[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118824-id":3,"doc-seo-118824-113":31,"detail-sidebar-cat-0-id-113":93},{"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},118824,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",54,"Penelitian & Laporan","Pemilihan Algoritma Machine Learning Optimal Untuk Prediksi Sifat Mekanik Aluminium","Penelitian ini merancang dan membandingkan model machine learning untuk memprediksi sifat mekanik aluminium berdasarkan persentase komposisi unsur kimia. Terdapat sembilan variabel masukan berupa unsur Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, dan Si, dengan dua target keluaran yaitu Yield Strength (YS) dan Tensile Strength (TS). Korelasi antar unsur kimia dan sifat mekanik dianalisis menggunakan heatmap correlation. Tiga algoritma, Decision Tree (DT), Random Forest (RF), dan Artificial Neural Network (ANN), dievaluasi dan dibandingkan performanya. Random Forest unggul pada prediksi YS, sedangkan ANN lebih baik pada prediksi TS.","Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik  \nAluminium  \n(1)*Desmarita Leni  \n(1)Teknik Mesin, Fakultas Teknik, Universitas Muhammadiyah Sumatera Barat.  \nJl. Pasir Jambak No.4, Pasie Nan Tigo, Kec. Koto Tangah, Kota Padang, Sumatera Barat 25586, Indonesia  \n*[Email: desmaritaleni@gmail.com](Email: desmaritaleni@gmail.com)  \nDiterima: 07.04.2023, Disetujui: 12.05.2023, Diterbitkan: 24.05.2023  \nABSTRACT  \nThis study designs and compares optimal machine learning models to predict the mechanical properties of aluminum, including Yield Strength (YS) and Tensile Strength (TS), based on the percentage composition of aluminum's chemical elements. The machine learning modeling in this study has nine input variables consisting of aluminum chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and two output or target variables consisting of YS and TS. Additionally, Heatmap correlation is used to observe the correlation between chemical elements and the mechanical properties of aluminum. Three machine learning algorithms, namely Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), are compared in this study. The comparison of these algorithms shows that Random Forest (RF) outperforms the other algorithms in predicting YS with MAE of 11.44, RMSE of 14.282, and R value of 0.93. On the other hand, ANN performs better in predicting TS with MAE of 19.593, RMSE of 22.005, andR value of 0.947.  \nKey words : Model, Machine Learning, Aluminum, Tensile Strength, Algorithm  \nABSTRAK  \nPenelitian ini merancang dan membandingkan pemodelan machine learning yang optimal untuk memprediksi sifat mekanik aluminium diantaranya adalah Yield Strenght (YS) dan Tensile Strenght (TS), berdasarkan persentase komposisi unsur kimia aluminium. Pemodelan machine learning padapenelitian ini memiliki 9 variabel masukan yang terdiri dari unsur kimia aluminium seperti, Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, dan 2 output atau target yang terdiri dari YS dan TS, selain itu untukmelihat korelasi antara unsur kimia dan sifat mekanik aluminium digunakan Heatmap correlation. Dalam penelitian ini dibandingkan 3 algoritma machine learning yang terdiri dari Decision Tree (DT), Random Forest (RF), dan Artificial Neural Network (ANN) . Hasil perbandingan ketiga alogaritmadiperoleh bahwa, Random Forest (RF) memiliki kinerja lebih baik dalam memprediksi nila YS dengan nilai MAE 11.44, RMSE 14.282, dan R 0.93, sedangkan ANN memiliki kinerja lebih baik dalam memprediksi nilai TS dengan nilai MAE 19.593, RMSE 22 .005, dan R 0.947.  \nKata kunci : Pemodelan, Machine learning, Aluminium, Tensile Strength, Alogaritma  \nI. Pendahuluan  \nAluminium adalah jenis material non ferrous yang paling banyak digunakan dalamberbagai aplikasi industri modern seperti industri dirgantara, struktural dan otomitif. Menurut data (Usgs, 2022), badan survei geologis Amerika Serikat (AS) atau US Geological Survey, produksi aluminium di seluruh dunia pada tahun 2021 mencapai 68 juta metrik ton. Jumlah tersebut naik 4,45% dibandingkan produksi tahun sebelumnya yang berjumlah 65,1 juta metrik ton (Data aluminum 2022) . Penggunaan aluminium yang luas di berbagai aspek industri modern mendorong engineer untuk lebih teliti dalam mengetahui sifat mekanik aluminium, hal ini  \nbertujuan untuk mencegah terjadinya kegalanpada material, sifat mekanik suatu material memiliki peran yang penting dalam menentukan bahan untuk komponen industri modern demi mencegah terjadinya kegagalanterhadap komponen industri secara prematur (Branco, 2018) . Kekuatan tarik dan kekuatan luluh merupakan dua diantara sifat mekanik material, sifat mekanik material dipengaruhioleh beberapa faktor seperti sruktur mikro, komposisi kimia dan berbagai perlakuan panas (heat treatment) (Antonio Augusto Morini, Manuel J. Ribeiro, 2019) . Sifat mekanik material dapat ditingkatkan dengan cara menambahkan atau mengurangkan unsur kimiatertentu sesuai kebutuhan, seperti penambahan  \nunsur nikel","cbCaijBBlGXv4GS5","https://ap.wps.com/l/cbCaijBBlGXv4GS5","pdf",1092933,7,1,10,"Indonesian","id",113,"# ABSTRAK\n## Pendahuluan","[{\"question\":\"Penelitian ini memprediksi sifat mekanik aluminium apa saja?\",\"answer\":\"Penelitian memprediksi Yield Strength (YS) dan Tensile Strength (TS) berdasarkan komposisi unsur kimia aluminium.\"},{\"question\":\"Variabel apa yang digunakan sebagai masukan pada model machine learning?\",\"answer\":\"Model menggunakan sembilan variabel masukan berupa persentase unsur kimia Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, dan Si.\"},{\"question\":\"Algoritma mana yang terbaik untuk memprediksi YS dan TS?\",\"answer\":\"Random Forest (RF) terbaik untuk memprediksi YS, sedangkan Artificial Neural Network (ANN) terbaik untuk memprediksi TS.\"}]","Pemilihan Algoritma Machine Learning Optimal Untuk Prediksi Sifat Mekanik Aluminium | PDF",1785720469,15,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"optimal-machine-learning-algorithm-selection-for-predicting-mechanical-properties-of-aluminum","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"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":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/optimal-machine-learning-algorithm-selection-for-predicting-mechanical-properties-of-aluminum/118824/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-18","2026-08-03",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Penelitian ini memprediksi sifat mekanik aluminium apa saja?","Question",{"text":77,"@type":78},"Penelitian memprediksi Yield Strength (YS) dan Tensile Strength (TS) berdasarkan komposisi unsur kimia aluminium.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Variabel apa yang digunakan sebagai masukan pada model machine learning?",{"text":82,"@type":78},"Model menggunakan sembilan variabel masukan berupa persentase unsur kimia Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, dan Si.",{"name":84,"@type":75,"acceptedAnswer":85},"Algoritma mana yang terbaik untuk memprediksi YS dan TS?",{"text":86,"@type":78},"Random Forest (RF) terbaik untuk memprediksi YS, sedangkan Artificial Neural Network (ANN) terbaik untuk memprediksi TS.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]