Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Revi Anistia Masykuroh
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 
Klasifikasi Fungsi Senyawa Aktif Berdasarkan Notasi Simplified Molecular Input Line Entry System (SMILES) Dengan Metode K-Means Naive Bayes (KMNB) Revi Anistia Masykuroh; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1067.697 KB)

Abstract

Indonesia is a tropical country that has the most biodiversity in the world. Almost all of the plants part like leaf, root, stem, fruit, flowers, seeds, and rhizome can be used for human health. In Indonesia the utilization of plants as medicine is so limited. Therefore, further research and continuous plant drugs or herbal remedies is really needed as well as the technologies are able to maximize the utilization. In 1980, David Weininger found a chemical notation for processing informations that related to a modern chemistry named Simplified Molecular Input Line System (SMILES) and that notation is specifically for computer used. On this research, K-Means Naive Bayes methods are used for the classification of the functions of the active compounds because this methods are able to grouping data according to their similarity and the classification process is much easier to understand. Based on the test results, the K-Means Naive Bayes are abled to give an accuracy system 85.45% with a 80% training data ratio and 20% testing data. The system also being tested using K-Fold Cross Validation with K-Fold as many as 10, the highest accuracy that can be given is 86.66% on 9th fold and the lowest is 70.37% on 1st fold. While the average of accuracy using the K-Fold Cross Validation is 82.6%.
Co-Authors Anam, Syaiful Dian Eka Ratnawati