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All Journal Prosiding Seminar Nasional CORISINDO
Tabroni, Rizal
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Computer Science & IT

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Desain Arsitektur Jaringan IoT Terintegrasi pada Smart Campus Universitas Bumigora Menggunakan VLAN untuk Ruang Kelas Cerdas Zul Fahmi, Muhammad Farhan; Tabroni, Rizal; Muhammad, Fhadilla; Husain
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5335

Abstract

The application of Internet of Things (IoT) technology in educational environments is expanding rapidly in response to the increasing need for energy efficiency, classroom security, and intelligent infrastructure management. This study designs a Virtual Local Area Network (VLAN)-based network architecture to integrate IoT devices such as smart lighting, automatic air conditioning, temperature sensors, access control, CCTV, and other networked equipment within smart classrooms at Universitas Bumigora, West Nusa Tenggara. The novelty of this research lies in its technical design approach, which includes per-classroom device inventory, functional VLAN segmentation, bandwidth estimation, and simulated topologies using Cisco Packet Tracer. The results demonstrate that VLAN segmentation enables structured and secure management of IoT networks, providing a practical foundation for building adaptive, modular, and scalable smart campus environments in higher education.
Rancang Bangun Sistem Peringatan Dini Banjir Kota Mataram Berbasis Sensor Iot Dan Artificial Neural Network (ANN) Tabroni, Rizal; Fhadilla, Muhammad; Priyanto, Dadang
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5355

Abstract

Banjir merupakan salah satu bencana alam yang sering terjadi di Kota Mataram akibat tingginya curah hujan, buruknya drainase, dan luapan air sungai. Untuk meminimalisir dampaknya, diperlukan sistem peringatan dini berbasis teknologi yang mampu memprediksi terjadinya banjir secara akurat dan cepat. Penelitian ini merancang dan membangun sistem peringatan dini banjir menggunakan kombinasi sensor IoT untuk pemantauan ketinggian air dan curah hujan, serta Artificial Neural Network (ANN) untuk memprediksi potensi banjir berdasarkan data historis dan real-time. Dataset curah hujan diperoleh dari BMKG NTB selama lima tahun terakhir (2019–2023), dengan penekanan pada dua kejadian banjir besar yang terjadi di tahun 2021 dan 2025. Sistem ini diimplementasikan menggunakan mikrokontroler ESP32 dan platform Firebase sebagai penyimpanan data, sedangkan ANN dilatih dengan pendekatan supervised learning dan dievaluasi menggunakan confusion matrix serta akurasi prediksi.
Co-Authors Fhadilla Muhammad, Fhadilla Fhadilla, Muhammad Husain Zul Fahmi, Muhammad Farhan