[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125689-id":3,"doc-seo-125689-113":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},125689,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",54,"Penelitian & Laporan","Prediksi Kekuatan Gempa Menggunakan Machine Learning dengan Model XGBoost sebagai Langkah Strategis dalam Perencanaan Struktur Bangunan Tahan Gempa di Indonesia","Besarnya beban gempa pada struktur bangunan dipengaruhi banyak variabel, seperti gaya horizontal dan vertikal, momen torsi akibat gempa, serta keterkaitan dengan berat dan kekakuan material struktur, konfigurasi sistem struktur, periode getar, kondisi tanah dasar, wilayah kegempaan, dan perilaku gempa itu sendiri. Penelitian ini mengembangkan prediksi kekuatan gempa di Indonesia menggunakan machine learning, khususnya model XGBoost, dengan dataset BMKG berisi 92.887 kejadian. Data dipisah menjadi training 80% dan testing 20%. Kinerja dinilai memakai RMSE, MAE, MSE, dan R-squared, menghasilkan RMSE training 0,07 dan R2 0,90; sedangkan pada pengujian RMSE 0,01 dan R2 0,98.","PREDIKSI KEKUATAN GEMPA MENGGUNAKAN MACHINE LEARNING DENGAN MODEL XGBOOST SEBAGAI LANGKAH STRATEGIS DALAMPERENCANAAN STRUKTUR BANGUNANTAHAN GEMPA DI INDONESIA  \nAri Wibowo  \nTeknik Arsitektur-Universitas Subang  \n[e-mail: ariwibowo@unsub.ac.id](e-mail: ariwibowo@unsub.ac.id)  \nAbstract  \nThe magnitude of the earthquake load acting on a building structure depends on many variables. The horizontal force, vertical force, and torsion moment that occurs due to an earthquake in a structure, are very dependent on the weight and stiffness of the structural material, configuration and structural system, the period or time of the structure's vibration, the condition of the subgrade, the earthquake area, and the behavior of the earthquake itself. So that the impact of earthquakes on building structures can be minimized, research is needed to predict the strength of earthquakes in Indonesia. Although it cannot be denied that predicting when, where and the magnitude of the next earthquake in an area or time seems quite difficult, there is an alternative for prediction using machine learning which can help predict the occurrence of an earthquake. The machine learning model used in this research is XGBoost. Meanwhile, the dataset used was taken from the BMKG catalog, where the data consists of 92,887 earthquake events in Indonesia. We separated this data into three parts as network training data, validation data and testing data with details of 80% for the training process or as many as 74309 and 20% for the testing process or as many as 18578. From the research conducted, it shows that the training stage is able to produce accuracy very well, this can be proven by looking at the model performance during training through RMSE, MAE, MSE, and R-squared losses. Where the RMSE in the training process is very small, namely 0.07 and the RSquared (R2) value is 0.90. Meanwhile, in the data testing process, high accuracy was also obtained. This can be seen from the RMSE value of 0.01 and the R-Squared (R2) value of 0.98.  \nKeywords: earthquake prediction, earthquake resistant buildings, Machine Learning, XGBoost  \nAbstrak  \nBesarnya beban gempa yang bekerja pada struktur bangunan, tergantung dari banyak variabel. Gaya horisontal, gaya vertikal, dan momen torsi yang terjadi akibat gempa pada struktur, sangattergantung pada berat dan kekakuan material struktur, konfigurasi, dan sistem struktur, periode atauwaktu getar struktur, kondisi tanah dasar, wilayah kegempaan, serta perilaku gempa itu sendiri. Agar dampak gempa pada struktur bangunan dapat diminimalisirkan, maka perlu adanya penelitiandalam memprediksi kekuatan gempa di wilayah Indonesia. Walaupun tidak dapat dipungkiri memprediksi kapan, dimana dan besarnya gempa bumi berikutnya dalam wilayah atau waktu tampaknya cukup