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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Global Education Journal
Agustina, Triya
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Computer Science & IT Education Social Sciences Other

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Performance Analysis of Random Forest Algorithm for Network Anomaly Detection using Feature Selection Agustina, Triya; Masrizal, Masrizal; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13625

Abstract

As the volume and complexity of computer network traffic continue to increase, network administrators face a growing challenge in monitoring and discovering unusual activity. To keep the network safe and functioning, detecting anomalies is essential. Machine learning-based anomaly detection techniques have become increasingly popular in recent years. This is due to the fact that conventional anomaly detection methods make it difficult to detect unknown and complex attacks. This research aims to conduct a performance analysis of two feature selection methods using the random forest algorithm using the UNSW-NB15 dataset to determine which model is most effective in detecting network traffic anomalies. The models evaluated were random forest with the filter method and random forest with the wrapper method. A number of metrics used for model performance assessment are accuracy, F1-score, receiver operating characteristic curve, and precision-recall. Dataset collection, data pre-processing, feature selection, model construction, and evaluation are the main components of the research methodology. The research results show that the Random Forest approach with the Filter method has an accuracy of 0.8950, F1-score of 0.8333, ROC score of 0.8928, and a precision-recall value of 0.8347. Meanwhile, the approach using the Wrapper method obtained an accuracy of 0.9151, F1-score of 0.8510, ROC score of 0.9136, and a precision-recall value of 0.8637. This shows that the performance of Random Forest with the Wrapper method is superior in all assessment metrics. Random Forest with the Wrapper Method is the right choice of model for detecting network traffic anomalies because of its stable performance and ability to handle complex patterns
Implementation of the Picture and Picture Learning Model in Science Learning for Grade V Students Agustina, Triya; Hamdan, Hamdan; Egok, Asep Sukenda
Global Education Journal Vol. 3 No. 2 (2025): Global Education Journal (GEJ)
Publisher : Civiliza Publishing, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59525/gej.v3i2.848

Abstract

Learning activities are the process of interaction between students, educators, and learning resources in the learning environment. This study aims to determine the completeness of the learning outcomes of Natural and Social Sciences (IPAS) of fifth-grade students of SD Negeri 1 Sumber Harta after being given the Picture and Picture learning model. The background of this study is the low learning outcomes of students in the subject of IPAS caused by the use of monotonous learning methods and less involvement of student activity. This study uses a quantitative approach with a quasi-experimental method and a one-group pre-test and post-test design. The subjects of the study were all 18 fifth-grade students. The data collection technique used a written test in the form of essay questions. The results showed that the average pre-test score of 37.35 increased to 78.38 in the post-test, with the learning completeness level increasing from 11.11% to 88.89%. Statistical tests using the z-test show that the calculated z value is 4.97, which is greater than the z (table) of 1.64 at a significance level of 0.05, which means the alternative hypothesis is accepted. Thus, it can be concluded that the application of the picture and picture learning model can significantly improve students' science learning outcomes.
Co-Authors Egok, Asep Sukenda Hamdan Hamdan Irmayanti Irmayanti, Irmayanti