Dried sea cucumber (Beche-de-mer), the product after cleaning, boiling, salting, and drying, is as delicious and healthy food. Dried sea cucumber (Beche-de-mer) also has a high market price and the highest nutritional value of all seafood products. Moisture content in dried sea cucumber (Beche-de-mer) can affect the international market prices of dried sea cucumber to decline. This condition takes place because the moisture content is one of the parameters that determine the quality of dried sea cucumber. Therefore, this research will discuss a prediction system for measuring moisture content in dried sea cucumber (Beche-de-mer) using hyperspectral imaging technique. This system uses reflectance mode with the wavelength from 400 to 1000 nm. The hardware from the prediction system for measuring moisture content is motors to generate, hyperspectral camera system, two 150 W halogen lamps, Teflon table, and personal computer link. Then, the PLSR algorithm is applied to the prediction system model at full wavelength. The prediction model is used to obtain the predicted value of moisture content. Then the results of the prediction model are compared with the data references obtained by the gravimetric method. The root means square errors and correlation coefficient are used to evaluate the prediction system performance of moisture content prediction. The best result of the prediction system in this work is to have a correlation coefficient of 0.99 and root mean square errors of 0.92% respectively, with the number of PLS component is 30. Based on the results of this research, the proposed system can be used as an alternative method of measuring the moisture content in dried sea cucumber (Beche-de-mer) with excellent accuracy and high reliability.