[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123227-id":3,"doc-seo-123227-113":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},123227,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",54,"Penelitian & Laporan","Pemodelan Prediksi Umpan Pulverized Coal pada Sistem Rotary Kiln Pabrik Semen - Berdasarkan Parameter Operasi & Kimia Berbasis Machine Learning","Penelitian ini bertujuan mengembangkan model prediksi umpan pulverized coal (PC) pada sistem rotary kiln di pabrik semen menggunakan algoritma machine learning untuk membantu operator Control Center Room (CCR) mengoptimalkan konsumsi PC, menekan biaya energi, dan meningkatkan efisiensi produksi. Model dibangun dengan memanfaatkan data parameter operasi serta sifat kimia bahan baku dan produk untuk menjawab kebutuhan pemilihan algoritma paling akurat dan penentuan faktor dominan. Hasil menunjukkan Gradient Boosting paling unggul dengan R-squared 0.976, mampu memprediksi nilai umpan lebih rendah dibanding pengaturan manual, sehingga berpotensi menghemat batubara dan menurunkan emisi CO2.","Syntax Literate: Jurnal Ilmiah Indonesia p–ISSN: 2541-0849  \ne-ISSN: 2548-1398  \nVol. 10, No. 2, Februari 2025   \nPEMODELAN PREDIKSI UMPAN PULVERIZED COAL PADA SISTEM ROTARY KILN PABRIK SEMEN BERDASARKAN PARAMETER OPERASI & KIMIA BERBASIS MACHINE LEARNING  \nRiduwan Maliki  \nSekolah Interdisiplin Manajemen dan Teknologi, Indonesia  \nEmail: [riduwan.maliki@outlook.com](riduwan.maliki@outlook.com)  \nAbstrak  \nPenelitian ini bertujuan untuk mengembangkan model prediksi umpan pulverized coal (PC) pada sistem rotary kiln di pabrik semen menggunakan algoritma machine learning. Model ini diharapkan dapat membantu operator Control Center Room (CCR) dalam mengoptimalkan konsumsi PC dan meningkatkan efisiensi produksi semen. Industri semen di Indonesia mengalami persaingan ketat dan tuntutan efisiensi operasional akibat kenaikan biaya energi. Konsumsi PC merupakan salah satu komponen biaya terbesar dalam produksi semen, dengan potensi optimasi yang signifikan. Penelitian ini berfokus pada tiga pertanyaanutama, yaitu 1.) membangun model prediksi umpan PC berbasis machine learning pada rotary kiln dengan memanfaatkan data parameter operasi, dan sifat kimia bahan baku dan produk; 2.) menentukan algoritma machine learning yang paling akurat dalam memprediksi nilai umpan PC pada rotary kiln; 3.) menentukan faktor apa yang paling signifikan dalam mempengaruhi nilai umpan PC pada rotary kiln. Penelitian ini diharapkan menghasilkan model prediksi PC yang akurat dan handal, serta mengidentifikasi faktor-faktor kunci yang mempengaruhi konsumsi PC. Hasil penelitian menunjukkan bahwa algoritma Gradient Boosting memberikan akurasi terbaik dengan nilai R-squared sebesar 0.976. Model yang dikembangkan mampu memprediksi nilai umpan yang lebih rendah dibandingkan dengan pengaturan manual oleh operator. Implikasi dari hasil ini adalah potensi penghematan penggunaan batubara dan pengurangan emisi CO2 dalam proses produksi semen.  \nKata kunci: rotary kiln, cement, coal, energy, machine learning, Support Vector Machine (SVM), Gradient Boosting, dan Neural Network  \nAbstract  \nThis research aims to develop a prediction model for pulverized coal (PC) feed in the rotary kiln system at a cement plant using machine learning algorithms. This model is expected to assist Control Center Room (CCR) operators in optimizing PC consumption and improving cement production efficiency. The cement industry in Indonesia is experiencing intense competition and demands for operational efficiency due to rising energy costs. PC consumption is one of the largest cost components in cement production, with significant optimization potential. This research focuses on three main questions, namely 1.) building a