[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119647-id":3,"doc-seo-119647-113":31,"detail-sidebar-cat-0-id-113":84},{"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},119647,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",52,"Teknologi","Pengembangan Aplikasi Prediksi Masa Tunggu Alumni Menggunakan Machine Learning Berbasis Streamlit","Penelitian ini mengembangkan aplikasi prediksi masa tunggu kerja alumni dengan memanfaatkan algoritma machine learning serta antarmuka berbasis Streamlit. Masa tunggu kerja diperlakukan sebagai indikator penting kualitas lulusan, dianalisis melalui tahapan preprocessing, pemilihan fitur relevan seperti IPK, lama studi, program studi, pengalaman kerja/magang, dan aktivitas organisasi, kemudian dimodelkan menggunakan algoritma klasifikasi. Hasil pemodelan diintegrasikan ke aplikasi interaktif yang mudah digunakan dan dapat diakses secara online. Aplikasi ditujukan untuk membantu evaluasi kinerja lulusan dan memberi gambaran prediksi masa tunggu bagi mahasiswa aktif.","Pengembangan Aplikasi Prediksi Masa Tunggu Alumni Menggunakan  \nMachine Learning Berbasis Streamlit  \nAde Rahmat  \nPoliteknik Kepribadian  \n[e-mail: ](e-mail: Aderahmat.Kepribadian@gmail.com)[Aderahmat.Kepribadian@gmail.com](e-mail: Aderahmat.Kepribadian@gmail.com)  \nAbstrak  \nPenelitian ini membahas pengembangan aplikasi prediksi masa tunggu kerja alumni denganmemanfaatkan algoritma machine learning dan antarmuka berbasis streamlit. Masa tunggu kerjamerupakan salah satu indikator penting dalam menilai kualitas lulusan sebuah perguruan tinggi, dan dilakukan melalui tahapan preprocessing, pemilihan fitur yang relevan (seperti IPK, lama studi, Program Studi, Pengalaman kerja/magang, dan aktivitas organisasi) sehingga pemodelan machine learning menggunakan algoritma klasifikasi. Hasil pemodelan kemudian diintergrasikan ke dalamaplikasi berbasis streamlit yang bersifat interaktif, mudah digunakan dan dapat diakses secara online. Aplikasi ini diharapkan dapat membantu pihak perguruan tinggi Universitas XYZ dalam melakukan evaluasi kinerja lulusan, dan dapat memberikan gambaran prediksi masa tunggu kerjabagi mahasiswa yang masih aktif.  \nKata kunci: Masa Tunggu Alumni, Prediksi, Machine Learning, Klasifikasi, Streamlit.  \nAbstract  \nThis research discusses the development of an application for predicting alumni job waiting periods using machine learning algorithms and a Streamlit-based interface. Job waiting periods are a key indicator in assessing the quality of university graduates. This is achieved through preprocessing, selecting relevant features (such as GPA, length of study, study program, work/internship experience, and organizational activities), and then using machine learning modeling using a classification algorithm. The modeling results are then integrated into a Streamlit-based application that is interactive, easy to use, and accessible online. This application is expected to assist XYZ University in evaluating graduate performance and provide an overview of job waiting periods for active students.  \nKeywords: Alumni Waiting Period, Prediction, Machine Learning, Classification, Streamlit..  \nPENDAHULUAN  \nPendidikan perguruan tinggi Universitas XYZ memiliki peran startegis dalam menyiapkan sumber daya manusia yang berkualitas dan berdaya saing di dunia kerja. [1] Salah satu indikator penting dalam menilai kualitas lulusan sautu perguruan tinggi Universitas XYZ adalah masa tunggu kerja alumni, yaitu rentang waktu yang dibutuhkan seorang lulusan untuk memperoleh pekerjaan pertama setelah menyelesaikan studi. Masa tunggu kerja yang relatif singkat [2]mencerminkan keterkaitan antara kurikulum dengan kebutuhan industri, kesiapan kompetensi lulusan, serta efektivitas sistem penyaluran tenaga kerja. Sebaliknya masa tunggu yang panjangdapat menjadi sinyal adanya ketidaksesuaian antara kompetensi lulusan dengan tuntutan pasakerja. Direa digital saat ini, pemenfaatan data alumni