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All Journal E-Jurnal Matematika The Journal of Experimental Life Sciences (JELS) 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 Biochemistry, Genetics & Molecular Biology 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 Mathematics Medicine & Pharmacology Neuroscience Public Health Social Sciences

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Klasifikasi Fungsi Senyawa Aktif berdasarkan Data Simplified Molecular Input Line Entry System (SMILES) menggunakan Metode Support Vector Machine (SVM) Dwi Febry Indarwati; 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

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Abstract

Chemical compounds can be distinguished into active compounds or commonly called bioactive compounds and inactive compounds or commonly called passive compounds. At this time there are still many active compunds that the pharmacological role does not known yet, so the system being made for classify the functions of active compounds that expected to support chemists research in the laboratory. To simplify the process of making the system, the representation of molecular structure must be easily processed by a computer so that the SMILES notation will be used, the SMILES notation describes chemical formula in a row notation. This system is using the SVM (Support Vector Machine) method because the SVM method has high generalization capabilities without requiring additional datasets. In this research uses as many as 15 features and objects as many as 3 classes of active compound functions, including metabolism, infection, and anti-inflammation. The best test result is 83.33% when using the Gaussian kernel RBF, using a lambda value (λ) of 5, the complexity value is 0.1, the sigma value (σ) is 0.5, and with the number of iterations is 5.
Implementasi Metode Adaptive Moving Self- Organizing Maps Untuk Mengelompokkan Pelajar Berdasarkan Aktivitas Belajar Pada Media Pembelajaran Interaktif Onky Prasetyo; Ahmad Afif Supianto; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Artikel dipublikasikan di Jurnal Nasional Terakreditasi, JUITA: Jurnal Informatika
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 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 Ririn Nirmalasari Sherly Witanto Sobri Abusini Suhhy Ramzini Suryanto, Agus Tatas H.P. Brotosudarmo Trisilowati, Trisilowati Wuryansari Muharini K. Yunita Dwi Alfiyanti Zuraidah Fitriah ZURAIDAH FITRIAH