Is it required to shuffle the training data for SVM multi-classification?

Actually I am using OpenCV's svm python interface and I am trying to classify data into 4 categories. When the labels and training data are in order, I mean for example the data were in 4 groups ordered as label 1, label 2, label 3 and label 4, the correct ratio was low, about only 50% right. But when I shuffled the training data, the result was reasonable, about 90% correct. So my question is: does the training data order affect the final result, or do I need to shuffle the data before training?