[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-128265-113":53,"doc-detail-128265-id":128},{"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":121,"head_meta":123,"extra_data":125,"updated_unix":127},113,"id","underpriced-issuers-development-of-a-classification-model-using-machine-learning-algorithms","Underpricing Emiten - Pengembangan Model Klasifikasi Menggunakan Algoritma Machine Learning","","Penelitian ini mengembangkan model klasifikasi untuk mengidentifikasi emiten IPO yang mengalami underpricing agar membantu pengambilan keputusan investor. Penelitian menggunakan metodologi CRISP-DM dengan sampel 209 emiten IPO non-perbankan dari platform OJK E-IPO hingga 31 Desember 2024. Mengingat ketidakseimbangan kelas (161 underpriced dan 48 tidak underpriced), diterapkan SMOTE. Model memakai sembilan fitur, membandingkan tujuh algoritma klasifikasi berdasarkan akurasi, precision, sensitivity, recall, F1-score, dan AUC, dan Random Forest memperoleh kinerja terbaik (89,2% akurasi; Macro F1 88,9%; AUC 0,946).",{"@graph":63,"@context":120},[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/underpriced-issuers-development-of-a-classification-model-using-machine-learning-algorithms/128265/",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/underpriced-issuers-development-of-a-classification-model-using-machine-learning-algorithms/128265.png","ImageObject",300,407,{"name":89,"@type":90},"Seraphina","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-19","2026-08-05",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",10,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Apa tujuan utama penelitian ini?","Question",{"text":110,"@type":111},"Mengembangkan model klasifikasi untuk mengidentifikasi emiten IPO yang underpriced sehingga dapat mendukung keputusan investasi.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana penelitian menangani masalah ketidakseimbangan kelas?",{"text":115,"@type":111},"Penelitian menggunakan SMOTE karena data terdiri dari kelas underpriced yang lebih banyak dibanding tidak underpriced.",{"name":117,"@type":108,"acceptedAnswer":118},"Algoritma klasifikasi apa yang menghasilkan kinerja terbaik dan metrik utamanya?",{"text":119,"@type":111},"Random Forest menunjukkan performa terbaik dengan akurasi 89,2%, Macro Average F1-score 88,9%, dan AUC 0,946.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},128265,1785946322,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":137,"language":138,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":61,"update_tm":127,"read_time":142},2336475104957,"https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136","Development of a Classification Model for Underpriced Issuers Using  \nMachine Learning Algorithms  \nDefitra Hidayatullah 1*, Ihsan Jatnika 1*  \n* Manajemen Sistem Informasi, Universitas Gunadarma  \n[defitra.hidayatullah@yahoo.com](defitra.hidayatullah@yahoo.com1)[1](defitra.hidayatullah@yahoo.com1), [ihsan@staff.gunadarma.ac.id](ihsan@staff.gunadarma.ac.id2)[2](ihsan@staff.gunadarma.ac.id2)  \n\n| Article history:\u003Cbr>Received 2025-06-06 Revised 2025-07-07 Accepted 2025-07-19 | This study develops a classification model to identify underpriced Initial Public Offering (IPO) issuers so that it can help investor decision-making. Using the CRISP-DM methodology, this research uses a sample of 209 non-banking IPO issuers of the OJK E-IPO platform (since establishment until December 31, 2024). In addressing the problem of class imbalance (161 underpriced and 48 not underpriced), SMOTE was used. The model utilizes nine features: Year-on-Year, IHSG, IPO price, ratio of shares issued, age of the firm, size of the firm, sales growth, Return on Assets (ROA), Debt to Equity Ratio (DER), and Asset Turnover Ratio (ATO) . Seven classifier algorithms were compared based on accuracy, precision, sensitivity, recall, F1-score, and AUC. Random Forest had the best performance with 89.2% accuracy, 88.9% Macro Average F1-score, and an AUC of 0.946. The findings suggest that the Random Forest model accurately identifies underpriced IPO issuers as a good investment decision-making tool. This research demonstrates that machine learning concepts can be implemented to classify underpriced issuers in Indonesia, continuing previous studies that contributed to understanding the correlation and significance of certain variables to underpricing.