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All Journal JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Arya Ardhi Baskara
Politeknik Pengayoman Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Comparative Evaluation of Data Mining Classification Algorithms For Predicting Earthquake Alert Levels Arya Ardhi Baskara
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 2 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i2.481

Abstract

Earthquakes are one of the most destructive natural disasters, particularly in Indonesia, which is located at the convergence of three active tectonic plates. Conventional early warning systems generally rely on real-time vibration detection but lack the capability to provide comprehensive predictions about the potential severity of an earthquake. This study aims to address these limitations by applying data mining techniques and machine learning algorithms to classify earthquake alert levels based on seismic parameters, including magnitude, depth, Community Determined Intensity (CDI), Modified Mercalli Intensity (MMI), and significance (Sig). A dataset of 1,300 earthquake records was obtained and processed using the Knowledge Discovery in Database (KDD) methodology, which includes data selection, preprocessing, transformation, modeling, and evaluation. Five classification algorithms were compared: Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), and Neural Network. Model performance was evaluated using confusion matrix metrics such as accuracy, precision, recall, and F1-score. The results indicate that Random Forest achieved the highest performance with an accuracy of 88.52% and macro recall of 88.90%, outperforming other algorithms. Decision Tree ranked second with balanced performance, while KNN and Neural Network achieved moderate results. Naïve Bayes performed the weakest. Overall, Random Forest is the most reliable algorithm for supporting earthquake early warning systems.
Performance Evaluation of Random Forest for Hypertension Risk Prediction Arya Ardhi Baskara
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 2 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i2.483

Abstract

Hypertension is a major global health concern and a leading risk factor for cardiovascular disease, stroke, and kidney failure. Early prediction of hypertension is crucial because the condition is often asymptomatic in its initial stages and late detection increases the likelihood of severe complications. This study aims to develop and evaluate a predictive model for hypertension using the Random Forest algorithm, a robust ensemble learning method well-suited for medical data classification. The dataset used in this research was obtained from Kaggle and contains 1,985 records with 11 attributes representing demographic, lifestyle, and clinical risk factors. Preprocessing was performed to ensure data quality, followed by Random Forest classification with different parameter settings. The model was evaluated using 5-fold and 10-fold cross-validation with various numbers of trees ranging from 50 to 250. Performance metrics included accuracy, precision, recall, F1-score, and AUC. Experimental results demonstrated that the Random Forest algorithm achieved consistently high performance, with accuracy above 93%, precision above 95%, recall above 91%, F1-scores above 93%, and AUC values between 0.986 and 0.991. These findings confirm that Random Forest is highly effective and reliable for predicting hypertension risk. The study highlights the algorithm’s potential as a decision-support tool for early detection, enabling preventive measures and improving public health outcomes.
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