and classification of anemia, potentially leading to transformative advancements in the discipline. The classification of anemia encompasses four distinct groups, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination. This comprehensive categorization offers clinicians a more refined and detailed comprehension of the condition. The integration of deep learning and machine learning in the Multilayer Extremely Randomized Tree Learning Machine (MERTLM) model represents a departure from traditional approaches and a significant advancement in the field of medical categorization accuracy. The MERTLM approach integrates randomized tree with multilayer extreme learning machine (M-ELM) representation learning, hence emphasizing the possibility of interdisciplinary collaboration in the field of diagnostics. In addition to its impact on anemia, artificial intelligence (AI) is playing a significant role in revolutionizing medical diagnosis by emphasizing the integration of innovative methods. This study utilizes the combined capabilities of machine learning and deep learning to improve accuracy. Notably, recent developments have resulted in an exceptional accuracy rate of 99.67%, precision of 99.60%, sensitivity of 99.47%, and an amazing F1-Score of 99.53%. This study represents a significant advancement in the field of anemia research, providing valuable insights that may be applied to intricate medical issues and enhancing the quality of patient care.
Co-Authors Dimas Chaerul Ekty Saputra Futri, Irianna Muryadi, Elvaro Islami Sunat, Khamron Tri Ratnaningsih