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Studi ini memetakan kebutuhan talenta digital dengan mengklasifikasikan data lowongan kerja dari Jobstreet berdasarkan tingkat keterampilan digital (digital, semi-digital, non-digital) serta menelusuri keterampilan yang paling sering muncul pada lowongan digital. Model XGBoost menghasilkan performa terbaik dengan F1-score 94,33%, sementara NER mengidentifikasi entitas keterampilan seperti komunikasi, problem solving, software, design, SQL, dan programming.",{"@graph":51,"@context":106},[52,68,89],{"@type":53,"itemListElement":54},"BreadcrumbList",[55,59,62,65],{"item":56,"name":57,"@type":58,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":60,"name":10,"@type":58,"position":61},"https://docshare.wps.com/id/template/",2,{"item":63,"name":39,"@type":58,"position":64},"https://docshare.wps.com/id/template/umum/",3,{"item":66,"name":47,"@type":58,"position":67},"https://docshare.wps.com/id/template/identifying-digital-skills-in-job-vacancy-ads-using-text-classification-and-named-entity-recognition-case-study-on-the-jobstreet-portal/236688/",4,{"url":66,"name":47,"@type":69,"image":70,"author":75,"headline":47,"publisher":78,"fileFormat":81,"inLanguage":45,"description":49,"dateModified":82,"datePublished":83,"encodingFormat":81,"isAccessibleForFree":84,"interactionStatistic":85},"DigitalDocument",{"url":71,"@type":72,"width":73,"height":74},"https://docshare.wps.com/thumbnails/identifying-digital-skills-in-job-vacancy-ads-using-text-classification-and-named-entity-recognition-case-study-on-the-jobstreet-portal/236688.png","ImageObject",442,249,{"name":76,"@type":77},"Xiajie","Person",{"url":56,"name":79,"@type":80},"DocShare","Organization","application/pdf","2026-09-21","2026-09-11",true,{"@type":86,"interactionType":87,"userInteractionCount":9},"InteractionCounter",{"@type":88},"ViewAction",{"@type":90,"mainEntity":91},"FAQPage",[92,98,102],{"name":93,"@type":94,"acceptedAnswer":95},"Mengapa adopsi teknologi berpengaruh pada dunia kerja dalam periode Januari–Maret 2025?","Question",{"text":96,"@type":97},"Adopsi teknologi (otomatisasi) dinyatakan menjadi salah satu penyebab tingginya PHK pada periode Januari–Maret 2025 berdasarkan Survei APINDO.","Answer",{"name":99,"@type":94,"acceptedAnswer":100},"Bagaimana penelitian memetakan kebutuhan talenta digital dari data lowongan kerja?",{"text":101,"@type":97},"Penelitian mengklasifikasikan data lowongan kerja dari Jobstreet berdasarkan tingkat keterampilan digital (digital, semi-digital, non-digital) lalu mengidentifikasi keterampilan yang paling sering muncul pada lowongan digital menggunakan NER.",{"name":103,"@type":94,"acceptedAnswer":104},"Apa hasil utama model XGBoost dan NER pada penelitian ini?",{"text":105,"@type":97},"XGBoost memberi performa terbaik dengan F1-score 94,33%, dan hasilnya menunjukkan dominasi lowongan non-digital sebesar 53,1%. NER berhasil mengidentifikasi keterampilan yang paling sering muncul seperti komunikasi, problem solving, software, design, SQL, dan programming.","https://schema.org",{"og:url":66,"og:type":108,"og:title":47,"og:site_name":79,"og:description":49},"article",{"robots":110,"canonical":66},"index,follow",{"doc_id":112,"site_id":44},236688,1790003685,{"code":4,"msg":5,"data":115},{"doc_id":112,"user_id":116,"nickname":76,"user_avatar":117,"doc_module":9,"category_id":38,"category_name":39,"doc_title":47,"doc_description":49,"doc_content":118,"file_id":119,"file_url":120,"file_type":121,"file_size":122,"view_count":9,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":123,"language":124,"language_code":45,"site_id":44,"html_lang":45,"table_of_contents":125,"faqs":126,"seo_title":127,"seo_description":49,"update_tm":128,"read_time":67},8814010472675,"https://avatar.qwps.com/avatar/WGlhamll","Identifikasi Keterampilan Digital dalam Iklan Lowongan Kerja Menggunakan Klasifikasi Teks dan Named Entity Recognition  \nStudi Kasus pada Portal Jobstreet  \n(Identifying Digital Skills from Job Vacancy Using Text Classification and Named Entity Recognition:  \nCase Study on Jobstreet Portal)  \nHandy Geraldy*, Rizka Amalia Farentina, Fransisca Angelina Dirk  \nBadan Pusat Statistik  \n[E-mail: ](E-mail: handy.geraldy@bps.go.id)[handy.geraldy@bps.go.id](E-mail: handy.geraldy@bps.go.id)  \nABSTRAK  \nPesatnya kemajuan Teknologi telah mendorong perubahan besar dalam dunia kerja. Survei yang dilaksanakan APINDO menyatakan bahwa adopsi teknologi menjadi salah satu penyebab tingginya PHK periode Januari-Maret 2025. Di lain sisi, McKinsey & Company menyatakan bahwa Indonesia membutuhkan 9 juta talenta digital pada tahun 2014-2030. Penelitian ini dilakukan untuk memetakan