\u003Cbr>\u003Cbr>This is an open access article under the CC–BY-SA license. |\n| --- | --- |\n| Keyword:\u003Cbr>Initial Public Offering, Underpricing, CRISP-DM, Machine Learning, Confusion Matrix. |  |\n\nArticle Info ABSTRACT  \nI. PENDAHULUAN  \nStatistik pasar modal menunjukkan jumlah investor pasar modal semakin meningkat dari tahun ke tahun. Hal ini menunjukkan antusiasme masyarakat dalam berinvestasi, khususnya di pasar modal Indonesia. Investasi dapat diartikansebagai aktivitas menunda konsumsi saat ini untuk dialokasikan pada aset produktifdalam jangka waktu tertentu [1] . Sedangkan pasar modal dapat diartikan sebagai tempat berbagai instrumen jangka panjang yang dapat ditransaksikan [2] . Salah satu pilihan investasi di pasar modal adalah pembelian saham IPO. IPO atau Initial Public Offering merupakan serangkaian proses yang harus dilalui perusahaanyang ingin menjual sahamnya kepada masyarakat sebelumsaham tersebut dapat ditransaksikan di bursa saham. Menurut Sugiyanto et al, keputusan untuk membeli saham IPOmemiliki risiko yang tergolong tinggi [3] . Salah satu informasi yang cukup penting dalam membeli saham IPO adalah informasi underpricing emiten. Underpricing merupakan selisih harga antara harga IPO dan harga listing.  \nHarga IPO ditentukan oleh kesepakatan emiten dan underwriter. Sedangkan harga listing terbentuk dari mekanisme pasar [4].  \nUnderpricing merupakan fenomena yang umum terjadipada saat perusahaan menawarkan sahamnya melalui IPO. Suatu saham dikatakan underprice apabila memiliki harga IPO yang lebih rendah daripada harga sebenarnya dan dijuallebih tinggi pada saat hari pertama listing di bursa [5] . Emitendengan harga IPO yang lebih rendah daripada harga aslinyacenderung menarik bagi investor. Hal ini dikarenakan investor berpotensi mendapatkan initial return berupa selisih harga beli saham IPO dan harga jual di bursa [6]. Underpricing tidak hanya penting bagi investor, melainkan juga bagi underwriter. Minat yang tinggi terhadap suatusaham IPO dapat menjadi tanda positif untuk memastikansaham yang ditawarkan terjual habis dan underwriter tidak menanggung saham yang tidak terjual [7] . Menurut Maylaniet al, terdapat dua faktor yang secara umum berpengaruh terhadap tingkat underpricing suatu emiten pada saat IPO, yai","cbCais4e4byR6Yrc","https://ap.wps.com/l/cbCais4e4byR6Yrc","pdf",1027979,7,"Indonesian","# Pendahuluan\n## Investasi dan Pasar Modal\n## Konsep IPO dan Underpricing\n## Faktor yang Mempengaruhi Underpricing\n## Studi Terdahulu\n# Metodologi Penelitian\n## CRISP-DM\n## Sampel Penelitian\n## Penanganan Ketidakseimbangan Data\n## Fitur yang Digunakan\n## Algoritma Klasifikasi yang Dibandingkan\n# Hasil dan Pembahasan\n## Perbandingan Kinerja Model\n## Temuan Utama Model Terbaik","[{\"question\":\"Apa tujuan utama penelitian ini?\",\"answer\":\"Mengembangkan model klasifikasi untuk mengidentifikasi emiten IPO yang underpriced sehingga dapat mendukung keputusan investasi.\"},{\"question\":\"Bagaimana penelitian menangani masalah ketidakseimbangan kelas?\",\"answer\":\"Penelitian menggunakan SMOTE karena data terdiri dari kelas underpriced yang lebih banyak dibanding tidak underpriced.\"},{\"question\":\"Algoritma klasifikasi apa yang menghasilkan kinerja terbaik dan metrik utamanya?\",\"answer\":\"Random Forest menunjukkan performa terbaik dengan akurasi 89,2%, Macro Average F1-score 88,9%, dan AUC 0,946.\"}]","Underpricing Emiten - Pengembangan Model Klasifikasi Menggunakan Algoritma Machine Learning | PDF",11]