Implementasi Metode Artificial Bee Colony - Kmeans (ABCKM) Untuk Pengelompokan Biji Wijen Berdasarkan Sifat Warna Cangkang Biji - Enny Trisnawati; Rekyan Regasari; Sutrisno Sutrisno (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Sesame is one of the vegetable oil producers which consumption level in the world is expected to continue to increase, along with the many benefits and uses. The selling price of sesame is determined by the quality of the sesame. The indicator that can be used as a hint of the quality of sesame is the color on the seed shell. One of the efforts to produce the best quality sesame is by crossbreeding between cultivars that produce the color of the sesame seeds that vary, so it needs to be grouped by the closeness in color. Several ways that previous researchers have done to classify sesame seeds such as qualitative and quantitative methods. Currently, there are 3 models of quantitative methods for the sesame seeds grouping which are IWOKM method, PSO-K-Means and GA-KMeans which the result of data grouping is quite good. ABCKM method that were used in this research which is the combination of KMeans method (KM) and Artificial Bee Colony (ABC. The performance of ABCKM will then be compared with KM, IWOKM, PSO-K-Means and GA-KMeans methods.Based on the result of comparison test of method, ABCKM method proved better than KM method and the previous method: IWOKM, GA-KMEANS and PSO-K-Means in grouping the sesame data. This result proved by the average value of fitness and silhoutte coefficent when using ABCKM method better than KM, IWOKM, GA-KMEANS and PSO-K-Means. The result of the ABCKM method grouping is the same as the previous method C1: C2 = 233: 58, so method in this study can be used as an alternative method for sesame seed grouping based on color of seed shell.