Judul: Sistem real time berbasis jaringan syaraf tiruan untuk prediksi masa kadaluarsa biskuit dengan sensor dielektrik
Abstrak: Biscuit is type of dried food which found frequently on expired condition in market, therefore prediction method should be implemented in avoidance of this condition. Recently, prediction of shelf-life of biscuit has been done by laboratory test, but this test has some disadvantages, i.e.: time-consuming, expensive, also required trained panelists, complex equipment and suitable ambience. One alternative method is the dielectric sensor integrated with Artificial Neural Networks (ANN) in an intelligent real-time system. The aim of this research is to select the measurement attribute dielectric (dielectric parameters, the condition of the circuit and frequency), to formulate design of sensors can measure the value of selected parameters, to design an ANN model, and to integrate sensor and ANN dielectric to predict the shelf-life of biscuits in real time. Selection dielectric parameters and conditions of the circuit (CC/Constant current or CV/Constant Voltage) performed with Linear Regression, Feature Selection (Relief) and Artificial Neural Networks. Dielectric parameters for determining the best shelf life of biscuits are parallel capacitance (Cp). Cp constantly in the top five at three selection method, especially in ANN. Whereas the best condition of the circuit's for determining the shelf life of biscuits are CC, because always at the first sequence of three methods of selection. Selection of the frequency range 42 Hz - 5 MHz done graphically. Frequency range is able to differentiate a good shelf-life is a biscuit with a low frequency range from 42 to 351.65 Hz and the high frequency of 4721.13 to 50054.56 Hz. Selection results of the previous stage for dielectric parameter, the condition of the circuit and frequency is used for sensor design formulation basic. Approach the design tools in accordance with the results of the selection is a RC circuit. Capacitance (C) be the parameters of the sensor dielectric designed. The frequency of this sensor set 5-6 kHz, because the results are more sensitive reading capacitance values of different shelf-life and type biscuits. Designing neural network model is the next stage. This model is designed by using a data input capacitance (C), frequency (f) and Dielectric Constanta (k) from the sensor readings. Moreover, the input data on ANN is a type of packaging, type of biscuit and actual shelf-life (in packaging). ANN learning algorithm is Back propagation. For training process, trial and error is processed in the activation function, learning function, amount of nodes per hidden layer variation, and number of hidden layer. The best training architecture (lowest MSE and highest R) is ANN with the 5 hidden layer, 10 nodes per hidden layer, the hidden layer activation function tansig, the output layer activation function purelin, learning function trainlm and 86 epoch. ANN combined with dielectric parameters has lowest MSE (9,8648 x 10-5) and R highest (99.80%) in the training performance. ANN models are integrated with dielectric sensor using microcontroller AVR ATMega 8535. This integration to measure in real time the dielectric value (variable input) of an unknown product shelf life and readable on computer. To facilitate interaction with the user, an interface is built with MATLAB GUI toolbox. So, integration of ANN and capacitance
Keyword: Artificial Neural Network, biscuit, dielectric sensor, intelligent real time system, shelf-life