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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Yunita Dwi Alfiyanti
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Fungsi Senyawa Aktif Data Berdasarkan Kode Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Modified K-Nearest Neighbor Yunita Dwi Alfiyanti; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 4 (2019): April 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Compounds are single chemical substances from two or more chemical elements that form bonds and can be described. The compound is divided into active compounds and inactive compounds. Active compounds are chemical compounds that have pharmacology or usability. Compounds have an arrangement that is difficult to process on a computer, for which code is created that is easy to process using a computer. The code is a SMILES (Simplified Molecular Input Line Entry System) which is a code of modern chemical bonds that will be converted into a line to facilitate the classification process in the system. The special character of SMILES is obtained by doing preprocessing with the results of 11 features consisting of B, Br, C, Cl, F, I, N, O, P, S and OH atoms. These features are then used for the classification process using the Modified K-Nearest Neighbor method, where this algorithm is the development of the KNN method which consists of two processing, training data validation and weighting. The classification of the function of active compounds aims to facilitate the grouping of active compounds based on their pharmacology through the help of information technology and computer science degeneration, which so far in the medical field requires a long time in its determination because it uses laboratory tests. Tests that have been conducted using 260 data are divided into 2 categories of classes, namely the Neural class and the Heart class which consists of 90% (234 data) training data and 10% (26 data) test data. The test gets results in the form of an accuracy value of 73% with a k value of 3, whereas in the k-fold cross validation test the value of accuracy is obtained an average of 62.69%.
Co-Authors Anam, Syaiful Dian Eka Ratnawati