machine learning-based PC feed prediction model in rotary kilns by utilizing data on operating parameters, and chemical properties of materials and products; 2.) determining the most accurate machine learning algorithm in predicting PC feed values in rotary kilns; 3.) determining what factors are most significant in influencing PC feed values in rotary kilns. This research is expected to produce an accurate and reliable PC prediction model, as well as identify key factors that affect PC consumption. The results indicate that the Gradient Boosting algorithm achieved the highest accuracy with an R-squared value of 0.976. The developed model can predict lower feed values compared to manual operator  \n1356 Syntax Literate, Vol. 10, No. 2, Februari 2025  \nPemodelan Prediksi Umpan Pulverized Coal pada Sistem Rotary Kiln Pabrik Semen Berdasarkan Parameter Operasi & Kimia Berbasis Machine Learning  \nadjustments. This implies potential savings in coal consumption and reduced CO2 emissions in the cement production process.  \nKeywords: rotary kiln, cement, coal, energy, machine learning, Support Vector Machine (SVM), Gradient Boosting, dan Neural Network  \nPendahuluan  \nIndustri semen di Indonesia dalam kurun waktu 5 tahun terakhir (2019-2023) mengalami persaingan yang ketat dengan bebera","cbCaigzE82FYSlUR","https://ap.wps.com/l/cbCaigzE82FYSlUR","pdf",3091225,3,1,19,"Indonesian","id",113,"# Pendahuluan\n## Persaingan industri semen dan tekanan efisiensi\n## Komposisi biaya energi dan peran pulverized coal\n## Fungsi rotary kiln dalam pembentukan clinker","[{\"question\":\"Penelitian ini mengembangkan model prediksi untuk kebutuhan apa?\",\"answer\":\"Model prediksi dikembangkan untuk memprediksi umpan pulverized coal (PC) pada sistem rotary kiln agar operator CCR dapat mengoptimalkan konsumsi PC dan meningkatkan efisiensi produksi semen.\"},{\"question\":\"Data apa yang digunakan untuk membangun model prediksi umpan PC?\",\"answer\":\"Model memanfaatkan data parameter operasi serta sifat kimia bahan baku dan produk dalam sistem rotary kiln.\"},{\"question\":\"Algoritma machine learning apa yang memberikan akurasi terbaik dan berapa nilai R-squared?\",\"answer\":\"Gradient Boosting memberikan akurasi terbaik dengan R-squared sebesar 0.976.\"}]","Pemodelan Prediksi Umpan Pulverized Coal pada Sistem Rotary Kiln Pabrik Semen - Berdasarkan Parameter Operasi & Kimia Berbasis Machine Learning | PDF",1785815337,29,{"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},"prediction-feed-modeling-of-pulverized-coal-in-cement-rotary-kiln-system-based-on-operational-chemical-parameters-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/id/document/prediction-feed-modeling-of-pulverized-coal-in-cement-rotary-kiln-system-based-on-operational-chemical-parameters-using-machine-learning/123227/",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","2026-08-04",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},"Penelitian ini mengembangkan model prediksi untuk kebutuhan apa?","Question",{"text":76,"@type":77},"Model prediksi dikembangkan untuk memprediksi umpan pulverized coal (PC) pada sistem rotary kiln agar operator CCR dapat mengoptimalkan konsumsi PC dan meningkatkan efisiensi produksi semen.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Data apa yang digunakan untuk membangun model prediksi umpan PC?",{"text":81,"@type":77},"Model memanfaatkan data parameter operasi serta sifat kimia bahan baku dan produk dalam sistem rotary kiln.",{"name":83,"@type":74,"acceptedAnswer":84},"Algoritma machine learning apa yang memberikan akurasi terbaik dan berapa nilai R-squared?",{"text":85,"@type":77},"Gradient Boosting memberikan akurasi terbaik dengan R-squared sebesar 0.976.","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"]