[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121300-id":3,"doc-seo-121300-113":31,"detail-sidebar-cat-0-id-113":96},{"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},121300,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",54,"Penelitian & Laporan","IMPLEMENTASI MACHINE LEARNING PADA ATRISI KARYAWAN BERDASARKAN PARAMETER KINERJA DENGAN MENGGUNAKAN METODE PARTICLE SWARM OPTIMIZATION DAN ENSEMBLE CLASSIFER","Penelitian ini bertujuan menerapkan Machine Learning untuk memprediksi awal atrisi karyawan dengan memanfaatkan parameter kinerja serta faktor terkait di lingkungan perusahaan. Atrisi karyawan dipahami sebagai pergantian dalam organisasi akibat berbagai alasan seperti resignasi, pindah, dan pensiun. Dataset IBM HR Analytics Employee Attrition dari Kaggle digunakan dengan 35 atribut. Proses Particle Swarm Optimization (PSO) dipakai untuk reduksi dimensi agar efisiensi dan performa model meningkat, lalu hasilnya dioptimasi dengan ensemble Bagging dan Boosting. Kombinasi tersebut menghasilkan akurasi terbaik sebesar 86,94% menggunakan metode Deep Learning.","IMPLEMENTATION OF MACHINE LEARNING ON EMPLOYEE ATTRITION BASED ON PERFORMANCE PARAMETERS USING PARTICLE SWARM OPTIMIZATION AND ENSEMBLE CLASSIFER METHODS  \nDifari Afreyna Fauziah*1, Agung Muliawan2, Muhaimin Dimyati3  \n1System and Information Technology, Faculty Of Science, Technology and Industry, Institute Technology and  \nScience Mandala, Indonesia  \n2Software Engineering, Faculty Of Science, Technology and Industry, Institute Technology and Science  \nMandala, Indonesia  \n3Management, Faculty of Economics and Business, Institute Technology and Science Mandala, Indonesia [Email:](Email:1difariafreyna@itsm.ac.id)[1](Email:1difariafreyna@itsm.ac.id)[difariafreyna@itsm.ac.id](Email:1difariafreyna@itsm.ac.id), [2](2agung.muliawan@itsm.ac.id)[agung.muliawan@itsm.ac.id](2agung.muliawan@itsm.ac.id), [3](3dimyati@itsm.ac.id)[dimyati@itsm.ac.id](3dimyati@itsm.ac.id)  \n(Article received: September 7, 2024; Revision: October 6, 2024; published: December 29, 2024)  \nAbstract  \nThis research aims to apply machine learning to predict the start of employee attrition by considering performance parameters and other related factors in the company environment. Employee attrition refers to employee turnover in an organization for various reasons such as resignation, moving, retirement, and so on. This research uses a dataset originating from the IBM HR Analytics Employee Attrition dataset available on Kaggle ([https://www.kaggle.com/](https://www.kaggle.com/)) which consists of 35 attributes. Particle Swarm Optimization (PSO) method is a dimension reduction method to improve the efficiency and performance of machine learning models by reducing unnecessary data. The machine learning approaches used in the early prediction of employee attrition in this research are Support Vector Machine, Deep Learning and Neural Network methods. This research will combine the dimensionality reduction process with machine learning to obtain employee attrition prediction results that are optimized using the Ensemble method, namely Bagging and Boosting to increase the accuracy value of the prediction results. The results of this research show that applying dimensionality reduction using the PSO method can improve the accuracy of results on the IBM HR Analytics Employee Attrition dataset. The best accuracy in attrition prediction was obtained by the Deep Learning method with an accuracy value of 86.94%, a precision value of 88.90%, and a recall value of 96.40% after combining it with PSO and optimizing with Bagging.  \nKeywords: Employee Attrition; Employee Retention; Employee Performance; Highest Accuracy; Machine learning  \nIMPLEMENTASI MACHINE LEARNING PADA ATRISI KARYAWAN BERDASARKAN PARAMETER KINERJA DENGAN MENGGUNAKAN METODE PARTICLE SWARM OPTIMIZATION DAN ENSEMBLE CLASSIFER  \nAbstrak  \nPenelitian ini bertujuan untuk menerapkan Machine Learning dalam memprediksi awal atrisi karyawan dengan mempertimbangkan parameter kinerja dan faktor terkait lainnya yang ada di lingkungan perusahaan. Atrisikaryawan merujuk pada pergantian karyawan dalam organisasi dengan berbagai alasan seperti resignasi, pindah, pensiun dan sebagainya. Penelitian ini menggunakan dataset yang berasal dari datasets IBM HR Analytics Employee Attrition yang tersedia di Kaggle ([https://www.kaggle.com/](https://www.kaggle.com/)) yang terdiri dari 35 atribut. Metode Particle Swarm Optimization (PSO) merupakan metode pengurangan dimensi untuk meningkatkan efisiensi dan kinerja model machine learning dengan melakukan pengurangan data yang tidak dibutuhkan. Pendekatan Machine Learning yang digunakan dalam prediksi awal atrisi karyawan pada penelitian ini yaitu metode Support Vector Machine, Deep Learning