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All Journal Jurnal Informatika JURNAL NASIONAL TEKNIK ELEKTRO IJCIT (Indonesian Journal on Computer and Information Technology) INTEGER: Journal of Information Technology Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Tekno Kompak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) International Journal of Recent Technology and Applied Science (IJORTAS) Humanism : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Indonesia eProceedings of Engineering Jurnal Informatika: Jurnal Pengembangan IT
Bernadus Anggo Seno AjiAji
Institut Teknologi Telkom Surabaya
Religion Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Public Health Social Sciences

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Journal : Jurnal Informatika

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Classification of IGF1R ligand compounds for Identification of herbal extracts using extreme gradient boosting Mohammad Hamim Zajuli Al Faroby; Siti Amiroch; Bernadus Anggo Seno Aji; Avriono Aritonang
Jurnal Informatika Vol 16, No 3 (2022): September 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v16i3.a23286

Abstract

Diabetes Mellitus is a serious disease that requires serious treatment. The cause of this disease is due to malfunctions in insulin and insulin-producing organs. One of the proteins that become insulin signaling receptors is IGF1R, which has an important role in activating and maximizing insulin performance. In this study, we aimed to obtain herbal compounds that can activate the function of the IGF1R protein by utilizing compound data in an open database and modeling it using the ensemble method, namely extreme gradient boosting. We found that this method produces the best classification model than with other algorithms. We predicted 844 data for herbal compounds, but only 15 data met the threshold of 0.6. We got one plant from the fifteen herbal compounds, namely Zostera Marine, which was confirmed to have compounds that bind to IGF1R. These compounds have the highest probability value in the classification model that we formed compared to others.
Co-Authors Anggraini, Sukma Dewi Anugrayani Bustamin Arliyanti Nurdin Avriono Aritonang Ayu Ainun A'ziziyyah Ayu Ainun A'ziziyyah Berliana Amelia Daniel Happy Putra Farah Zakiyah Rahmanti Faroby, Mohammad Hamim Zajuli Al Helmy Widyantara Ignatia Indreswari Ignatia Indreswari Nugroho Juniyanto, Muhammad Ma'sum Khodijah Amiroh Mohammad Hamim Zajuli Al Faroby Muhammad Adib Kamali Muhammad Iqbal Maulana Muhammad Rafi Irzam Muhammad Rizal Dwi Pramana Oktavia Ayu Permata Pangestu Widodo, Pangestu Philip Tobianto Daely Rasyid Sabillillah Rifdatun Ni’mah Rose, Clarenza Dixie Siti Amiroch, Siti Wahyu Listyanto, Ferdhyan Yohanes Setiawan Zaenal Abidin