Sistem Monitoring Deteksi Dini Kebakaran Hutan Berbasis Multiplatform Di Kabupaten Siak Menggunakan SDLC Prototyping

Authors

DOI:

https://doi.org/10.30865/json.v3i4.4136

Keywords:

Forest Fires, Peatlands, SDLC Prototyping, Branch Sensor, Parent Sensor, Multiplatform.

Abstract

Forest fires can occur by natural or human factors. The Siak district is one of the areas in Riau Province which has a forest with peat soil, so it has high fertility. However, in recent years, many abandoned peatlands have been dry due to the behavior of locals and extreme weather, which has caused the peatlands prone to fire. This research collaborates with BPBD Siak as a user to deliver an early detection system for forest fires. The system is developed by using the SDLC prototyping model, which emphasizes the active role of the user to be involved. The source of peatland information is from branch sensors and parent sensors which periodically send information regarding the status of monitored land to the cloud server for storage purposes. The cloud server is a source of information for the three platforms that can be leveraged for users to monitor the status of peatlands both in real-time and previous conditions, namely web applications, mobile applications, and LED boards. Based on the performed tests, all components of the system worked well and the three platforms were successful in displaying information on the real-time condition of the monitored peatlands.

Author Biographies

Diki Arisandi, Universitas Abdurrab, Pekanbaru

Department of Infromatics Engineering

Liza Trisnawati, Universitas Abdurrab, Pekanbaru

Department of Infromatics Engineering

Amir Syamsuadi, Universitas Abdurrab, Pekanbaru

Department of Psychology, Social and Political Sciences

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Published

2022-06-30

How to Cite

Arisandi, D., Trisnawati, L., & Syamsuadi, A. (2022). Sistem Monitoring Deteksi Dini Kebakaran Hutan Berbasis Multiplatform Di Kabupaten Siak Menggunakan SDLC Prototyping. Jurnal Sistem Komputer Dan Informatika (JSON), 3(4), 410–416. https://doi.org/10.30865/json.v3i4.4136

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