kebutuhan talenta digital melalui klasifikasi data lowongan kerja dari Jobstreet menuruttingkat keterampilan digital (digital, semi-digital, non-digital) serta mengidentifikasi keterampilan yang paling sering munculpada lowongan kerja digital. Model klasifikasi XGBoost memberikan performa terbaik dengan F1-score 94.33%, lebih baik dibandingkan SVM, regresi logistik dan random forest. Penelitian ini telah dapat memberikan gambaran mengenai klasifikasilowongan kerja menurut tingkat keterampilan digital. Hasil XGBoost telah menunjukkan bahwa lowongan kerja didominasioleh pekerjaan non-digital yakni sebesar 53,1% . Selanjutnya, model NER berhasil mengidentifikasi keterampilan padalowongan kerja digital, dengan “komunikasi”,“problem solving”,“software”,“design”,“SQL”, dan “programming” sebagaiketerampilan dengan frekuensi kemunculan paling banyak.  \nKata kunci: klasifikasi, named entity recognition, lowongan kerja, talenta digital  \nABSTRACT  \nTechnological advancements have significantly reshaped the nature of work. A survey conducted by APINDO indicates that technology adoption contributed to elevated layoff rates during January-March 2025. Meanwhile, McKinsey & Company states that Indonesia will need 9 million digital talents (2014-2030). This study maps digital talent demand by classifying job vacancies data from Jobstreet based on digital skill levels (digital, semi-digital, and non-digital) and identifying the most frequently mentioned digital skills. The XGBoost achieves the best performance with an F1-score of 94.33%, outperforming SVM, logistic regression, and random forest. The study has provided an overview of job vacancy classifications based on the level of digital skills required. The XGBoost results indicate that 53,1% of job vacancies are classified as non-digital jobs. Furthermore, the NER model successfully identified skill entities in digital job vacancies, revealed that “communication”,“problem solving”, “software”,“design”,“SQL”, and “programming” were the most frequently mentioned skills.  \nKeywords: classification, named entity recognition, job vacancy, digital talent  \nPENDAHULUAN  \nPesatnya kemajuan Teknologi Informasi dan Komunikasi (TIK) telah mendorong terjadinya dinamika dalam dunia kerja. Gempuran kemajuan teknologi membuat banyak hal dalam pekerjaan menjadi lebih mudah dilakukanmelalui otomatisasi pekerjaan. Penerapan otomatisasi perlu disertai dengan pengembangan keterampilan TIK atauketerampilan digital supaya tenaga kerja dapat bersaing di dunia kerja (McKinsey & Company, 2019) . Kementerian Ketenagakerjaan menyebutkan bahwa kebutuhan tenaga kerja berdasarkan kompetensi TIK diproyeksikan terus meningkat dari tahun 2022-2025, dengan proyeksi kebutuhan sebesar 1,9 juta tenaga kerja ditahun 2025. Laporan McKinsey & Company (2020) memperkuat hal tersebut denganpernyataan bahwa Indonesia membutuhkan 9 juta talenta di bidang digital pada tahun 2014-2030.  \nDi lain sisi, teknologi juga berdampak pada banyaknya Pemutusan Hubungan Kerja (PHK) . Salah satu penyebab tingginya angka PHK periode Januari-Maret 2025 menurut Survei yang dilaksanakan oleh Asosi","cbCaiaUS99Bwo9WX","https://ap.wps.com/l/cbCaiaUS99Bwo9WX","pdf",786771,10,"Indonesian","# Pendahuluan\n## Latar belakang perubahan dunia kerja akibat adopsi teknologi\n## Kebutuhan pemetaan talenta digital dari data lowongan kerja\n## Masalah data lowongan yang tidak terstruktur dan klasifikasi yang kurang detail\n## Kerangka klasifikasi berbasis konsep penggunaan teknologi digital","[{\"question\":\"Mengapa adopsi teknologi berpengaruh pada dunia kerja dalam periode Januari–Maret 2025?\",\"answer\":\"Adopsi teknologi (otomatisasi) dinyatakan menjadi salah satu penyebab tingginya PHK pada periode Januari–Maret 2025 berdasarkan Survei APINDO.\"},{\"question\":\"Bagaimana penelitian memetakan kebutuhan talenta digital dari data lowongan kerja?\",\"answer\":\"Penelitian mengklasifikasikan data lowongan kerja dari Jobstreet berdasarkan tingkat keterampilan digital (digital, semi-digital, non-digital) lalu mengidentifikasi keterampilan yang paling sering muncul pada lowongan digital menggunakan NER.\"},{\"question\":\"Apa hasil utama model XGBoost dan NER pada penelitian ini?\",\"answer\":\"XGBoost memberi performa terbaik dengan F1-score 94,33%, dan hasilnya menunjukkan dominasi lowongan non-digital sebesar 53,1%. NER berhasil mengidentifikasi keterampilan yang paling sering muncul seperti komunikasi, problem solving, software, design, SQL, dan programming.\"}]","Identifikasi Keterampilan Digital dalam Iklan Lowongan Kerja Menggunakan Klasifikasi Teks dan Named Entity Recognition - Studi Kasus pada Portal Jobstreet | PDF",1789107070]