Klasifikasi Senyawa Kimia dengan Notasi Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Extreme Learning Machine (ELM) - Isti Marlisa Fitriani; Dian Eka Ratnawati; Syaiful Anam (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Indonesia has a huge natural's potential by the existence of various plants and animals discovery. This issue brings a good for Indonesian people through taking advantage of nature, especially in pharmacology. In pharmacology, active compounds can be used to prevent and cure disease. Therefore, a research is conducted in informatics's field by making an active compounds' classification system to determine its pharmacological benefits. SMILES is a chemical compound notation used in this research. SMILES's features which are used as many as 15, namely B, C, N, O, P, S, F, Cl, Br, I, OH, @, =, #, and (). ELM is an ANN method that can do a generalization better than conventional methods in a limited time. A number of hidden neurons test which were conducted using k-fold cross validation method in 2 classes produced the best accuracy, 85,03%, in Metabolism and Inflammation class scenario with a total of 5, 10, and 15 hidden neurons. A number of hidden neurons' test use k-fold cross validation method which were conducted in 3 classes produced the best accuracy, 55,06%, in Metabolism, Inflammation, and Cancer class scenario with a total of 300 hidden neurons. The best accuracy was obtained as many as 55,06% by testing 15 features with 300 hidden neurons, while in 11 features's test with 400 hidden neurons was found a number of 49,18% as the best accuracy.