sulit, namun terdapat suatu alternative untuk prediksi menggunakan machine  \nlearning yang dapat membantu memperkirakan terjadinya gempa. Adapun model machine learning yang digunakan dalam penelitian ini adalah XGBoost. sedangkan dataset yang digunakan diambildari katalog BMKG, dimana data tersebut terdiri dari 92.887 kejadian gempa di Indonesia. Data tersebut kami pisahkan menjadi tiga bagian sebagai data pelatihan jaringan, data validasi dan data pengujian dengan rincian 80% untuk proses pelatihan atau sebanyak 74309 dan 20% untuk proses pengujian atau sebanyak 18578. Dari penelitian yang dilakukan, menunjukkan bahwa pada tahappelatihan mampu menghasilkan akurasi dengan sangat baik, hal ini dapat dibuktikan dengan melihat kinerja model selama pelatihan melalui loss RMSE, MAE, MSE, dan R-squared. Dimana RMSEpada proses pelatihan sangat kecil yaitu 0.07 dan nilai R-Squared (R2) yang bernilai 0.90. sedangkan pada proses pengujian data juga diperoleh akurasi yang tinggi. Hal ini dapat dilihat dari hasil nilai RMSE 0.01 dan nilai R-Squared (R2) yang bernilai 0.98.  \nKata Kunci: prediksi gempa, bangunan tahan gempa, Machine Learning, XGBoost  \nPENDAHULUAN  \nGempa bumi merupakan gunca","cbCaicE7DUPIsb8w","https://ap.wps.com/l/cbCaicE7DUPIsb8w","pdf",881200,7,1,12,"Indonesian","id",113,"# Pendahuluan\n## Faktor penyebab dan karakteristik gempa\n## Dampak gempa terhadap infrastruktur\n# Metode Penelitian\n## Model machine learning XGBoost\n## Dataset BMKG dan pembagian data\n## Evaluasi kinerja model","[{\"question\":\"Mengapa prediksi kekuatan gempa pada struktur bangunan diperlukan?\",\"answer\":\"Dampak gempa pada struktur sangat bergantung pada banyak variabel, sehingga prediksi kekuatan gempa dibutuhkan untuk meminimalkan kerusakan pada bangunan.\"},{\"question\":\"Model machine learning apa yang digunakan dalam penelitian ini?\",\"answer\":\"Penelitian ini menggunakan model XGBoost untuk memprediksi kekuatan gempa.\"},{\"question\":\"Bagaimana pembagian dataset dan hasil evaluasi pada data latih serta uji?\",\"answer\":\"Dataset BMKG sebanyak 92.887 kejadian dibagi 80% untuk training (74.309) dan 20% untuk testing (18.578). Pada training diperoleh RMSE 0,07 dan R2 0,90, sedangkan pada testing RMSE 0,01 dan R2 0,98.\"}]","Prediksi Kekuatan Gempa Menggunakan Machine Learning dengan Model XGBoost sebagai Langkah Strategis dalam Perencanaan Struktur Bangunan Tahan Gempa di Indonesia | PDF",1785900676,18,{"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},"earthquake-strength-prediction-using-machine-learning-with-xgboost-as-a-strategic-step-for-designing-earthquake-resistant-building-structures-in-indonesia","",{"@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/earthquake-strength-prediction-using-machine-learning-with-xgboost-as-a-strategic-step-for-designing-earthquake-resistant-building-structures-in-indonesia/125689/",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","2026-08-17","2026-08-05",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},"Mengapa prediksi kekuatan gempa pada struktur bangunan diperlukan?","Question",{"text":77,"@type":78},"Dampak gempa pada struktur sangat bergantung pada banyak variabel, sehingga prediksi kekuatan gempa dibutuhkan untuk meminimalkan kerusakan pada bangunan.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Model machine learning apa yang digunakan dalam penelitian ini?",{"text":82,"@type":78},"Penelitian ini menggunakan model XGBoost untuk memprediksi kekuatan gempa.",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana pembagian dataset dan hasil evaluasi pada data latih serta uji?",{"text":86,"@type":78},"Dataset BMKG sebanyak 92.887 kejadian dibagi 80% untuk training (74.309) dan 20% untuk testing (18.578). Pada training diperoleh RMSE 0,07 dan R2 0,90, sedangkan pada testing RMSE 0,01 dan R2 0,98.","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"]