Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach

Marleny, Finki Dona; Mambang, Mambang

doi:10.38043/tiers.v3i1.3604

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…1, June 2023:43-55 Overall the accuracy measurement results of the two models are shown in Table 3. Table 3 shows that LightGBM can achieve an accuracy of up to 85.49% on unbalanced data [24]. Even though the Random Forest is not able to form an accurate model on the same data.…”

Section: Resultsmentioning

confidence: 99%

Implementation of LightGBM and Random Forest in Potential Customer Classification

Sari,

Romadloni,

Lityaningrum

et al. 2023

TIERS

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Classification is one of the data mining techniques that can be used to determine potential custumers. Previous research show that the boosting method, especially LGBM, produces the highest accuracy value of all models, namely 100%. Meanwhile, for the two bagging methods, Random Forest produced the highest accuracy compared to Extra Trees, namely 99.03%. The research uses the LGBM and Random Forest methods to classify potential customers. The results of this study indicate that in imbalance data the LightGBM method has better accuracy than the Random Forest, which is 85.49%, when the Random Forest is unable to produce a model. The SMOTE method used in this study affects the accuracy of the random forest but does not affect the accuracy of LightGBM. Over all the Accuracy, Recall, Specificity, and Precision values, Random Forest produces a good value compared to LightGBM on balanced data. Meanwhile, LightGBM is able to handle unbalanced data.

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Section: Resultsmentioning

confidence: 99%

Implementation of LightGBM and Random Forest in Potential Customer Classification

Sari,

Romadloni,

Lityaningrum

et al. 2023

TIERS

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show abstract

“…The API serves as the communication channel between users and the machine learning system. In this case, the REST (Representational State Transfer) API communication protocol is utilized for seamless interaction between users and the system, particularly in Android application services [26] [27].…”

Section: Model Deploymentmentioning

confidence: 99%

Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets

Idhom,

Prasetya,

Riyantoko

et al. 2023

TIERS

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This research focuses on accurately classifying pneumonia in children under the age of 5 using X-ray images, considering the challenge of an imbalanced dataset. A modified VGG-16 CNN architecture is evaluated for pneumonia classification in Chest X-Ray Images. The study compares testing results with and without data augmentation techniques and explores the potential application of the model in an Android-based machine learning system for pneumonia diagnosis assistance. Using a dataset of 5,856 Chest X-Ray images categorized as normal or pneumonia, obtained from Kaggle, the research conducts two test scenarios: one without data augmentation and another with data augmentation techniques. The modified VGG-16 CNN algorithm's performance is evaluated using the accuracy metric. The results highlight the effectiveness of data augmentation in improving pneumonia classification accuracy. The augmented tests outperform the non-augmented ones, achieving an impressive 92% accuracy, indicating a significant 15% improvement over the non-augmented scenario. This improvement underscores the efficacy of data augmentation techniques in enhancing the CNN's ability to accurately classify pneumonia, particularly when faced with an imbalanced dataset. Furthermore, the research explores the potential integration of the trained model into an Android-based machine learning system for pneumonia diagnosis assistance. This integration would enable doctors to analyze X-ray images and identify potential pneumonia cases in patients. The integration of advanced machine learning systems in healthcare holds promise for improving patient care and the accuracy of pneumonia diagnoses. In summary, this research contributes to the accurate classification of pneumonia in children under 5 years old using X-ray images. It emphasizes the efficacy of data augmentation techniques in enhancing classification accuracy and explores the practical application of an Android-based machine learning system for pneumonia diagnosis assistance. These findings underscore the importance of advanced machine learning systems in healthcare and their potential to improve pneumonia diagnosis accuracy and enhance patient care.

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Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach

Cited by 2 publications

References 34 publications

Implementation of LightGBM and Random Forest in Potential Customer Classification

Implementation of LightGBM and Random Forest in Potential Customer Classification

Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets

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