[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125049-id":3,"doc-seo-125049-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125049,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",54,"Penelitian & Laporan","Investigasi Efisiensi Penghambatan Korosi Senyawa Quinoxaline Berbasis Machine Learning - Sebuah Studi","Korosi memberikan dampak merugikan yang signifikan pada sektor industri maupun akademik melalui konsekuensi ekonomi, lingkungan, sosial, keamanan, dan keselamatan. Penelitian ini berfokus pada potensi quinoxaline sebagai inhibitor korosi yang relatif tidak beracun, mudah diproduksi, serta efektif pada berbagai kondisi korosif. Eksplorasi kandidat melalui eksperimen bersifat intensif waktu dan sumber daya. Dengan pendekatan machine learning berbasis model quantitative structure-property relationship (QSPR), berbagai algoritma linier dan non-linier dievaluasi untuk memprediksi nilai corrosion inhibition efficiency (CIE). Hasil menunjukkan Gradient Boosting Regressor (GBR) non-linier unggul dengan metrik RMSE, MSE, MAD, MAPE, dan R2.","Investigasi Efisiensi Penghambatan Korosi Senyawa Quinoxaline Berbasis Machine Learning  \nA Study on the Corrosion Inhibition Efficiency of Quinoxaline Compounds Utilizing Machine  \nLearning  \nVicenzo Frendyatha Adiprasetyaa, Muhamad Akroma,b,*, Gustina Alfa Trisnapradikaa,b  \naProgram Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang, Indonesia bResearch Center for Materials Informatics, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang, Indonesia  \nArtikel histori :  \nDiterima 27 Juni 2023  \nDiterima dalam revisi 1 Februari 2024 Diterima 21 Februari 2024  \nOnline 22 Maret 2024  \nABSTRAK: Korosi memberikan kekhawatiran serius bagi sektor industri dan akademik karenamempunyai dampak negatif yang signifikan terhadap sejumlah bidang, termasuk perekonomian, lingkungan, masyarakat, industri, keamanan, dan keselamatan. Saat ini, banyak peminat topik pengendalian kerusakan bahan berbasis molekul organik. Quinoxaline mempunyai potensisebagai inhibitor korosi karena tidak beracun, mudah diproduksi, dan efektif dalam berbagaikondisi korosif. Mengeksplorasi kemungkinan kandidat penghambat korosi melalui penelitian eksperimental adalah proses yang memakan waktu dan sumber daya yang intensif. Dengan menggunakan pendekatan machine learning (ML) berdasarkan model quantitative structureproperty relationship (QSPR), kami mengevaluasi beragam algoritma linier dan non-linier sebagai model prediktif nilai corrosion inhibition efficiency (CIE) dalam penelitian ini. Kami menemukan bahwa, untuk kumpulan data senyawa quinoxaline, model non-linier Gradient Boosting Regressor (GBR) mengungguli keseluruhan model linier dannon-linier, serta hasil dari literatur dalam hal kinerja prediksi berdasarkan metrik root mean squared error (RMSE), mean squared error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE) dan coefficient of determination (R2) . Secara keseluruhan, penelitian kami memberikan sudut pandang baru tentang kapasitas model ML untuk memperkirakan kemampuan penghambatankorosi pada permukaan besi oleh senyawa organik quinoxaline.  \nKata Kunci: korosi; inhibitor; machine learning; quinoxaline.  \nABSTRACT: Corrosion represents a formidable challenge for both industrial and academic sectors due to its substantial adverse implications across various domains, encompassing the economy, environment, society, industry, security, and safety. The contemporary focus on material damage control, specifically of organic molecules, underscores the potential utility of quinoxaline as a corrosion inhibitor. This is attributed to its non-toxic nature, facile production, and efficacy under diverse corrosive conditions. Exploring corrosion inhibitor candidates through empirical research constitutes a time-intensive and resource-demanding endeavor. In this study, we employed a machine learning (ML) paradigm founded on the quantitative structure-property relationship (QSPR) model to assess numerous linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values. Our investigation revealed that, within the dataset on the quinoxaline compound, the non-linear Gradient Boosting Regressor (GBR) model exhibited superior performance compared to both linear and non-linear models, as well as results documented in existing literature. This superiority was quantitatively measured using the root mean squared error (RMSE) metric, mean squared error (MSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and coefficient of determination (R2) . In summary, our research contributes a novel perspective on the efficacy of ML models in estimating the corrosion inhibition capacity of iron surfaces facilitated by the organic compound quinoxaline.  \nKeywords: corrosion; inhibitor; machine learning; quinoxaline.  \n1. Pendahuluan  \nKorosi dapat dianggap sebagai kebalikan dari proses  \nekstraksi (pemurnian) logam. Logam yang ditemukan secara alami umumnya ber","cbCairgMedSbEP7l","https://ap.wps.com/l/cbCairgMedSbEP7l","pdf",296191,2,1,5,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang korosi dan dampaknya\n## Strategi pengendalian korosi dan peran inhibitor","[{\"question\":\"Mengapa quinoxaline dipertimbangkan sebagai inhibitor korosi?\",\"answer\":\"Quinoxaline berpotensi sebagai inhibitor karena bersifat tidak beracun, mudah diproduksi, dan efektif pada beragam kondisi korosif.\"},{\"question\":\"Metode apa yang digunakan untuk memprediksi corrosion inhibition efficiency (CIE)?\",\"answer\":\"Penelitian memakai pendekatan machine learning berbasis model quantitative structure-property relationship (QSPR) untuk membandingkan algoritma linier dan non-linier.\"},{\"question\":\"Algoritma machine learning apa yang menunjukkan performa terbaik, dan bagaimana diukur?\",\"answer\":\"Gradient Boosting Regressor (GBR) non-linier mengungguli model lain, dinilai menggunakan metrik RMSE, MSE, MAD, MAPE, dan koefisien determinasi R2.\"}]","Investigasi Efisiensi Penghambatan Korosi Senyawa Quinoxaline Berbasis Machine Learning - Sebuah Studi | PDF",1785896351,8,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"investigation-of-corrosion-inhibition-efficiency-of-quinoxaline-compounds-based-on-machine-learning-a-study","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/id/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/investigation-of-corrosion-inhibition-efficiency-of-quinoxaline-compounds-based-on-machine-learning-a-study/125049/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Mengapa quinoxaline dipertimbangkan sebagai inhibitor korosi?","Question",{"text":76,"@type":77},"Quinoxaline berpotensi sebagai inhibitor karena bersifat tidak beracun, mudah diproduksi, dan efektif pada beragam kondisi korosif.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Metode apa yang digunakan untuk memprediksi corrosion inhibition efficiency (CIE)?",{"text":81,"@type":77},"Penelitian memakai pendekatan machine learning berbasis model quantitative structure-property relationship (QSPR) untuk membandingkan algoritma linier dan non-linier.",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritma machine learning apa yang menunjukkan performa terbaik, dan bagaimana diukur?",{"text":85,"@type":77},"Gradient Boosting Regressor (GBR) non-linier mengungguli model lain, dinilai menggunakan metrik RMSE, MSE, MAD, MAPE, dan koefisien determinasi R2.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]