menjadi penting untuk dianalisis gunamendapatkan hasil informasi yang bermanfaat dalam penyusunan kebijakan, evaluasi kurikulum, serta peningkatan layanan kampus. Data alumni dapat mencakup berbagai variabel, seperti Indeks Prestasi Kumulatif (IPK), Lama studi, program studi, pengalaman kerja atau magang, sertaketerampilan tambahan. Perkembangan Teknologi Machine Learning hadir sebagai salah satu solusi dalam memprediksi pola dari data historis untuk menghasilkan perkiraan yang lebih akurat. Dengan memanfaatkan algoritma machine learning, data alumni dapat diolah sehinggamenghasilkan model prediksi masa tunggu kerja berdasarkan indikator-indikator tertentu. Hal ini  \nakan membantu Kampus Universitas XYZ, dalam mengidentifikasi faktor-faktor yang berpengaruh terhadap masa tunggu kerja, serta memberikan intervensi yang tepat untuk meningkatkan kesiapan kerja bagi alumni. [3]  \nBerdasarkan uraian tersebut, Penelitian ini dilakukan pada tahun 2025 yang dari mana penelitian ini dikembangkan menjadi suatu aplikasi Tracer Study menggunakan Machnie Learning yan","cbCaiqIwES6DqDKy","https://ap.wps.com/l/cbCaiqIwES6DqDKy","pdf",823696,4,1,9,"Indonesian","id",113,"# Pendahuluan\n## Peran masa tunggu kerja alumni\n## Pemanfaatan data alumni dan machine learning\n## Tujuan penelitian\n# Metode\n## Metode penelitian (Research and Development)\n## Teknik pengumpulan data dan variabel\n## Prapemrosesan data","[{\"question\":\"Bagaimana hasil pemodelan diintegrasikan ke dalam aplikasi?\",\"answer\":\"Hasil pemodelan klasifikasi diintegrasikan ke aplikasi berbasis Streamlit agar interaktif, mudah digunakan, dan dapat diakses online untuk mendukung analisis masa tunggu kerja alumni.\"}]","Pengembangan Aplikasi Prediksi Masa Tunggu Alumni Menggunakan Machine Learning Berbasis Streamlit | PDF",1785725458,14,{"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":79,"head_meta":81,"extra_data":83,"updated_unix":29},"development-of-an-alumni-job-waiting-period-prediction-application-using-streamlit-based-machine-learning","",{"@graph":37,"@context":78},[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/teknologi/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/id/document/development-of-an-alumni-job-waiting-period-prediction-application-using-streamlit-based-machine-learning/119647/",{"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","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Bagaimana hasil pemodelan diintegrasikan ke dalam aplikasi?","Question",{"text":76,"@type":77},"Hasil pemodelan klasifikasi diintegrasikan ke aplikasi berbasis Streamlit agar interaktif, mudah digunakan, dan dapat diakses online untuk mendukung analisis masa tunggu kerja alumni.","Answer","https://schema.org",{"og:url":53,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":85},[86,91,95,99,103,107,111,115,117,121,125],{"id":87,"doc_module":4,"doc_module_name":47,"category_name":88,"show_sort_weight":89,"slug":90},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":92,"doc_module":4,"doc_module_name":47,"category_name":93,"show_sort_weight":89,"slug":94},48,"Cerita & Novel","story-novel",{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":89,"slug":98},56,"Gaya Hidup","lifestyle",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":89,"slug":102},51,"Komik","comic",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":89,"slug":106},53,"Layanan Kesehatan","healthcare",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":89,"slug":110},54,"Penelitian & Laporan","research-report",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":89,"slug":114},49,"Sastra","literature",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":89,"slug":116},"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":89,"slug":120},50,"Ujian","exam",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":89,"slug":124},57,"Umum","general",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":4,"slug":128},181,"Formulir","formulir"]