[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-128792-113":53,"doc-detail-128792-id":132},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":125,"head_meta":127,"extra_data":129,"updated_unix":131},113,"id","use-of-machine-learning-in-hr-information-systems-for-predicting-employee-turnover-decision-tree-algorithm","Penggunaan Machine Learning dalam Sistem Informasi SDM untuk Prediksi Turnover Karyawan - Algoritma Decision Tree","","Turnover karyawan menjadi isu penting dalam industri laundry karena dapat mengganggu kesinambungan operasional dan kualitas layanan. Penelitian ini mengembangkan model prediksi turnover menggunakan pendekatan machine learning yang diintegrasikan ke Sistem Informasi Sumber Daya Manusia (SISDM), dengan algoritma Decision Tree sebagai metode utama. Model memanfaatkan atribut karyawan seperti usia, masa kerja, kinerja, kehadiran, dan gaji. Decision Tree dipilih karena interpretabilitasnya serta kemampuannya menangani data kategorikal dan numerik. Hasil penelitian menunjukkan prediksi yang akurat untuk mengidentifikasi karyawan berisiko tinggi meninggalkan perusahaan, sehingga manajemen dapat mengambil keputusan proaktif. Sistem ini mendukung strategi retensi dan peningkatan pengelolaan SDM pada sektor laundry.",{"@graph":63,"@context":124},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/use-of-machine-learning-in-hr-information-systems-for-predicting-employee-turnover-decision-tree-algorithm/128792/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/use-of-machine-learning-in-hr-information-systems-for-predicting-employee-turnover-decision-tree-algorithm/128792.png","ImageObject",300,407,{"name":89,"@type":90},"Violet","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-19","2026-08-06",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",9,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116,120],{"name":107,"@type":108,"acceptedAnswer":109},"Mengapa turnover karyawan penting untuk ditangani di industri laundry?","Question",{"text":110,"@type":111},"Turnover dapat menyebabkan ketidakstabilan operasional dan meningkatkan biaya rekrutmen serta pelatihan karyawan baru, sekaligus berdampak pada kualitas layanan.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana penelitian ini memprediksi turnover karyawan?",{"text":115,"@type":111},"Penelitian mengembangkan model prediksi menggunakan machine learning dengan algoritma Decision Tree yang diintegrasikan ke dalam SISDM.",{"name":117,"@type":108,"acceptedAnswer":118},"Data atribut apa yang digunakan dalam model Decision Tree?",{"text":119,"@type":111},"Model menggunakan atribut karyawan seperti usia, masa kerja, kinerja, kehadiran, dan gaji.",{"name":121,"@type":108,"acceptedAnswer":122},"Apa keunggulan Decision Tree dalam konteks penelitian ini?",{"text":123,"@type":111},"Decision Tree dipilih karena mudah diinterpretasikan serta efektif menangani data kategorikal dan numerik, sehingga cocok untuk pengambilan keputusan manajerial.","https://schema.org",{"og:url":79,"og:type":126,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":128,"canonical":79},"index,follow",{"doc_id":130,"site_id":56},128792,1786003465,{"code":4,"msg":5,"data":133},{"doc_id":130,"user_id":134,"nickname":89,"user_avatar":135,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":136,"file_id":137,"file_url":138,"file_type":139,"file_size":140,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":141,"language":142,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":143,"faqs":144,"seo_title":145,"seo_description":61,"update_tm":131,"read_time":146},1099523885336,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Journal of Information Technology and Computer Science (INTECOMS)  \nVolume 8 Nomor 4, Tahun 2025 e-ISSN : 2614-1574  \np-ISSN : 2621-3249  \nPENGGUNAAN MACHINE LEARNING DALAM SISTEM INFORMASI SDMUNTUK PREDIKSI TURNOVER KARYAWAN FAZHAR LAUNDRY DENGAN  \nALGORITMA DECISSION TREE  \nTHE USE OF MACHINE LEARNING IN HR INFORMATION SYSTEMS TO PREDICT EMPLOYEE TURNOVER ATFAZHAR LAUNDRY WITH THE DECISION  \nTREE ALGORITHM  \nSri Lestari1, Rofika Qolbi2  \nSekolah Tinggi Ilmu Komputer Cipta Karya Informatika 1,2  \n[Rofikaqolbi45@gmai.com](Rofikaqolbi45@gmai.com2)[2](Rofikaqolbi45@gmai.com2)  \nABSTRACT  \nEmployee turnover is a critical issue in the laundry industry, affecting operational continuity and service quality. This study aims to develop a predictive model for employee turnover using machine learning techniques, specifically the DecisioTree algorithm, integrated into a Human Resource Information System (HRIS). The model utilizes various employee attributes such as age, length of service, performance, attendance, and salary. The Decision Tree algorithm is chosen for its interpretability and ability to handle both categorical and numerical data effectively. The results demonstrate that the model can accurately predict employees at high risk of leaving, enabling management to make proactive decisions. This system provides valuable insights for improving employee retention strategies and enhancing overall HR management in the laundry sector.  \nKeywords: Machine Learning, Employee Turnover, Decision Tree, Human Resource Information System, Prediction, Laundry Industry.  \nABSTRAK  \nTurnover karyawan merupakan tantangan signifikan dalam industri laundry yang dapat berdampak pada efisiensi operasional dan kualitas layanan. Penelitian ini bertujuan untuk mengembangkan sistem prediksi turnover karyawan menggunakan pendekatan machine learning dengan algoritma Decision Tree yang diintegrasikan ke dalam sistem informasi sumber daya manusia (SDM) . Data yang digunakan mencakup faktor-faktor seperti usia, masa kerja, kinerja, kehadirandan gaji. Algoritma Decision Tree dipilih karena kemampuannyadalammenghasilkan model yang mudah diinterpretasikan serta efektif dalam menangani data kategorikal dan numerik. Hasil penelitian menunjukkan bahwa modelprediksi yang dikembangkan mampu mengidentifikasipotensi karyawan yang berisiko tinggi untuk melakukan turnover dengan tingkat akurasi yang memadai. Sistemini diharapkan dapat membantu manajemen dalam mengambil keputusan strategis untuk menurunkan tingkat turnover serta meningkatkan retensi karyawan.  \nKata Kunci: Machine Learning, Turnover Karyawan, Decision Tree, Sistem Informasi SDM, Prediksi, Industri Laundry.  \nPENDAHULUAN  \nKetenagakerjaan modern yang dinamis menghadirkan tantangan signifikan bagi organisasi, terutama dalam mengelola modal manusia secara efektif. Turnover karyawan, masalah yang umum terjadi, dapat menyebabkan beban finansial yang besar bagi perusahaan, meliputi biaya rekrutmen, biaya pelatihan untuk karyawan baru, dan penurunanproduktivitas selama periode transisi. Bagiusaha kecil dan menengah (UKM) seperti Fazhar Laundry, di mana sumber dayamungkin lebih terbatas, meminimalkan  \nturnover yang tidak terduga sangat penting untuk menjaga stabilitas operasional dan pertumbuhan yang berkelanjutan. Metode tradisional untuk mengidentifikasikaryawan yang berisiko keluar seringkali mengandalkan tindakan reaktif ataupenilaian subjektif oleh personel SDM, yang bisa memakan waktu, tidak konsisten, dan kurang akurat.Dalambeberapa tahun terakhir, integrasi teknikanalitik canggih, khususnya Machine Learning (ML), ke dalam Sistem Informasi Sumber Daya Manusia (SISDM) telah menunjukkan potensi besar  \ndalam mengubah manajemen tenaga kerja yang proaktif.  \nAlgoritma Machine Learning dapat menganalisis sejumlah besar data historiskaryawan, termasuk demografi, tinjauan kinerja, perubahan gaji, masa kerja, dan catatan pelatihan, untuk mengidentifikasi pola kompleks dan memprediksi hasil dimasa depan. Di anta","cbCainFHsyTOXy3j","https://ap.wps.com/l/cbCainFHsyTOXy3j","pdf",489433,8,"Indonesian","# Pendahuluan\n## Latar belakang dan urgensi turnover karyawan\n## Identifikasi Masalah (Research Problem)\n## Tujuan penelitian dan manfaat implementasi","[{\"question\":\"Mengapa turnover karyawan penting untuk ditangani di industri laundry?\",\"answer\":\"Turnover dapat menyebabkan ketidakstabilan operasional dan meningkatkan biaya rekrutmen serta pelatihan karyawan baru, sekaligus berdampak pada kualitas layanan.\"},{\"question\":\"Bagaimana penelitian ini memprediksi turnover karyawan?\",\"answer\":\"Penelitian mengembangkan model prediksi menggunakan machine learning dengan algoritma Decision Tree yang diintegrasikan ke dalam SISDM.\"},{\"question\":\"Data atribut apa yang digunakan dalam model Decision Tree?\",\"answer\":\"Model menggunakan atribut karyawan seperti usia, masa kerja, kinerja, kehadiran, dan gaji.\"},{\"question\":\"Apa keunggulan Decision Tree dalam konteks penelitian ini?\",\"answer\":\"Decision Tree dipilih karena mudah diinterpretasikan serta efektif menangani data kategorikal dan numerik, sehingga cocok untuk pengambilan keputusan manajerial.\"}]","Penggunaan Machine Learning dalam Sistem Informasi SDM untuk Prediksi Turnover Karyawan - Algoritma Decision Tree | PDF",12]