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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Journal of Innovation and Applied Technology Natural B Jurnal Berkala Epidemiologi Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Prosiding Seminar Nasional Teknoka Jurnal Irigasi Indonesian Journal of Electrical Engineering and Computer Science COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Anam, Syaiful
Brawijaya University
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Public Health Social Sciences

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Klasifikasi Fungsi Senyawa Aktif berdasarkan Notasi Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Random Forest Faiz Anggiananta Winantoro; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 4 (2021): April 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A compound is a single substance composed of two or more elements that form chemical bonds. There are two types of compounds, namely active compounds and inactive compounds. Active compounds are compounds that have physiological effects on other organisms. In Indonesia, there are still many active compounds whose function is unknown. Therefore, a classification method is needed to help determine the function of the active compound. Classification is done with data written in SMILES notation. From the SMILES notation, features such as the number of atoms B, C, N, O, P, S, F, Cl, Br, I, OH, =, #, @, -, +, COC, C = C, are taken. O-], N +, C = O, and () go through the preprocessing process. Before being used for the classification process, all these features are divided by the length of the SMILES notation to get their value. This research was conducted to classify the function of active compounds by applying the Random Forest (RF) method with the SMILES data object with 4 classes of compound functions. RF was chosen because this method has almost no overfitting conditions, is able to handle data with many features, and this method is not affected by datasets that have missing values. The best accuracy resulted in testing with 4 class data is 69% and the best average in testing with the K-Fold Cross Validation method is 63%. Then, on the data with 3 classes of compound functions, the best accuracy is 76% and the best average in testing with the K-Fold Cross Validation method is 70%. Finally, testing data with 2 classes of compound functions produces the highest accuracy of 86% and the best average of 80%.
Co-Authors A.S. Adhiwibawa, Marcelinus Abu Bakar Sambah Agus Suryanto Agus Widodo Ahmad Afif Supianto Candra Dewi Corina Karim, Ari Andari Dian Eka Ratnawati Dwi Agus Kuncoro Dwi Febry Indarwati Dwi Mifta Mahanani Faiz Anggiananta Winantoro Fery Widhiatmoko Helen Yuliana Angmalisang Hidayat, Noor Indah Yanti Isti Marlisa Fitriani Ivan C. Onggara Kestrilia Rega Prilianti Marcelinus A.S. Adhiwibawa Mochamad Hakim Akbar Assidiq Maulana Mochamad Hakim Akbar Assidiq Maulana Mochammad Iskandar Ardiyansyah Rochman Mohamad Handri Tuloli Musafir, Raqqasyi Rahmatullah Nur Khilmiyatul Ilmiyah Nyimas Ayu Widi Indriana Onggara, Ivan C. Onky Prasetyo Raden Rizky Widdie Tigusti Revi Anistia Masykuroh Sherly Witanto Sobri Abusini Suhhy Ramzini Suryanto, Agus Tatas H.P. Brotosudarmo Trisilowati, Trisilowati Wuryansari Muharini K. Yunita Dwi Alfiyanti ZURAIDAH FITRIAH Zuraidah Fitriah