Penerapan Metode K-Means-ACO Untuk Pengelompokan Biji Wijen Berdasarkan Sifat Warna Cangkang Biji - Pangestu Ari Wijaya; Rekyan Regasari Mardi Putri; Dian Eka Ratnawati (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Sesame is one kinds of the groceries that produce vegetable oil. Nowadays, the needs of sesame is increasing so it is necessary to pick a good quality in producing sesame. To conduct sesame plants crossing, the color of sesame seed shell is very infuential on its quality. Several previous studies used in this research has been done to cluster sesame seed with qualitative and quantitative method. The qualitative method in this research is conducted by field observation while the quantitative method is conducted by processing the sesame data from measurement result by using chromameter which resulted of an L*, a* and b* color. Several previous studies has successfully done the clustering by using qualitative method namely IWOKM, PSOKM and GAKM method. This study will categorize and compare the result of sesame data with same of data by using K-Means-ACO method with the previous method. From several journals, the method is proved that K-Means-ACO method has optimal results because in the analysis step combined the optimization and clustering algorithm method. Based on the test results of the K-Means-ACO method compared with the previous method, the good result of clustering sesame seed based on the color of the seed shell. It is proven by the grouping result is 233:58. After all, this research could be concluded that the K-Means-ACO method could be used as the alternative method to conduct the sesame seed classification based on its seed shell color.