You can use the Evaluate class to perform this 10-fold cross-validation. To define the cross-validation you have to set the parameter as '-x 10' in the EvaluateModel.
clear all; close all; clc;
%% Add jar file to path plus import dependencies
javaaddpath('/usr/local/weka-3-6-11/weka.jar');
import weka.classifiers.trees.RandomForest.*;
import weka.classifiers.meta.Bagging.*;
import weka.classifiers.Evaluation.*;
import weka.core.Instances.*
%% load the arff file and extract the informations
filename = 'algo_output/results_features_labeling2_2class.arff';
loader = weka.core.converters.ArffLoader();
loader.setFile(java.io.File(filename));
data = loader.getDataSet();
data.setClassIndex(D.numAttributes()-1);
%% classification
classifier = weka.classifiers.functions.MultilayerPerceptron();
classifier.buildClassifier(data);
classifier.toString()
%% 10-fold cross-validation
ev = weka.classifiers.Evaluation(data);
v(1) = java.lang.String('-t');
v(2) = java.lang.String(filename);
v(3) = java.lang.String('-x');
v(4) = java.lang.String('10');
v(5) = java.lang.String('-i');
prm = cat(1,v(1:end));
ev.evaluateModel(classifier, prm)
For more information, check this link.