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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MEDIA KONSERVASI Mechatronics, Electrical Power, and Vehicular Technology Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology Applied Engineering and Technology
Kohei Arai
Graduate School of Science and Engineering, Saga University, Saga City, Japan
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Journal : Applied Engineering and Technology

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Decision tree based algorithms for Indonesian Language Sign System (SIBI) recognition Nugraha, Agil Zaidan; Salsabila, Reni Fatrisna; Handayani, Anik Nur; Wibawa, Aji Prasetya; Hitipeuw, Emanuel; Arai, Kohei
Applied Engineering and Technology Vol 3, No 2 (2024): August 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i2.1536

Abstract

Indonesian Sign Language System (SIBI) recognition plays a crucial role in improving effective communication for individuals with hearing loss in Indonesia. To support automatic SIBI recognition, this research presents a performance analysis of two main algorithms, namely Decision Tree and C4.5, in the context of the SIBI recognition task. This research utilizes a rich SIBI dataset that includes a variety of SIBI signs used in everyday communication. Data pre-processing, model construction with both algorithms, and model performance evaluation using accuracy, precision, recall, and F1-score metrics are all part of the study. Regarding SIBI recognition accuracy, the experimental results demonstrate that the Decision Tree performs better than Decision Tree. The Decision Tree also makes models that are easier to understand, which is important for making communication systems based on SIBI.
Ensemble learning approaches for predicting heart failure outcomes: A comparative analysis of feedforward neural networks, random forest, and XGBoost Ariyanta, Nadindra Dwi; Handayani, Anik Nur; Ardiansah, Jevri Tri; Arai, Kohei
Applied Engineering and Technology Vol 3, No 3 (2024): December 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i3.1750

Abstract

Heart failure is a leading cause of morbidity and mortality worldwide, and early prediction of outcomes is critical for timely intervention and improved patient care. Accurate prediction models can help clinicians identify high-risk patients, optimize treatment strategies, and reduce healthcare costs. In this study, we developed and evaluated machine learning models to predict mortality in patients with heart failure using a medical dataset of 299 patients with 13 clinical variables collected in 2015. Four models were tested, including a Feedforward Neural Network (FNN), Random Forest, XGBoost, and an ensemble model combining all three models. The experimental process included data preprocessing, feature scaling, and stratified cross-validation to ensure robust evaluation. The results showed that the ensemble model achieved the best performance with an ROC-AUC of 0.9134 and an F1 score of 0.7439, outperforming individual models such as Random Forest (ROC-AUC: 0.9117) and XGBoost (ROC-AUC: 0.9130). FNN, despite having the highest accuracy (0.8455), showed lower performance in terms of recall and precision, likely due to its sensitivity to overfitting on small datasets. These results highlight the effectiveness of ensemble learning in medical prediction tasks, especially for handling complex, high-dimensional health data. The proposed ensemble model has the potential to be integrated into clinical decision support systems, enabling real-time risk assessment and personalized treatment plans for heart failure patients. Future research should explore larger, multicenter datasets, incorporate advanced feature engineering techniques, and investigate the integration of deep learning architectures such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to process sequential data such as ECG signals.
Co-Authors Aji Prasetya Wibawa Ali Ridho Barakbah Andrie Asmara, Rosa Anik Nur Handayani Ariyanta, Nadindra Dwi Burhanuddin, Mohd Aboobaider Damayanti, Farradila Ayu Daud, Nathan Dimas Wahyu Wibowo Faqih, Kamil Heru Wahyu Herwanto Hitipeuw, Emanuel Indriati Indriati Jevri Tri Ardiansah Kurnianingtyas, Diva Marji Marji Mustika Mentari Nugraha, Agil Zaidan Priharta, Ari Primadi, Wahyu Rahmat Samudra Anugrah, Muhammad Resty Wulanningrum Rosa Andrie Asmara Sakti Oktafiansyah, Haidar Salsabila, Reni Fatrisna