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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Applied Engineering and Technology
Ariyanta, Nadindra Dwi
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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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.
Machine Learning for Open-ended Concept Map Proposition Assessment: Impact of Length on Accuracy Wicaksono, Reo; Dwi Prasetya, Didik; Elbaith Zaeni, Ilham Ari; Ariyanta, Nadindra Dwi; Hirashima, Tsukasa
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5044

Abstract

Open-ended concept maps allow learners to freely connect concepts, enriching understanding by linking new and prior knowledge. However, manually assessing proposition quality is time-consuming and subjective. This study proposes an automatic classification model for proposition quality assessment using term frequency–inverse document frequency (TF-IDF), a text representation method based on word frequency, and several machine learning algorithms. Two datasets were used are Relational Database with an average 5 words per proposition and Cybersecurity Authentication with an average 10 words per proposition. Comparative experiments with Support Vector Machine (SVM), a supervised classification algorithm, K-Nearest Neighbor, Random Forest, and Long Short-Term Memory (LSTM), a recurrent neural network for sequence data, revealed that SVM with RBF kernel achieved the highest performance on shorter propositions 87% accuracy, Cohen’s Kappa 0.76, while LSTM showed greater strength in handling longer propositions 85% accuracy, Cohen’s Kappa 0.69. These findings suggest that proposition length influences model effectiveness. The proposed approach can reduce the burden of manual assessment, increase the objectivity of evaluation, and support more efficient implementation of concept maps in education.
Assessing the Semantic Alignment in Multilingual Student-Teacher Concept Maps Using mBERT Ariyanta, Nadindra Dwi; Prasetya, Didik Dwi; Elbaith Zaeni, Ilham Ari; Hirashima, Tsukasa; Wicaksono, Reo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5046

Abstract

This study examines the effectiveness of mBERT (Multilingual Bidirectional Encoder Representations from Transformers) in assessing semantic alignment between student and teacher concept maps in multilingual educational contexts, comparing its performance with TF-IDF. Using datasets in both Indonesian and English, the study demonstrates that mBERT outperforms TF-IDF in capturing complexsemantic relationships, achieving 96% accuracy, 96% precision, 100% recall, and a 98% F1 score in the Indonesian dataset. In contrast, TF-IDF achieved higher precision (73%) and accuracy (79%) in the English dataset, where mBERT recorded 54% accuracy, 47% precision, but 90% recall. Semantic alignment was measured using cosine similarity to calculate the cosine of the angle between vectorsrepresenting textual embeddings generated by both models. This method facilitates cross-linguistic semantic comparison, overcoming challenges related to word frequency and syntactic variations. While mBERT’s computational demands and the study’s limited linguistic scope suggest room for improvement, the findings highlight the potential for hybrid models and emphasize the transformative impact of AI-driven tools, such as mBERT, in fostering inclusive and effective multilingual education.
Co-Authors Anik Nur Handayani Didik Dwi Prasetya Hirashima, Tsukasa Jevri Tri Ardiansah Kohei Arai Wicaksono, Reo Zaeni, Ilham Ari Elbaith