Classification of Spiral and Non-Spiral Galaxies using Decision Tree Analysis and Random Forest Model: A Study on the Zoo Galaxy Dataset

Alfaris, Lulut; Siagian, Ruben Cornelius; Muhammad, Aldi Cahya; Nyuswantoro, Ukta Indra; Laeiq, Nazish; Mobo, Froilan D.

doi:10.15294/sji.v10i2.44027

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…This technique addresses the constraints encountered with a single Decision Tree, including issues such as overfitting and potential bias of the dataset. RF enhances predictive power by amalgamating insights from multiple decision trees, resulting in robust and precise predictions [34], [35], [36]. The RF algorithm begins with random sample selection, decision-tree construction, and voting or averaging to generate a final prediction.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Ridwan,

Sembiring,

Setiawan

et al. 2024

SJI

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Purpose: As the complexity and diversity of cyberattacks continue to grow, traditional security measures fall short in effectively countering these threats within web-based environments. Therefore, there is an urgent need to develop and implement innovative, advanced techniques tailored specifically to detect and address these evolving security risks within web applications.Methods: This research focuses on analyzing attack detection in log access servers using machine learning classification with two primary approaches: expert labeling integration and best model selection. Expert labeling determines whether log entries are safe or indicate an attack.Result: Validation in labeling was applied using different datasets to minimize errors and increase confidence in the resulting dataset. Experimental results show that the Decision Tree and Random Forest models have nearly identical accuracy rates, around 89.3%-89.4%, while the ANN model has an accuracy of 81%.Novelty: This study proposes a fusion of expert knowledge in labeling log entries with a rigorous process of selecting the best classification model. This integration has not been extensively explored in previous research, offering a novel approach to enhancing attack detection within web applications. The research contribution lies in the integration of expert security assessment and the selection of the best model for detecting attacks on server access logs, along with validating labels using various datasets from different log devices to enhance confidence in the analysis results.

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Section: Random Forest (Rf)mentioning

confidence: 99%

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Ridwan,

Sembiring,

Setiawan

et al. 2024

SJI

View full text Add to dashboard Cite

show abstract

“…The entropy equation serves as a pivotal tool in DT analysis, particularly when calculating the impurity at a node, represented as: 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 (𝑃) = − ∑ 𝑝(𝑖). log 2 (𝑝(𝑖)) (8) In this equation, 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 (𝑃) represents the entropy of the dataset P, where 𝑝(𝑖) denotes the probability that an instance in dataset P belongs to class i [67].…”

Section: Decision Treementioning

confidence: 99%

Impact of a Synthetic Data Vault for Imbalanced Class in Cross-Project Defect Prediction

Putri Nabella,

Rudy Herteno,

Setyo Wahyu Saputro

et al. 2024

j.electron.electromedical.eng.med.inform

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Software Defect Prediction (SDP) is crucial for ensuring software quality. However, class imbalance (CI) poses a significant challenge in predictive modeling. This study delves into the effectiveness of the Synthetic Data Vault (SDV) in mitigating CI within Cross-Project Defect Prediction (CPDP). Methodologically, the study addresses CI across ReLink, MDP, and PROMISE datasets by leveraging SDV to augment minority classes. Classification utilizing Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF), also model performance is evaluated using AUC and t-Test. The results consistently show that SDV performs better than SMOTE and other techniques in various projects. This superiority is evident through statistically significant improvements. KNN dominance in average AUC results, with values 0.695, 0.704, and 0.750. On ReLink, KNN show 16.06% improvement over the imbalanced and 12.84% over SMOTE. Similarly, on MDP, KNN 20.71% improvement over the imbalanced and a 10.16% over SMOTE. Moreover, on PROMISE, KNN 13.55% improvement over the imbalanced and 7.01% over SMOTE. RF displays moderate performance, closely followed by LR and DT, while NB lags behind. The statistical significance of these findings is confirmed by t-Test, all below the 0.05 threshold. These findings underscore SDV's potential in enhancing CPDP outcomes and tackling CI challenges in SDV. With KNN as the best classification algorithm. Adoption of SDV could prove to be a promising tool for enhancing defect detection and CI mitigation

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Classification of Spiral and Non-Spiral Galaxies using Decision Tree Analysis and Random Forest Model: A Study on the Zoo Galaxy Dataset

Cited by 2 publications

References 24 publications

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Impact of a Synthetic Data Vault for Imbalanced Class in Cross-Project Defect Prediction

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