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All Journal JURNAL PENDIDIKAN DAN ILMU FISIKA Julia Jurnal
Fiqih Akbari
Politeknik Negeri Sambas
Computer Science & IT Education Physics

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Membangun Network Video Recorder (NVR) MenggunakanSet Top Box (STB) Pada Rusunawa Poltesa Berbasis Wireless Yudistira; Theresia Widji Astuti; Muhammad Usman; Ellys Mey Sundari; Fiqih Akbari
Julia: Jurnal Ilmu Komputer An Nuur Vol 6 No 1 (2026): juliajournal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v6i1.57

Abstract

This research proposes an innovative solution for recording and storing surveillance video data in the parking environment of Rusunawa Poltesa using a Set Top Box (STB)-based Network Video Recorder (NVR) with Armbian Linux operating system. STBs, typically used for television signal reception, are adapted into intelligent devices to efficiently control and manage surveillance cameras (IP Camera) at a lower cost compared to conventional NVRs. The use of wireless technology increases the flexibility of camera placement without the limitations of cables, simplifies installation, and allows for the adjustment of camera positions according to surveillance needs. The STB-based NVR implementation uses the open-source Shinobi Community Edition (Shinobi CE) software written in Node.js, enabling video recording from IP cameras in H.264 and H.265 formats. This research aims to find an integrated and innovative solution to improve the surveillance system in Rusunawa Poltesa in a modern, efficient, and cost-effective manner. The research results show that the STB-based NVR system can record video at frame rates up to 15 FPS at HD resolution and 5 FPS at SD resolution, with memory consumption of approximately 4.02 MB per minute for HD resolution (1920x1080) and 1.98 MB per minute for SD resolution (1280x720).
Transformation of Students' Scientific Reasoning Through Artificial Intelligence-Based Media: A Systematic Review Fiqih Akbari; Musa Marsel Maipauw; Ronny Mugara
Jurnal Pendidikan dan Ilmu Fisika Vol 5 No 2 (2025): Desember 2025
Publisher : Universitas Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52434/jpif.v5i2.43176

Abstract

This study aims to examine the transformation of students' scientific reasoning through the use of Artificial Intelligence (AI)-based media in science education. The method employed is a Systematic Literature Review (SLR), with a comprehensive literature search conducted in the Scopus database. This study reviews various articles that discuss the application of AI in science education, particularly focusing on its impact on students' scientific reasoning. The findings show that AI significantly contributes to enhancing students' abilities to analyze data, identify patterns, and understand complex scientific concepts. AI technologies, such as Graph Neural Networks (GNN) and generative AI models (VAE and GAN), enable students to conduct virtual experiments and simulations that accelerate their understanding of science content. Additionally, AI supports adaptive learning tailored to students' individual needs, allowing them to learn at their own pace. In conclusion, integrating AI into science education not only accelerates the learning process but also deepens students' scientific reasoning, equipping them with critical thinking skills necessary to tackle future challenges. The implications of this study suggest that AI can be an effective pedagogical tool in enriching students' learning experiences, and further exploration of its use in various educational contexts is essential.
Co-Authors Astuti, Theresia Widji Ellys Mey Sundari Muhammad Usman Musa Marsel Maipauw Ronny Mugara Yudistira