PENGEMBANGAN SISTEM INFORMASI PREDIKTIF MENGGUNAKAN MACHINE LEARNING UNTUK MANAJEMEN RISIKO UMKM

DAPOT HUTAGALUNG, NPM 2409406013 (2026) PENGEMBANGAN SISTEM INFORMASI PREDIKTIF MENGGUNAKAN MACHINE LEARNING UNTUK MANAJEMEN RISIKO UMKM. Tugas_Akhir (Artikel) : Jurnal Sistem Informasi, Teknik Komputer dan Teknologi Pendidikan (JUSTIKPEN) Sinta 5, 5 (2). pp. 56-60. ISSN 2828-7924 (Online)

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Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran strategis dalam perekonomian nasional, namun masih menghadapi berbagai risiko seperti risiko keuangan, operasional, dan pasar yang dapat mengancam keberlangsungan usaha. Keterbatasan sumber daya dan kemampuan analisis risiko menyebabkan UMKM sulit melakukan mitigasi secara efektif. Penelitian ini bertujuan untuk mengembangkan sistem informasi prediktif berbasis machine learning yang dapat membantu UMKM dalam mengidentifikasi dan memprediksi risiko usaha secara lebih akurat. Metode yang digunakan meliputi pengumpulan data historis UMKM, pemodelan machine learning menggunakan algoritma klasifikasi dan regresi, serta evaluasi performa sistem berdasarkan tingkat akurasi, presisi, dan recall. Hasil penelitian menunjukkan bahwa sistem informasi prediktif yang dikembangkan mampu memberikan prediksi risiko yang cukup akurat dan dapat digunakan sebagai alat bantu pengambilan keputusan bagi pelaku UMKM. Dengan demikian, penerapan teknologi machine learning dalam manajemen risiko UMKM diharapkan dapat meningkatkan ketahanan dan keberlanjutan usaha. Kata kunci : Sistem Informasi Prediktif, Machine Learning, Manajemen Risiko, UMKM ======================================================================== Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in the national economy, but they still face various risks, such as financial, operational, and market risks, that can threaten their sustainability. Limited resources and risk analysis capabilities make it difficult for MSMEs to mitigate risks effectively. This research aims to develop a machine learning-based predictive information system that can help MSMEs identify and predict business risks more accurately. The methods used include collecting historical MSME data, machine learning modeling using classification and regression algorithms, and evaluating system performance based on accuracy, precision, and recall. The results show that the developed predictive information system is capable of providing fairly accurate risk predictions and can be used as a decision-making tool for MSMEs. Thus, the application of machine learning technology in MSME risk management is expected to improve business resilience and sustainability. Keywords : Predictive Information Systems, Machine Learning, Risk Management, MSMES

Item Type: Article
Uncontrolled Keywords: Sistem Informasi Prediktif, Machine Learning, Manajemen Risiko, UMKM =========================== Predictive Information Systems, Machine Learning, Risk Management, MSMES
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Divisions: Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User: Unnamed user with email repository@ulb.ac.id
Date Deposited: 01 Sep 2026 04:37
Last Modified: 01 Sep 2026 04:37
URI: http://repository.ulb.ac.id/id/eprint/2705

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