IoT-Based Early Fire Detection System for Wood Waste Area Using Long Short-Term Memory (LSTM)

Chandra, Vanya Nindya (2026) IoT-Based Early Fire Detection System for Wood Waste Area Using Long Short-Term Memory (LSTM). Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

This paper outlines how an IoT-based early fire detection system of wood waste warehouse was designed using Long Short-Term Memory (LSTM). The wood waste in warehouses is extremely flammable and fire could be easily ignited by a spark or friction and could be initiated also by the application of the static electricity or any lights of heat which could not be noticed. The conventional fire detection mechanisms are normally reactive and only notice fire when it is perceived to have smoke or hot temperatures and this could be too late in the case of a huge storage facility. In response to this, the proposed system will be used to monitor the environmental conditions at the time including temperature, humidity and gaseous concentration. Sensor information is also fed into the LSTM and it is then in a position to recognize any unusual patterns that can be utilized to signal an early fire danger. The system will automatically distribute the web push notifications to inform the users in time in case of the dangerous conditions detected. Based on IoT and LSTM, the prototype will improve detecting fire at its initial stages, reducing the response time, and reducing economic, environmental, and safety losses in wood waste warehouses. Keywords: IoT, LSTM, early fire detection, wood waste warehouse, web push notification

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Thesis (Undergraduate)

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