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Tinjauan sistematis ini mengkaji konvergensi keduanya dan menyoroti tiga pola integrasi utama: ML sebagai surrogate model untuk mempercepat simulasi MC, MC untuk mengkuantifikasi ketidakpastian pada model ML, serta generative ML untuk membangkitkan skenario stres. Hasil menunjukkan peningkatan akurasi prediktif, efisiensi komputasi, dan kapabilitas penilaian risiko ekor. Tantangannya meliputi kompleksitas model dan kebutuhan interpretabilitas. Studi menyimpulkan paradigma baru yang kuat serta merekomendasikan riset lanjutan untuk interpretabilitas dan standar validasi.",{"@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/simulation-and-intelligence-convergence-review-of-the-integration-of-monte-carlo-and-machine-learning-methods-in-risk-management/127464/",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/simulation-and-intelligence-convergence-review-of-the-integration-of-monte-carlo-and-machine-learning-methods-in-risk-management/127464.png","ImageObject",300,407,{"name":89,"@type":90},"Ophelia","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",9,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Apa tiga pola integrasi utama Monte Carlo dan machine learning dalam manajemen risiko?","Question",{"text":110,"@type":111},"Tinjauan sistematis mengidentifikasi: (1) ML sebagai surrogate model untuk mempercepat simulasi MC, (2) MC untuk mengkuantifikasi ketidakpastian dalam model ML, dan (3) generative ML models untuk pembangkitan skenario stres.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Integrasi MC dan ML memberi manfaat apa bagi penilaian risiko?",{"text":115,"@type":111},"Sinergi tersebut meningkatkan akurasi prediktif, efisiensi komputasi, serta kapasitas penilaian risiko ekor (tail risk).",{"name":117,"@type":108,"acceptedAnswer":118},"Apa tantangan utama saat mengimplementasikan integrasi MC-ML?",{"text":119,"@type":111},"Masih perlu diatasi kompleksitas model dan kebutuhan interpretabilitas agar hasil dapat dipahami serta divalidasi dengan andal.","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},127464,1785939047,{"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},962084925290,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Konvergensi simulasi dan kecerdasan: Tinjauan terhadap integrasi metode Monte Carlo dan machine learning dalam manajemen risiko  \nRatna Mustika Dewi 1*, Oktarian Wisnu Lusantono 2, Indra Wahyu Murtyanto 3  \n1 Departemen Teknik Industri, Universitas Atma Jaya Yogyakarta, Yogyakarta, Indonesia; email: [ratna.mustika@uajy.ac.id](ratna.mustika@uajy.ac.id)  \n2 Program Studi Teknik Pertambangan, Universitas Pembangunan Nasional (UPN) Veteran Yogyakarta, Yogyakarta, Indonesia  \n3 PT Studio Mineral Batubara, Yogyakarta, Indonesia  \n* Corresponding author  \nAbstrak  \nIntegrasi metode Monte Carlo (MC) dan machine learning (ML) merepresentasikan terobosan dalam manajemen risiko modern untuk mengatasi kompleksitas dan ketidakpastian dinamik. Tinjauan sistematis ini mengkaji konvergensi kedua pendekatan tersebut, mengidentifikasi tiga pola integrasiutama: (1) penggunaan ML sebagai surrogate model untuk mempercepat simulasi MC, (2) pemanfaatan MC untuk mengkuantifikasi ketidakpastian dalam model ML, dan (3) penerapan generative ML models untuk pembangkitan skenario stres. Hasil analisis menunjukkan bahwa sinergi ini mampu meningkatkan akurasi prediktif, efisiensi komputasi, dan kapasitaspenilaian risiko ekor. Meskipun demikian, tantangan implementasi seperti kompleksitas model dankebutuhan interpretabilitas masih perlu diatasi. Penelitian ini menyimpulkan bahwa integrasi MCML menawarkan paradigma baru yang powerful dan merekomendasikan pengembangan teknik interpretabilitas serta standar validasi sebagai agenda penelitian mendatang.  \nKata Kunci: Monte Carlo, manajemen risiko, machine learning  \nAbstract  \n[Simulation and intelligence convergence: A systematic review of the integration of Monte Carlo method and machine learning in risk management] The integration of Monte Carlo (MC) methods and machine learning (ML) represents a breakthrough in modern risk management for addressing dynamic complexity and uncertainty. This systematic review examines the convergence of these two approaches, identifying three main integration patterns: (1) the use of ML as a surrogate model to accelerate MC simulation,(2) the use of MC to quantify uncertainty in ML models, and  \n(3) the application of generative ML models for stress scenario generation. The analysis results indicate that this synergy can improve predictive accuracy, computational efficiency, and tail risk assessment capacity. Nevertheless, implementation challenges such as model complexity and the need for interpretability still need to be addressed. This study concludes that MC-ML integration offers a powerful new paradigm and recommends the development of interpretability techniques and validation standards as the agenda for future research.  \nKeywords: Monte Carlo, risk management, machine learning  \nReceived: 30-11-2025; Revised: 31-12-2025; Accepted: 31-12-2025  \nDOI: [https://doi.org/10.24002/jtimr.v3.i2.13337](https://doi.org/10.24002/jtimr.v3.i2.13337)  \nSaran format untuk sitasi artikel ini:  \nDewi, R. M., Lusantono. O. W., & Murtyanto, I. (2025) . Konvergensi simulasi dan kecerdasan: Tinjauan terhadap integrasi metode Monte Carlo dan machine learning dalam manajemen risiko. Jurnal Teknik Industri dan Manajemen Rekayasa, 3(2), 125-131.  \n1. Pendahuluan  \nManajemen risiko modern menghadapi paradigma baru yang ditandai dengan volatilitas tinggi, ketidakpastian kompleks, dan interdependensi global (Kaplan dan Mikes, 2012) . Peristiwa systemic risk seperti krisis finansial 2008 dan pandemi COVID-19 telah mengungkap keterbatasan model tradisional dalam menangkap nonlinearitas dan fat-tailed distributions dari distribusi risiko kontemporer (Taleb, 2025) . Kondisi ini diperparah dengandisrupsi digital di mana cyber risk dan algorithmic failure menciptakan ancaman eksistensialbaru yang bergerak dalam kecepatan tinggi (World Economic Forum, 2023) . Transformasi fundamental ini menuntut evolusi metodologi dari pendekatan statis menuju framework dinamis berbasis komputasi canggih.  \nMetode kuanti","cbCaiiFGKKdXOzKC","https://ap.wps.com/l/cbCaiiFGKKdXOzKC","pdf",425146,7,"Indonesian","# Pendahuluan\n## Risiko modern dan keterbatasan model tradisional\n## Konvergensi simulasi stokastik dan artificial intelligence\n# Pola integrasi Monte Carlo dan machine learning\n## ML sebagai surrogate untuk percepatan simulasi MC\n## MC untuk mengkuantifikasi ketidakpastian model ML\n## Generative ML untuk skenario stres\n# Tantangan implementasi dan agenda penelitian","[{\"question\":\"Apa tiga pola integrasi utama Monte Carlo dan machine learning dalam manajemen risiko?\",\"answer\":\"Tinjauan sistematis mengidentifikasi: (1) ML sebagai surrogate model untuk mempercepat simulasi MC, (2) MC untuk mengkuantifikasi ketidakpastian dalam model ML, dan (3) generative ML models untuk pembangkitan skenario stres.\"},{\"question\":\"Integrasi MC dan ML memberi manfaat apa bagi penilaian risiko?\",\"answer\":\"Sinergi tersebut meningkatkan akurasi prediktif, efisiensi komputasi, serta kapasitas penilaian risiko ekor (tail risk).\"},{\"question\":\"Apa tantangan utama saat mengimplementasikan integrasi MC-ML?\",\"answer\":\"Masih perlu diatasi kompleksitas model dan kebutuhan interpretabilitas agar hasil dapat dipahami serta divalidasi dengan andal.\"}]","Konvergensi simulasi dan kecerdasan - Tinjauan terhadap integrasi metode Monte Carlo dan machine learning dalam manajemen risiko | PDF",11]