Implementasi Gabungan Metode K-Means Learning Vector Quantization (LVQ) Untuk Klasifikasi Fungsi Senyawa Aktif Menggunakan Data SMILES - Nur Khilmiyatul Ilmiyah; Dian Eka Ratnawati; Syaiful Anam (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
The active compound is a chemical compound that has many functions. While the inactive compound, doesn't have much function only as additional substances. Active compounds can be divided into two therapeutic functions as alternative medicine, and Pharmacology function to control drug containing the active compounds in it. In order to get functions in the active compounds used notation SMILES. SMILES notation is a representation of the active compounds with modern chemical notation, so that the computer can read the elements of the compound. Of the many SMILES notations at this time, all the SMILES notations cannot be used as medicine because they are still in the testing phase. SMILES notation that has been tested could be used as medicine. Therefore, this research will be built a fixed classification model that takes into account all the data. Based on the test results, the K-Means method of combined Learning Vector Quantization (LVQ) generate value accuracy of 72.22%, K-means conventional 52.65%, while Learning Vector Quantization (LVQ) owns 67.96%. The results show that the combined K-Means method of Learning Vector Quantization (LVQ) have better results than conventional K-means and Learning Vector Quantization (LVQ).