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All Journal Knowledge Engineering and Data Science Science in Information Technology Letters Applied Engineering and Technology
Jevri Tri Ardiansah
Universitas Negeri Malang
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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SQL Logic Error Detection by Using Start End Mid Algorithm Jevri Tri Ardiansah; Aji Prasetya Wibawa; Triyanna Widyaningtyas; Okazaki Yasuhisa
Knowledge Engineering and Data Science Vol 1, No 1 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (608.845 KB) | DOI: 10.17977/um018v1i12018p33-38

Abstract

Data base is an important part of a system and it stores data to be manipulated. A language called SQL (Structured Query Language) is used for manipulating those data to make needed information. There are two types of error which make SQL more difficult in practical implementation. They are syntax error and logic error. The difference between them is that syntax error can be detected by compiler so it is easy to learn by its warning. But compiler does not show error warning if logical error was occurred. It makes logic error is more difficult to understand than syntax error. To help data base's user to learn SQL in practical implementation, web based SQL compiler that be able to detect syntax and logic error is developed by using Start End Mid algorithm.
Comparative analysis of decision tree and random forest classifiers for structured data classification in machine learning Kinasih, Agnes Nola Sekar; Handayani, Anik Nur; Ardiansah, Jevri Tri; Damanhuri, Nor Salwa
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1746

Abstract

This study explores the application of machine learning techniques, specifically classification, to improve data analysis outcomes. The primary objective is to evaluate and compare the performance of Decision Tree and Random Forest classifiers in the context of a structured dataset. Using the Elbow Method for optimal clustering alongside decision tree and random forest for classification algorithms, this research investigates the effectiveness of each method in accurately categorizing data. The study employs K-Means clustering to segment the data and Decision Trees and Random Forests for classification tasks. Dataset used in this research was obtained from Kaggle consisting of 13 attributes and 1048575 rows, all of which are numeric. The key results show that Random Forest outperforms Decision Trees in terms of classification accuracy, precision, recall, and F1 score, providing a more robust model for data classification. The performance improvement observed in Random Forest, particularly in handling complex datasets, demonstrates its superiority in generalizing across varied classes. The findings suggest that for applications requiring high accuracy and reliability, Random Forest is preferable to Decision Trees, especially when the dataset exhibits high variability. This research contributes to a deeper understanding of how different machine learning models can be applied to real-world classification problems, offering insights into the selection of the most appropriate model based on specific data characteristics.
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 Anik Nur Handayani Ariyanta, Nadindra Dwi Damanhuri, Nor Salwa Kinasih, Agnes Nola Sekar Kohei Arai Okazaki Yasuhisa Triyanna Widyaningtyas