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All Journal JE (Journal of Empowerment) Journal of Informatics, Information System, Software Engineering and Applications (INISTA)
Arisagas, Muhamad Rifki
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Social Sciences

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Peran Orang Tua dalam Mengawasi Penggunaan Gawai pada Anak Arisagas, Muhamad Rifki; Monariska, Erma
JE (Journal of Empowerment) Vol 5, No 1 (2024): JUNI
Publisher : Universitas Suryakancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/je.v5i1.4133

Abstract

Abstrak Penelitian ini bertujuan meningkatkan kesadaran orang tua mengenai pengawasan penggunaan gawai oleh anak-anak dan pentingnya keamanan siber. Dengan memfokuskan pada pencegahan kecanduan dan ancaman digital, program ini melibatkan lokakarya, pertunjukan teater, dan diskusi yang dipandu oleh ahli. Metode komprehensif ini bertujuan untuk memberikan pengetahuan serta strategi bagi orang tua dan anak agar dapat berinteraksi dengan dunia digital secara bijaksana dan aman. Hasil penelitian menunjukkan adanya peningkatan kesadaran orang tua tentang risiko digital, literasi digital anak-anak, dan komunikasi tentang keamanan siber. Keberhasilan program tercermin dari partisipasi aktif dan antusiasme masyarakat. Pendekatan ini menawarkan contoh strategi multidimensi yang efektif dalam menghadapi tantangan digital dan mendorong penggunaan gawai yang sehat di kalangan anak. Temuan ini memberikan panduan berharga bagi pendidik, orang tua, dan komunitas dalam mengawasi penggunaan gawai dan meningkatkan kesadaran akan keamanan siber untuk generasi mendatang. Abstract This study seeks to boost awareness of the role parents play in overseeing their children's smartphone use and promoting cybersecurity awareness. The program, aimed at preventing digital addiction and addressing online threats, includes workshops, theatrical performances, and expert-led discussions. By employing a comprehensive approach, the research provides parents and children with the tools and knowledge needed for safe and informed digital interactions. Results reveal increased parental awareness of digital risks, improved digital literacy among children, and better communication about cybersecurity between parents and their kids. The program's success is reflected in the active engagement and enthusiasm of community members. This method offers a multidimensional strategy for tackling current digital challenges and encouraging healthy smartphone habits in children. The study's findings provide valuable insights for educators, parents, and communities, suggesting that similar initiatives can help create a safer digital environment for future generations.
Analysis of NSL-KDD for the Implementation of Machine Learning in Network Intrusion Detection System Yuliana, Yuliana; Supriyadi, Dhoni Hanif; Fahlevi, Mohammad Reza; Arisagas, Muhamad Rifki
Journal of INISTA Vol 6 No 2 (2024): May 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v6i2.1389

Abstract

In the world of network data communication, anomaly detection is a crucial element in identifying abnormal behavior among the flowing data packets. Research in the field of intrusion detection often focuses on the search and analysis of anomalous patterns and the misuse of communication data. The research methodology in this study adopts CRISP-DM (Cross-Industry Standard Process for Data Mining) as the framework. The primary goal of this research is to conduct a comparative analysis of classification techniques to identify normal and anomaly records within network data. For this purpose, a publicly available standard dataset, NSL-KDD, is used. The NSL-KDD dataset consists of 41 attributes with relevance, and the 42nd attribute is used to identify normal class and four attack classes. The results of the analysis using the NSL-KDD dataset, applying the CRISP-DM methodology and machine learning techniques in the Network Intrusion Detection System, reveal that the Decision Tree model has the highest accuracy, achieving 100% on the training data and 80% on the testing data. These findings are compared with the results of using other models such as Random Forest, Logistic Regression, and K-Nearest Neighbor. This discovery has significant implications for enhancing NIDS's ability to recognize network threats and improve network system security.
Co-Authors Erma Monariska Fahlevi, Mohammad Reza Supriyadi, Dhoni Hanif Yuliana Yuliana