[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121393-id":3,"doc-seo-121393-113":31,"detail-sidebar-cat-0-id-113":93},{"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},121393,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",54,"Penelitian & Laporan","Tinjauan Literatur Sistematik Pendekatan Machine Learning untuk Deteksi Kecurangan Laporan Keuangan","Kecurangan laporan keuangan menjadi ancaman serius terhadap integritas dan transparansi pelaporan, terutama pada perusahaan publik. Penelitian ini menyusun tinjauan literatur sistematis atas 43 publikasi akademik (2011–2025) yang mengkaji metode klasifikasi untuk deteksi kecurangan. Metode dikelompokkan dalam empat taksonomi: statistik konvensional, supervised learning, ensemble learning, dan deep learning. Hasil menegaskan keunggulan akurasi pada Random Forest, XGBoost, dan LSTM, tetapi menyoroti tantangan komputasi tinggi serta keterbatasan interpretabilitas. Arah riset masa depan meliputi integrasi data terstruktur dan tidak terstruktur, text mining untuk data naratif, serta pengembangan sistem deteksi adaptif.","[Website : jurnal.umj.ac.id/index.php/semnastek](Website : jurnal.umj.ac.id/index.php/semnastek)  \nTinjauan Literatur Sistematik Pendekatan Machine Learning untuk Deteksi Kecurangan Laporan Keuangan  \nDewa Ferrouzi Diaz Zhah Pahlevi1, Isti Surjandari1*  \n1Department of Industrial Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia  \nAbstrak  \nKecurangan laporan keuangan merupakan ancaman serius terhadap integritas dan  \ntransparansi sistem pelaporan keuangan perusahaan, khususnya bagi perusahaan  \npublik. Untuk mengatasi tantangan ini, pendekatan berbasis machine learning telah  \nbanyak dikembangkan sebagai alat bantu dalam mengidentifikasi pola-pola  \nkecurangan secara otomatis dan akurat. Penelitian ini menyajikan tinjauan literatur  \nsistematis terhadap berbagai metode klasifikasi yang digunakan dalam deteksi  \nkecurangan laporan keuangan, berdasarkan 43 publikasi akademik dari tahun 2011  \nhingga 2025. Metode klasifikasi dikelompokkan ke dalam empat taksonomi utama:  \nmetode statistik konvensional, algoritma supervised learning, metode ensemble  \nlearning, dan metode deep learning. Studi ini juga membahas berbagai sumber data  \nyang digunakan, faktor-faktor yang memengaruhi hasil klasifikasi, serta kelebihan  \ndan kekurangan masing-masing pendekatan. Hasil tinjauan menunjukkan bahwa  \nmeskipun metode statistik konvensional seperti regresi logistik masih sering  \ndigunakan karena kemudahan interpretasinya, metode berbasis machine learning  \nseperti Random Forest, XGBoost, dan LSTM memberikan performa yang lebih  \nunggul dalam hal akurasi. Namun demikian, tantangan seperti kebutuhan komputasi  \nyang tinggi dan keterbatasan interpretabilitas tetap menjadi perhatian. Studi ini juga  \nmengidentifikasi arah penelitian masa depan, termasuk penggabungan data terstruktur  \ndan tidak terstruktur, penggunaan teknik text mining pada data naratif, serta  \npengembangan sistem deteksi adaptif. Dengan semakin berkembangnya teknologi  \ndata dan kecerdasan buatan, pendekatan berbasis machine learning memiliki potensi  \nbesar untuk meningkatkan efektivitas dan efisiensi dalam mendeteksi kecurangan  \nlaporan keuangan.  \nKata kunci: Deteksi Kecurangan, Laporan Keuangan, Machine learning, Ensemble  \nlearning, Deep learning  \nAbstract  \nFinancial statement fraud poses a serious threat to the integrity and transparency of  \ncorporate financial reporting systems, particularly for publicly listed companies. To  \naddress this challenge, machine learning-based approaches have been widely  \ndeveloped as tools to automatically and accurately identify fraudulent patterns. This  \nstudy presents a systematic literature review of various classification methods used in  \nfinancial statement fraud detection, based on 43 academic publications from 2011 to  \n2025. Classification methods are categorized into four main taxonomies:  \nconventional statistical methods, supervised learning algorithms, ensemble learning  \nmethods, and deep learning approaches. The study also discusses the different data  \nsources used, factors influencing classification performance, as well as the strengths  \nand limitations of each approach. The findings indicate that while conventional  \nstatistical methods such as logistic regression are still commonly used due to their  \nease of interpretation, machine learning-based methods such as Random Forest,  \nXGBoost, and LSTM demonstrate superior performance in terms of accuracy.  \nNevertheless, challenges such as high computational requirements and limited  \ninterpretability remain concerns. This study also identifies future research directions,  \nSeminar Nasional Sains dan Teknologi 2025 1  \nFakultas Teknik Universitas Muhammadiyah Jakarta, 28 Mei 2025  \n[Website : jurnal.umj.ac.id/index.php/semnastek](Website : jurnal.umj.ac.id/index.php/semnastek)  \nincluding the integration of structured and unstructured data, the application of text  \nmining techniques to narrative data, and the development of adaptive","cbCaiiRv3OR8MTMq","https://ap.wps.com/l/cbCaiiRv3OR8MTMq","pdf",240132,5,1,11,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang dan urgensi deteksi kecurangan laporan keuangan\n## Regulasi dan konteks pasar modal\n# Tinjauan Literatur Sistematis\n## Ruang lingkup publikasi (2011–2025)\n## Taksonomi metode klasifikasi\n# Metode dan Hasil Sintesis\n## Statistik konvensional\n## Supervised learning dan ensemble learning\n## Deep learning\n# Perbandingan, Kelebihan, dan Kekurangan\n## Interpretabilitas dan kebutuhan komputasi\n# Arah Penelitian Masa Depan\n## Data terstruktur vs tidak terstruktur\n## Text mining pada data naratif\n## Sistem deteksi adaptif","[{\"question\":\"Bagaimana penelitian ini mengelompokkan metode klasifikasi untuk deteksi kecurangan laporan keuangan?\",\"answer\":\"Metode dikelompokkan ke dalam empat taksonomi utama: statistik konvensional, supervised learning, ensemble learning, dan deep learning.\"},{\"question\":\"Mengapa metode statistik konvensional seperti regresi logistik tetap banyak digunakan?\",\"answer\":\"Regresi logistik masih sering dipakai karena kemudahan interpretasinya dalam analisis hasil klasifikasi.\"},{\"question\":\"Apa saja tantangan utama yang disoroti dari pendekatan berbasis machine learning?\",\"answer\":\"Tantangan utama mencakup kebutuhan komputasi yang tinggi dan keterbatasan interpretabilitas model.\"}]","Tinjauan Literatur Sistematik Pendekatan Machine Learning untuk Deteksi Kecurangan Laporan Keuangan | PDF",1785735472,17,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"systematic-literature-review-of-machine-learning-approaches-for-financial-statement-fraud-detection","",{"@graph":37,"@context":87},[38,55,70],{"@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":54},"https://docshare.wps.com/id/document/systematic-literature-review-of-machine-learning-approaches-for-financial-statement-fraud-detection/121393/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-17","2026-08-03",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Bagaimana penelitian ini mengelompokkan metode klasifikasi untuk deteksi kecurangan laporan keuangan?","Question",{"text":77,"@type":78},"Metode dikelompokkan ke dalam empat taksonomi utama: statistik konvensional, supervised learning, ensemble learning, dan deep learning.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Mengapa metode statistik konvensional seperti regresi logistik tetap banyak digunakan?",{"text":82,"@type":78},"Regresi logistik masih sering dipakai karena kemudahan interpretasinya dalam analisis hasil klasifikasi.",{"name":84,"@type":75,"acceptedAnswer":85},"Apa saja tantangan utama yang disoroti dari pendekatan berbasis machine learning?",{"text":86,"@type":78},"Tantangan utama mencakup kebutuhan komputasi yang tinggi dan keterbatasan interpretabilitas model.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]