[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117520-id":3,"doc-seo-117520-113":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},117520,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",54,"Penelitian & Laporan","Prediksi Kuat Tekan Kolom Beton Persegi dengan Perkuatan FRP Menggunakan Machine Learning","Analisis kuat tekan aksial kolom beton persegi yang diperkuat FRP dibahas melalui pendekatan machine learning sebagai alternatif dari model analitik dan mekanik yang sebelumnya banyak mengacu pada kolom lingkaran dengan modifikasi. Penelitian memprediksi kuat tekan dengan variasi radius kelengkungan pada sudut FRP serta membandingkan tiga algoritma individual—JST, CatBoost, dan M5-Tree. Hasil menunjukkan ensemble machine learning memberi akurasi lebih baik (R-squared 0,9557).","Prediksi Kuat Tekan Kolom Beton Persegi dengan Perkuatan FRP Menggunakan Machine Learning  \nANANG KRISTIANTO 1*, YOSAFAT AJI PRANATA 1,  \nNOVIE THERESIA Br. PASARIBU2, JASEN ALFATAMA2  \n1Program Studi Teknik Sipil, Universitas Kristen Maranatha, Bandung, Indonesia  \n2 Program Studi Teknik Elektro, Universitas Kristen Maranatha, Bandung, Indonesia Email: [anang.kristianto@eng.maranatha.edu](anang.kristianto@eng.maranatha.edu1)[1](anang.kristianto@eng.maranatha.edu1), [yosafat.ap@eng.maranatha.edu](yosafat.ap@eng.maranatha.edu1)[1](yosafat.ap@eng.maranatha.edu1), [novie.theresia@eng.maranatha.edu](novie.theresia@eng.maranatha.edu2)[2](novie.theresia@eng.maranatha.edu2)  \nABSTRAK  \nAnalisis secara teoritis kuat tekan kolom beton persegi dengan perkuatan FRP masih mengandalkan pendekatan seperti yang digunakan untuk kolom lingkaran dengan modifikasi. Sesuai dengan kemajuan teknologi saat ini pendekatan menggunakan machine learning diharapakan dapat memberikan perspektif berbeda sekaligus hasil yang lebih akurat. Tujuan dari penelitian ini adalah menggunakan beberapa teknik machine learning untuk memprediksi kuat tekan aksial kolom persegi yang diperkuat dengan FRP dengan berbagai radius kelengkungan pada sudutnya dan membandingkan metode machine learning dengan pendekatan analitik dan mekanik yang sudah ada. Tiga algoritma digunakan dalam pendekatan machine learning: Jaringan Syaraf Tiruan (JST), Categorical Boost (CatBoost), dan M5-Tree. Ensemble Machine Learning membuat kinerja yang lebih baik dibandingkan dengan tiga model individual JST, CatBoost, dan M5-Tree dengan nilai R-squared sebesar 0,9557. Penggunaan machine learning untuk memprediksi dibandingkan dengan beberapa usulan teori tegangan terkekang, memberikan hasil yang paling mendekati dengan nilai R-squared sebesar 0,8276.  \nKata kunci: tegangan, kolom beton, FRP, prediksi, pembelajaran mesin  \nABSTRACT  \nTheoretically analyzing the compressive strength of square concrete columns with FRP reinforcement still relies on the approach as used for circular columns with modifications. In accordance with current technological advances, machine learning approaches are expected to provide a different perspective as well as more accurate results. The objective of this research is to use several machine learning techniques to predict the axial compressive strength of square columns reinforced with FRP with various radii of curvature at the corners and compare the machine learning methods with existing analytical and mechanical approaches. Three algorithms were used in the machine learning approach: Artificial Neural Network (ANN), Categorical Boost (CatBoost), and M5-Tree. The machine learning ensemble performed better than the three individual models JST, CatBoost, and M5-Tree with an R-squared value of 0.9557. The use of machine learning to predict, compared to some proposed confined stress theories, gave the closest results with an R-squared value of 0.8276.  \nKeywords: stress, concrete column, FRP, prediction, machine learning  \n1. PENDAHULUAN  \nKolom beton bertulang (RC) yang diperkuat dengan menggunakan Fiber Reinforced Polymer (FRP) telah banyak digunakan untuk meningkatkan kapasitas aksial kolom. Untuk mensimulasikan perilaku kolom beton yang dikekang dengan FRP, sejumlah penelitian dan model kekuatan telah banyak diusulkan [11][21] . Sebagian besar model yang saat ini digunakan didasarkan pada penelitian Richart dkk. [16], dimana model ini menggunakan pendekatan penampang melingkar yang memberikan tekanan pengekangan yang merata, dengan memperhitungkan diameter penampang serta kekuatan dan ketebalan lembaran Fiber Reinforced Polymer (FRP) .  \nApabila dibandingkan dengan kolom melingkar, model untuk kolom persegi panjang yang dikekang FRP jauh lebih sedikit [11][21] . Kolom dengan penampang persegi yang terkekang FRP mengalami tekanan pengekangan yang tidak seragam di sepanjang sisi perimeternya. Hal ini menjadi kesulitan tersendiri dalam menganalisis distribusi tekanan de","cbCaiiHzUFpQMrz5","https://ap.wps.com/l/cbCaiiHzUFpQMrz5","pdf",491272,4,1,12,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang pemodelan kolom beton dengan FRP\n## Keterbatasan model kolom persegi dibanding kolom melingkar\n## Pendekatan mekanik dan elemen hingga\n## Pendekatan analitis matematis\n## Transisi menuju machine learning","[{\"question\":\"Mengapa pendekatan machine learning digunakan untuk memprediksi kuat tekan kolom beton persegi dengan FRP?\",\"answer\":\"Karena analisis teoritis sebelumnya masih bertumpu pada pendekatan kolom lingkaran dengan modifikasi, sementara machine learning diharapkan memberi perspektif berbeda dan hasil yang lebih akurat.\"},{\"question\":\"Algoritma apa saja yang digunakan dalam pendekatan machine learning pada penelitian ini?\",\"answer\":\"Tiga algoritma individual digunakan, yaitu Jaringan Syaraf Tiruan (JST), CatBoost, dan M5-Tree.\"},{\"question\":\"Seberapa baik kinerja ensemble machine learning dibanding model individual?\",\"answer\":\"Ensemble machine learning menunjukkan kinerja lebih baik dibanding tiga model individual dengan nilai R-squared sebesar 0,9557.\"}]","Prediksi Kuat Tekan Kolom Beton Persegi dengan Perkuatan FRP Menggunakan Machine Learning | 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pendekatan machine learning digunakan untuk memprediksi kuat tekan kolom beton persegi dengan FRP?","Question",{"text":76,"@type":77},"Karena analisis teoritis sebelumnya masih bertumpu pada pendekatan kolom lingkaran dengan modifikasi, sementara machine learning diharapkan memberi perspektif berbeda dan hasil yang lebih akurat.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Algoritma apa saja yang digunakan dalam pendekatan machine learning pada penelitian ini?",{"text":81,"@type":77},"Tiga algoritma individual digunakan, yaitu Jaringan Syaraf Tiruan (JST), CatBoost, dan M5-Tree.",{"name":83,"@type":74,"acceptedAnswer":84},"Seberapa baik kinerja ensemble machine learning dibanding model individual?",{"text":85,"@type":77},"Ensemble machine learning menunjukkan kinerja lebih baik dibanding tiga model individual dengan nilai R-squared sebesar 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