Vessel Classifying and Trajectory Based on Automatic Identification System Data
Teks penuh
Dokumen terkait
In this research, 75% (33 image for each class) of the data are used as training data and the remaining (25%, 12 image for each class) as testing data. The first stage of preprocess
pembagian data menjadi 2 yaitu data training dan data testing (3) menormalisasi data, (4) membangun model Feedforward neural network dengan algoritma Backpropagation ,
Metode Double Exponential Smoothing digunakan untuk melakukan peramalan dengan membagi data menjadi data training dan data testing sehingga dari model
Langkah-langkah dalam pembentukan model meliputi tahap identifikasi input, pembagian data menjadi data training dan testing, penentuan banyak neuron pada lapisan
a Applelogo model with one 3-segment group linking line segments and node angleα shown b A test image from the database c The gradient magnitude image d Example of Centroid boosting e
Accuracy training for CNN model construction process Traing Validation Testing Accuracy 98.2258% 98.5326% 97.8693% Mean absolute error 0.1199% When comparing over 182
However, the validation accuracy value has been achieved in the 8th epoch so the model used in the test using test data is the 8th epoch model with a loss value in the training data of
The training data is used to develop the ability of the model to classify different varieties of vegetables from the image data while it is validated using the validation portion.. The
