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

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Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification Saputra, Dimas Chaerul Ekty; Ma'arif, Alfian; Sunat, Khamron
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20898

Abstract

This study addresses the necessity for more advanced diagnostic tools in managing diabetes, a chronic metabolic disorder that leads to disruptions in glucose, lipid, and protein metabolism caused by insufficient insulin activity. The research investigates the innovative application of machine learning models, specifically Stacked Multi-Kernel Support Vector Machines Random Forest (SMKSVM-RF), to determine their effectiveness in identifying complex patterns in medical data. The innovative ensemble learning method SMKSVM-RF combines the strengths of Support Vector Machines (SVMs) and Random Forests (RFs) to leverage their diversity and complementary features. The SVM component implements multiple kernels to identify unique data patterns, while the RF component consists of an ensemble of decision trees to ensure reliable predictions. Integrating these models into a stacked architecture allows SMKSVM-RF to enhance the overall predictive performance for classification or regression tasks by optimizing their strengths. A significant finding of this study is the introduction of SMKSVM-RF, which displays an impressive 73.37% accuracy rate in the confusion matrix. Additionally, its recall is 71.62%, its precision is 70.13%, and it has a noteworthy F1-Score of 71.34%. This innovative technique shows potential for enhancing current methods and developing into an ideal healthcare system, signifying a noteworthy step forward in diabetes detection. The results emphasize the importance of sophisticated machine learning methods, highlighting how SMKSVM-RF can improve diagnostic precision and aid in the continual advancement of healthcare systems for more effective diabetes management.
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 Alfian Ma’arif Dimas Chaerul Ekty Saputra Futri, Irianna Muryadi, Elvaro Islami Tri Ratnaningsih Win, Thinzar Aung