[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122621-id":3,"doc-seo-122621-113":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},122621,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",54,"Penelitian & Laporan","Machine Learning untuk Membuat Model Decision Tree - Memprediksi Kesiapan Psikologi Berwirausaha","Penelitian ini bertujuan membangun model pohon keputusan (decision tree) berbasis machine learning untuk memprediksi kesiapan psikologi berwirausaha pada lulusan perguruan tinggi. Penelitian diawali dengan survei terhadap 700 mahasiswa (usia 17–25 tahun) di Riau menggunakan instrumen EPR, lalu menghasilkan 604 data valid setelah validasi. Kebutuhan utama penelitian adalah memperoleh aturan keputusan dari model terbaik untuk aplikasi konseling berbasis AI. Model dibagi menjadi pruning dan unpruning, dengan kinerja pruning lebih baik: akurasi >99% (training) dan 82,87% (percentage split), sedangkan unpruning 90,18% dan 80,38%.","MACHINE LEARNING TO CREATE DECISION TREE MODEL TO PREDICT OUTCOME OF ENTERPRENEURSHIP PSYCHOLOGICAL READINESS (EPR)  \nNesi Syafitri*1, Syarifah Farradinna2, Wella Jayanti3, Yudhi Arta4  \n1,4Teknik Informatika, Fakultas Teknik, Universitas Islam Riau, Indonesia  \n2,3Psikologi, Fakultas Psikologi, Universitas Islam Riau, Indonesia  \n[Email:](Email:1nesisyafitri@eng.uir.ac.id)[1](Email:1nesisyafitri@eng.uir.ac.id)[nesisyafitri@eng.uir.ac.id](Email:1nesisyafitri@eng.uir.ac.id), [2](2syarifah.farradinna@psy.uir.ac.id)[syarifah.farradinna@psy.uir.ac.id](2syarifah.farradinna@psy.uir.ac.id), [3](3wellajayanti17@gmail.com)[wellajayanti17@gmail.com](3wellajayanti17@gmail.com),  \n[4](4yudhiarta@eng.uir.ac.id)[yudhiarta@eng.uir.ac.id](4yudhiarta@eng.uir.ac.id)  \n(Naskah masuk: 31 Agustus 2022, Revisi: 11 Oktober 2022, Diterbitkan: 23 Maret 2023)  \nAbstract  \nThis study aims to create a decision tree model using machine learning to predict psychological readiness for entrepreneurship in college graduates. This research was conducted through several stages of research. In the early stages, a survey was conducted on 700 students from several universities in Riau aged between 17-25 years. The survey was conducted using the Entrepreneur Psychology Readiness (EPR) instrument. Furthermore, the survey data was validated and obtained 604 valid data to be used informing machine learning models The urgency of this research is tofind a number of decision rules from the best decision tree model to be used in building AI-based counseling applications in measuring entrepreneurial psychology readiness for college graduates. In this research, the decision tree model that is formed is divided into 2 models, namely: decision tree with pruning model and decision tree with unpruning. The pruning decision tree model produces 180 decision rules, while the unpruning model produces 121 decision rules. Good accuracy results are obtained in the pruned decision tree, which is above 99% in the use training set mode, and 82.87% in the percentage split mode. Meanwhile, the accuracy results on the unpruned decision tree are 90. 18% with the use training set mode test, and 80.38% in the percentage split mode. The decision tree model with pruning technique has better performance than the unpruning decision tree model.  \nKeywords: Accuracy, Decision Tree, Enterprenuer Readiness, Machine Learning, Psychological assessment  \nMACHINE LEARNING UNTUK MEMBUAT MODEL DECISION TREE GUNAMEMPREDIKSI KESIAPAN PSIKOLOGI BERWIRAUSAHA  \nAbstrak  \nPenelitian ini bertujuan untuk membuat sebuah model pohon keputusan (Decision Tree Model) dengan menggunakan machine learning untuk memprediksi kesiapan psikologi untuk berwirausaha pada lulusan perguruan tinggi. Penelitian ini dilakukan melalui beberapa tahap penelitian. Pada tahapan awal dilakukan survey terhadap 700 orang mahasiswa dari beberapa perguruan tinggi di Riau yang berusia antara 17-25 tahun. Survey dilakukan dengan menggunakan instrument Enterprenuer Psikology Readiness (EPR) . Selanjutnya data