Combination of Fuzzy C-Means and Simple Additive Weighting Using Partition Coefficient Index

In the selection of palm oil tree seedlings, it is necessary to consider the determinants of alternatives and criteria to produce an optimal choice of seedlings corresponds to land suitability. At the moment, the selection process is still not using any determining factors. This study aims to show the best cluster ranks to solve the problem of seedling selection. This research was conducted with the Decision Support System of palm oil tree seedling selection by proposing the Fuzzy C-Means method and Simple Additive Weighting using Partition Coefficient Index. The Fuzzy C Means method is used for data grouping whereas the Partition Coefficient Index is used to find the best cluster selection and Simple Additive Weighting is used for ranking cluster data selection. Based on the results of the research, it is known that the method can be applied to the Decision Support System of palm oil tree seedling selection by producing more objective and precise decisions. The sensitivity test then proves that the proposed method, Fuzzy C- Means method and Simple Additive Weighting using Partition Coefficient Index, in term of consistency to the effect of changing criteria is better, affected only 1 time out of 6 tests, compared to the method of Fuzzy C Means and Simple Additive Weighting using Xie Beni Index that affected by changing criteria in 3 times out of 6 tests.