Image Gradient - Flipping Effect of the Convolution Matrix (Kernel) for Edge Detection
This is a common question and there are a few other answers on this site which may be helpful. What you have noticed is correct. To be clear, convolution is defined by this flipping, it gives the convolution operation some nice maths properties. Non-flipping convolution is called cross-correlation.

For these kernels, the only thing flipping changes is the sign of the result. You can safely use the inverse (flip) of the kernels to get the form you desire.