survey divalidasi dan diperoleh 604 data valid untuk digunakan dalam membentuk model machine learning. Urgensi dari penelitian ini adalah menemukan sejumlah aturan keputusan (decision rules) dari model pohon keputusan (decision tree) terbaik untuk digunakan dalam membangun aplikasi konseling berbasis AI dalam mengukur kesiapan psikologi kewirausahaan bagi lulusan perguruan tinggi. Pada penelitian ini model decision tree yang dibentuk, dibedakan dalam 2 model yaitu: decision tree dengan model pruning dan decision tree dengan unpruning. Decision tree model pruning menghasilkan 180 aturan keputusan (decision rules), sedangkan model unpruning menghasilkan 121 decision rules. Hasil akurasi yang baik diperoleh pada decision tree dengan pruned yaitu diatas 99% pada mode use training set, dan 82.87% pada mode percentage split. Sementara hasilakurasi pada decision tree unpruned yaitu 90.18% dengan pengujian mode use training set, dan 80.38% padamode ","cbCaitoVw4yDJk1e","https://ap.wps.com/l/cbCaitoVw4yDJk1e","pdf",810505,4,1,10,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang kewirausahaan dan peran faktor psikologis\n## Perkembangan instrumen dan pemanfaatan AI dalam konseling\n# Metode Penelitian\n## Survei dan instrumen EPR\n## Validasi data dan pembentukan model\n# Model dan Evaluasi\n## Decision tree dengan pruning\n## Decision tree dengan unpruning\n## Perbandingan akurasi model","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Membangun model decision tree berbasis machine learning untuk memprediksi kesiapan psikologi berwirausaha pada lulusan perguruan tinggi.\"},{\"question\":\"Bagaimana data penelitian dikumpulkan dan diproses?\",\"answer\":\"Survei dilakukan pada 700 mahasiswa di Riau menggunakan instrumen EPR, kemudian data divalidasi hingga diperoleh 604 data valid untuk membentuk model.\"},{\"question\":\"Apa perbedaan model decision tree yang digunakan?\",\"answer\":\"Penelitian membandingkan dua model: decision tree dengan pruning dan decision tree tanpa pruning (unpruning), yang menghasilkan jumlah aturan keputusan berbeda.\"},{\"question\":\"Model mana yang memiliki kinerja lebih baik dan bagaimana hasil akurasinya?\",\"answer\":\"Decision tree dengan pruning lebih baik, dengan akurasi di atas 99% pada training set dan 82,87% pada percentage split; sedangkan unpruning mencapai 90,18% dan 80,38%.\"}]","Machine Learning untuk Membuat Model Decision Tree - Memprediksi Kesiapan Psikologi Berwirausaha | PDF",1785811767,15,{"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},"machine-learning-to-build-a-decision-tree-model-predicting-entrepreneurship-psychological-readiness","",{"@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/machine-learning-to-build-a-decision-tree-model-predicting-entrepreneurship-psychological-readiness/122621/",{"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","2026-08-15","2026-08-04",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},"Membangun model decision tree berbasis machine learning untuk memprediksi kesiapan psikologi berwirausaha pada lulusan perguruan tinggi.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Bagaimana data penelitian dikumpulkan dan diproses?",{"text":81,"@type":77},"Survei dilakukan pada 700 mahasiswa di Riau menggunakan instrumen EPR, kemudian data divalidasi hingga diperoleh 604 data valid untuk membentuk model.",{"name":83,"@type":74,"acceptedAnswer":84},"Apa perbedaan model decision tree yang digunakan?",{"text":85,"@type":77},"Penelitian membandingkan dua model: decision tree dengan pruning dan decision tree tanpa pruning (unpruning), yang menghasilkan jumlah aturan keputusan berbeda.",{"name":87,"@type":74,"acceptedAnswer":88},"Model mana yang memiliki kinerja lebih baik dan bagaimana hasil akurasinya?",{"text":89,"@type":77},"Decision tree dengan pruning lebih baik, dengan akurasi di atas 99% pada training set dan 82,87% pada percentage split; sedangkan unpruning mencapai 90,18% dan 80,38%.","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"]