Major Histocompatibility Complex molecules (MHCs) are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Predicting which peptide sequences will be presented by the MHC II molecule is therefore important for understanding the activation of T helper cells and can be used for the rational design of novel vaccines. In the present work, the Gibbs sampling approach with or without sequence clustering using different methods (Hobohm1, Hobohm2 or heuristic sequence weighting) is presented to predict MHC class II peptide sequences with a high binding affinity. Finally the results were displayed in terms of Pearsons Correlation Coefficient (PCC), a Receiver operating characteristic (ROC curves) and a Matthews Correlation Coefficient (MCC) metrics and visualized as sequence logos with potential motifs. It can be concluded that the PCC, MCC and AUC metrics are consistently poor for each allele between the applied methods, which is also observed in the study conducted by Nielsen \textit{et al.} in 2007. The Gibbs approach with heuristic sequence weighting has shown to find the most conserved motifs among the applied methods.