dan Neural Network. Penelitian ini akan melakukan kombinasi dari prosesreduksi dimensi dengan Machine Learning untuk mendapatkan hasil prediksi atrisi karyawan yang dioptimasidengan metode Ensemble yaitu Bagging dan Boosting untuk meningkatkan nilai akurasi dari hasil prediksi. Hasil penelitian ini me","cbCaisguBFiBtqAg","https://ap.wps.com/l/cbCaisguBFiBtqAg","pdf",1009152,4,1,9,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang atrisi karyawan\n## Dampak atrisi terhadap perusahaan\n# Metode penelitian\n## Dataset IBM HR Analytics Employee Attrition\n## Particle Swarm Optimization (PSO)\n## Model klasifikasi dan ensemble","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Penelitian ini bertujuan menerapkan Machine Learning untuk memprediksi awal atrisi karyawan dengan mempertimbangkan parameter kinerja dan faktor terkait di lingkungan perusahaan.\"},{\"question\":\"Dataset apa yang digunakan dan berapa jumlah atributnya?\",\"answer\":\"Penelitian menggunakan dataset IBM HR Analytics Employee Attrition yang tersedia di Kaggle, dengan total 35 atribut.\"},{\"question\":\"Bagaimana PSO dan metode ensemble digunakan dalam penelitian ini?\",\"answer\":\"PSO digunakan untuk reduksi dimensi agar model lebih efisien dan performanya meningkat. Setelah itu, hasilnya dioptimasi menggunakan ensemble seperti Bagging dan Boosting untuk meningkatkan akurasi prediksi.\"},{\"question\":\"Berapa performa terbaik yang diperoleh pada prediksi atrisi karyawan?\",\"answer\":\"Akurasi terbaik diperoleh oleh metode Deep Learning dengan nilai akurasi 86,94%, presisi 88,90%, dan recall 96,40% setelah dikombinasikan dengan PSO dan dioptimasi dengan Bagging.\"}]","IMPLEMENTASI MACHINE LEARNING PADA ATRISI KARYAWAN BERDASARKAN PARAMETER KINERJA DENGAN MENGGUNAKAN METODE PARTICLE SWARM OPTIMIZATION DAN ENSEMBLE CLASSIFER | PDF",1785734969,14,{"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":91,"head_meta":93,"extra_data":95,"updated_unix":29},"implementation-of-machine-learning-for-employee-attrition-based-on-performance-parameters-using-particle-swarm-optimization-and-ensemble-classifier","",{"@graph":37,"@context":90},[38,54,69],{"@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":20},"https://docshare.wps.com/id/document/implementation-of-machine-learning-for-employee-attrition-based-on-performance-parameters-using-particle-swarm-optimization-and-ensemble-classifier/121300/",{"url":53,"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","2026-08-18","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"Apa tujuan penelitian ini?","Question",{"text":76,"@type":77},"Penelitian ini bertujuan menerapkan Machine Learning untuk memprediksi awal atrisi karyawan dengan mempertimbangkan parameter kinerja dan faktor terkait di lingkungan perusahaan.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Dataset apa yang digunakan dan berapa jumlah atributnya?",{"text":81,"@type":77},"Penelitian menggunakan dataset IBM HR Analytics Employee Attrition yang tersedia di Kaggle, dengan total 35 atribut.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana PSO dan metode ensemble digunakan dalam penelitian ini?",{"text":85,"@type":77},"PSO digunakan untuk reduksi dimensi agar model lebih efisien dan performanya meningkat. Setelah itu, hasilnya dioptimasi menggunakan ensemble seperti Bagging dan Boosting untuk meningkatkan akurasi prediksi.",{"name":87,"@type":74,"acceptedAnswer":88},"Berapa performa terbaik yang diperoleh pada prediksi atrisi karyawan?",{"text":89,"@type":77},"Akurasi terbaik diperoleh oleh metode Deep Learning dengan nilai akurasi 86,94%, presisi 88,90%, dan recall 96,40% setelah dikombinasikan dengan PSO dan dioptimasi dengan Bagging.","https://schema.org",{"og:url":53,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,103,107,111,115,119,121,125,129,133,137],{"id":99,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":101,"slug":106},48,"Cerita & Novel","story-novel",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":101,"slug":110},56,"Gaya Hidup","lifestyle",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":101,"slug":114},51,"Komik","comic",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":101,"slug":118},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":101,"slug":120},"research-report",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":101,"slug":124},49,"Sastra","literature",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":101,"slug":128},52,"Teknologi","technology",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":101,"slug":132},50,"Ujian","exam",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":101,"slug":136},57,"Umum","general",{"id":138,"doc_module":4,"doc_module_name":47,"category_name":139,"show_sort_weight":4,"slug":140},181,"Formulir","formulir"]