Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Sherly Witanto
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Pengelompokan Fungsi Aktif Senyawa Data SMILES (Simplified Molecular Input Line Entry System) Menggunakan Metode K-Means Dengan Inisialisasi Pusat Klaster Menggunakan Metode Heuristic O(N LogN) Sherly Witanto; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (932.274 KB)

Abstract

Active compounds have function as a medicine that can prevent or cure diseases. Some of the active compounds have been known the function and some are still in the research stage. Currently in Indonesia there is still no program that capable to classifying chemical compounds as drugs for certain diseases. SMILES notation is the conversion of chemical compounds in the form of line notation. Notation SMILES able to provide convenience to the process of computerization on the classification of chemical compounds. The classification of the SMILES notation is carried out by taking the values ​​of the B, S, N, O, I, F, C, P, Cl, Br and OH atoms present in the compound. Before being processed, to get the value of the feature is done by dividing the process of each atom with the length of the compound. K-Means algorithm is the most widely used clustering method because it is easy and simple. The grouping of active function using K-Means method has weakness in random cluster initialization process, so that heuristic method o (n logn) is used to get the cluster initials with better value. Based on the software that has been made, the test is done using 512 of training data and test data as much as 128. Accuracy obtained from the test that is equal to 63% and testing using ¬K-Fold Cross Validation with 10 times the test produces an average accuracy of 52,58 %. Testing using K-Means with heuristic o (n logn) yielded better accuracy compared to conventional K-Means.
Co-Authors Anam, Syaiful Dian Eka Ratnawati