[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118917-id":3,"doc-seo-118917-113":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},118917,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",54,"Penelitian & Laporan","Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN)","Penelitian ini merancang pemodelan machine learning dengan algoritma Artificial Neural Network (ANN) untuk memprediksi kekuatan tarik aluminium. Model menggunakan 8 variabel input berupa persentase komposisi kimia aluminium (Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si) dan 1 variabel output yaitu tensile strength. Performa ditingkatkan melalui variasi parameter seperti jumlah split data, training cycle, learning rate, dan jumlah hidden neurons. Hasil pemodelan menghasilkan nilai RMSE 15,383 dengan konfigurasi terbaik pada split 60% training dan 40% testing, training cycle 100, learning rate 0,08, momentum 0,9, serta 7 hidden neurons.","Pemodelan Machine Learning Untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN)  \nDesmarita Leni1*, Helga yermadona2, Ade Usra Berli 3, Ruzita Sumiati 4, Haris5  \n1Teknik Mesin, Fakultas Teknik, Universitas Muhammadiyah Sumatera Barat Jl. Pasir Jambak No.4, Pasie Nan Tigo, Kec. Koto Tangah, Kota Padang, Sumatera Barat 25586  \n2,3Teknik Sipil, Fakultas Teknik, Universitas Muhammadiyah Sumatera Barat Jl. Pasir Jambak No.4, Pasie Nan Tigo, Kec. Koto Tangah, Kota Padang, Sumatera Barat 25586  \n4,5Teknik Mesin, Politeknik Negeri Padang  \nJl. Kampus, Limau Manis, Kec. Pauh, Kota Padang, Sumatera Barat 25164  \nE-mail: [desmaritaleni@gmail.com](desmaritaleni@gmail.com1)[1](desmaritaleni@gmail.com1)  \nAbstract  \nThis research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.  \nKeywords: Algorithm, Artificial Neural Network (ANN), Aluminum  \nAbstrak  \nPenelitian ini merancang sebuah pemodelan machine learning menggunakan algoritma Artificial Neural Network (ANN) untuk memprediksi kekuatan tarik aluminium. Penelitian ini menghasilkan sebuah model machine learning yang memiliki 8 (delapan) variabel data input (masukan) yang terdiri dari presentase komposisi kimia aluminium seperti Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, dan 1 output (luaran) yaitu kekuatan tarik aluminium. Penelitian ini melakukan perubahan beberapa variasi parameter seperti variasi jumlah splitdata, training cycle, learning rate, dan hidden neuron. Pemodelan Artificial Neural Network (ANN) ini menghasilkan nilai RMSE 15.383 dengan parameter terbaik datasplit 60 data training dan 40 data testing, training cycle 100, learning rate 0.08, momentum 0.9 dan hidden neuron 7.  \nKata kunci: Algoritma, Artificial Neural Network (ANN), Aluminium  \n1. Pendahuluan  \nAluminium adalah jenis material non ferrous yang paling banyak digunakan dalam berbagaiaplikasi industri modern seperti industri dirgantara, struktural dan otomotif. Menurut data badan survei geologis Amerika Serikat (AS) atau US Geological Survey, produksi aluminium di seluruh dunia padatahun 2021 mencapai 68 juta metrik ton. Jumlah tersebut naik 4,45% dibandingkan produksi tahunsebelumnya yang berjumlah 65,1 juta metrik ton [1] . Pengaplikasian aluminium yang luas dan hampir setiap harinya penggunaan aluminium baru dikembangkan, hal ini disebabkan oleh sifatnya yang ringan, ketahanan korosi , kekuatan yang sangatbaik, dapat di daur ulang dan biaya ekstrusi aluminium relatif lebih rendah dibandingkan  \nmaterial logam lainnya [2] . Penggunaan aluminium yang begitu pesat dan beragam di dunia teknik mendorong industri aluminium untuk mengembangkan paduan aluminium yang sesuaidengan pengaplikasiannya, hal ini bertujuan untuk mencegah terjadinya kegagalan pada material, sifatmekanik suatu material memiliki peran yang penting dalam menentukan bahan untuk komponen industri modern demi mencegah terjadinya kegagalanterhadap komponen industri secara prematur [3] . Sifat mekanik material diantaranya adalah kekuatan, kekuatan merupakan kemampuan material untuk menahan deformasi plastis atau patah, dan sifat kekuatan seperti kekuatan tarik dan plastisitas material dipengaruhi oleh komposisi kimianya[4] . Selain itu, perlakuan panas, seperti annealing,  \ntempering dan quenching, dapat se","cbCaisWXQ0IuMXKT","https://ap.wps.com/l/cbCaisWXQ0IuMXKT","pdf",491050,5,1,8,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang penggunaan aluminium dan kebutuhan prediksi sifat mekanik\n## Konsep machine learning dan ANN untuk estimasi/prediksi\n# Metode Penelitian\n## Perancangan model ANN dan variabel input-output\n## Variasi parameter pemodelan\n# Hasil dan Pembahasan\n## Evaluasi kinerja menggunakan RMSE\n## Parameter terbaik model","[{\"question\":\"Penelitian ini memprediksi apa dengan model machine learning?\",\"answer\":\"Model memprediksi tensile strength atau kekuatan tarik aluminium sebagai output tunggal.\"},{\"question\":\"Apa saja variabel input yang digunakan untuk ANN pada penelitian ini?\",\"answer\":\"Input terdiri dari 8 variabel berupa persentase komposisi kimia aluminium, yaitu Mg, Zn, Ti, Cu, Mn, Cr, Fe, dan Si.\"},{\"question\":\"Parameter ANN apa yang divariasikan untuk memperoleh performa terbaik?\",\"answer\":\"Penelitian memvariasikan jumlah split data, training cycle, learning rate, momentum, serta jumlah hidden neurons.\"}]","Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN) | 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ini memprediksi apa dengan model machine learning?","Question",{"text":77,"@type":78},"Model memprediksi tensile strength atau kekuatan tarik aluminium sebagai output tunggal.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Apa saja variabel input yang digunakan untuk ANN pada penelitian ini?",{"text":82,"@type":78},"Input terdiri dari 8 variabel berupa persentase komposisi kimia aluminium, yaitu Mg, Zn, Ti, Cu, Mn, Cr, Fe, dan Si.",{"name":84,"@type":75,"acceptedAnswer":85},"Parameter ANN apa yang divariasikan untuk memperoleh performa terbaik?",{"text":86,"@type":78},"Penelitian memvariasikan jumlah split data, training cycle, learning rate, momentum, serta jumlah hidden 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