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All Journal Jurnal Elementaria Edukasia Journal of Computer Science and Technology Application
Palma Juanta
Universitas Prima
Computer Science & IT Education Other

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Literatur Review: Analisi Model Pembelajaran Blended Learning Dalam Pembelajaran IPA di Sekolah Dasar Apriani Sijabat; Palma Juanta; Festiyed; Yerimadesi
Jurnal Elementaria Edukasia Vol. 6 No. 2 (2023): juni
Publisher : Elementary Teacher Education Program, Majalengka University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jee.v6i2.5602

Abstract

The purpose of writing this article is to provide an overview of the learning process of blended learning and studies that examine the effect of blended learning models on learning outcomes. Where this can be used as a reference in the use of blended learning models in learning science. The method used is the method of meta-analysis which was carried out through studies in a number of international and national journals in the last 3 years. Sampling using a homogeneous sampling strategy. The samples in this study were 30 international and national journals that discussed the blended learning model, which in this case focused more on the effect of the blended learning model on student learning outcomes. Data were obtained using the results summary instrument and data analysis. The results of the research show that from several studies reviewed in the last 3 years, it is stated that the blended learning model is generally effective in improving student learning outcomes, especially in learning Natural Sciences. Therefore this blended learning model can be used as a learning model that can be used in science lessons at school.
Deep Learning Driven Big Data Architecture for Scalable Intelligent Network Threat Detection Sigit Anggoro; Palma Juanta; Ariesya Aprillia; Adele Valerry
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/vc0kmq21

Abstract

This study proposes a deep learning driven big data architecture designed to enable scalable and intelligent network threat detection in high volume traffic environments. Increasing network traffic volume and heterogeneity generated by enterprise systems, cloud services, and Internet of Things devices require more adaptive and intelligent security mechanisms beyond traditional signature-based approaches. This study aims to develop an intelligent threat-detection framework that leverages deep-learning models and big data analytics to enhance detection accuracy, scalability, and real-time response capabilities in large-scale network environments. A distributed big data architecture is integrated with advanced deep neural networks to process high-dimensional network traffic features, perform automated feature learning, and classify malicious activities using optimized training and validation strategies. The proposed framework is evaluated using benchmark intrusion detection datasets and simulated real-world network traffic scenarios to ensure robustness and generalizability. Experimental findings demonstrate that the proposed approach achieves superior detection accuracy, lower False-Positive Rates, and improved processing efficiency compared with conventional machine learning-based intrusion-detection systems. The integration of deep learning and big data analytics provides a scalable and adaptive solution for intelligent threat detection in computer networks, contributing to the development of next-generation cybersecurity systems capable of addressing evolving and sophisticated cyber attacks.
Co-Authors Adele Valerry Apriani Sijabat Ariesya Aprillia Festiyed Sigit Anggoro Yerimadesi