<![CDATA[The identification method can be done by observing a physical system that will provide input and output signals. The input and output signals generated by the system will be calculated with the identification method's algorithm to produce filter parameters that can represent the system. The algorithm used in this thesis is Recursive Least Square (RLS). The components contained in the temperature and humidity plant system, namely the Digital Temperature and Humidity sensor (DHT22), a 25 Watt incandescent lamp, and a 24 Volt mist maker. The structure of the model used in this thesis is the Auto Regressive Moving Average Exogenous (ARMAX). The order used in this thesis is second-order according to the residual analysis, indicating that the second-order graph is between the dotted line and close to the zero axis point. The parameter results obtained at the plant temperature are a1 = -1.331; a2 = - 0.3402; b1 = 0.1775; b2 = –0,1682; c1 = –0.5658; c2 = –0,2031. In plant humidity is a1 = -0.9459; a2 = -0.043202; b1 = 0.008937; b2 = 0.002168; c1 = 0.05638; c2 = 0.03856. Akaike's FPE validation results on the temperature and humidity plant model are 0.009251 and 0.1663, respectively. The accuracy test results with the setpoint signal at the plant temperature and humidity were FIT = 95.43%, respectively; FIT = 93.86%.Keywords: Temperature and Humidity Plant, System Identification, ARMAX, RLS]]>
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