PENERAPAN ALGORITMA NAÏVE BAYES UNTUK KLASIFIKASI BAKTERI GRAM-NEGATIF - Evy Priyanti (JURNAL TEKNIK KOMPUTER AMIK BSI, 2017)
The classification depends on the variety of bacteria that exist. The important feature to identify an organism of bacterial phenotype is the scheme that utilizes the morphology and staining properties of the bacteria itself, to classify the phenotype scheme is used Naïve Bayes algorithm that has proven to have a high degree of accuracy and high rate of speed when applied into E.coli dataset in E. coli dataset consisting of seven features are: mcg, gvh, lips, chg, aac, alm1, alm2, and proteins are classified into 8 classes: cytoplasmic (cp), an inner membrane without the signal sequence (im), perisplasm (pp), in the membrane with uncleavable signal sequence (IMU), outer membrane (oM), outer membrane lipoprotein (OML), the membrane lipoprotein (IML), an inner membrane with cleavable signal sequence (IMS) with an accuracy of 80.93%, with Naïve Bayes algorithm so it can be ascertained that the classification of gram-negative bacteria with E. coli phenotype datasets prove to be accurate.