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All Journal In Theos : Jurnal Pendidikan dan Teologi KOMNET : Jurnal Komputer, Jaringan dan Internet
Jansen, Arnoldus
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Peran Bapak sebagai Pendidik Iman Anak Jansen, Arnoldus; Pacheconai, Carlos De; Daryanto, Daryanto
In Theos : Jurnal Pendidikan dan Theologi Vol. 1 No. 10 (2021): Oktober
Publisher : Actual Insight

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.929 KB) | DOI: 10.56393/intheos.v1i10.1194

Abstract

Bapak adalah sosok penting dalam keluarga. Bapak sering dipandang sebagai kepala keluarga. Sebab itu peran bapak dalam keluarga sungguh penting. Sudah sewajarnya bapak bertanggung jawab atas anggota keluarganya dan memastikan segala kebutuhan terpenuhi termasuk pendidikan iman anak. Artikel ini akan membahas peran bapak sebagai pendidik iman anak dalam keluarga di Stasi Santo Petrus Lalapin Paroki Santo Vincentius A Paulo Batulicin. Penelitian ini bertujuan untuk mengetahui bagaimana peran bapak sebagai pendidik iman anak di Stasi Santo Petrus Lalapin Paroki Santo Vincentius A Paulo Batulicin. Artikel ini menggunakan penelitian berbasis kuantitatif. Subyek yang digunakan dalam penelitian ini adalah bapak bapak yang ada di Stasi Santo Petrus Lalapin sebanyak 28 orang. Pengumpulan data menggunakan angket tertutup dengan bantuan katekis setempat dalam prosesnya. Teknik analisa data menggunakan rumus F Prosen dan Scoring. Berdasarkan penelitian hasil dari angket yang sudah diberikan menujukan sebagian besar para bapak berhasil membangun iman anak.
Network Intrusion Detection Using Machine Learning in Network Intrusion Detection Systems (NIDS) Jansen, Arnoldus; Yuswanto, Dery; Styawan, Budi; Girinata, I Made Candra
KOMNET : Jurnal Komputer, Jaringan dan Internet Vol. 4 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengabdian Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/yt59ac51

Abstract

Computer network security has become a crucial aspect as dependence on network-based services increases. One important mechanism in maintaining network security is the Network Intrusion Detection System (NIDS), which functions to detect suspicious activity or attacks on network traffic. The traditional signature-based approach has limitations in detecting new attacks (zero-day attacks). Therefore, this study proposes the application of Machine Learning and Deep Learning methods to improve network intrusion detection capabilities. The CIC-IDS2017 dataset was used as the data source because it represents various types of modern network attacks. The research stages included data pre-processing, feature selection, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The models used include Random Forest as a representation of Machine Learning and Long Short-Term Memory (LSTM) as a representation of Deep Learning. The results show that the Deep Learning approach is capable of providing better detection performance on complex attacks compared to conventional Machine Learning methods. This research is expected to serve as a reference in the development of adaptive and accurate network intrusion detection systems.
Co-Authors Daryanto Daryanto Girinata, I Made Candra Pacheconai, Carlos De Styawan, Budi Yuswanto, Dery