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All Journal Nusantara Hasana Journal VISI SAKTI: Jurnal Industri Kreatif dan Inovatif
Lidia Simanjuntak
Universitas Efarina
Agriculture, Biological Sciences & Forestry Arts Humanities Economics, Econometrics & Finance Education Nursing

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PENGUATAN EKOSISTEM UMKM MELALUI KOLABORASI PEMERINTAH DAN DUNIA USAHA: MODEL KEMITRAAN DAN MANAJEMEN HUBUNGAN STAKEHOLDER Uning Heri Gagarin; Jumadiah Wardati; Lidia Simanjuntak
Nusantara Hasana Journal Vol. 4 No. 2 (2024): Nusantara Hasana Journal, July 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i2.2006

Abstract

This Community Service (PkM) activity aims to strengthen the SME ecosystem through collaboration between the government and the business sector by developing an operational partnership model and a measurable stakeholder relationship management system. Common challenges include fragmented SME support programs, limited market access and financing, inconsistent quality standards, and cross-sector coordination that lacks established working mechanisms and shared indicators. The PkM intervention is carried out through mapping of stakeholders and SME needs, designing partnership models (governance, roles, service workflows), developing joint program packages (market access, product upgrading, financing, digitalization), and establishing a coordination platform in the form of a partnership forum and a KPI dashboard. Key outputs include partnership model documents, MoU/MOU templates, collaboration SOPs, stakeholder maps, and a 12-month action plan ready for implementation.
INTEGRASI AI/ML UNTUK MITIGASI RISIKO OPERASIONAL DI PERUSAHAAN INDUSTRI BEI Uning Heri Gagarin; Jumadiah Wardati; Lidia Simanjuntak
Jurnal Industri Kreatif dan Inovatif Vol. 3 No. 1 (2025): Desain Komunikasi Visual
Publisher : Institut Teknologi dan Bisnis Kristen Bukit Pengharapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61696/visisakti.v3i1.1058

Abstract

Digital technology has introduced major disruption to operational risk management, particularly for industrial companies listed on the Indonesia Stock Exchange (BEI). The growing complexity of operations increases the difficulty of identifying and mitigating risks such as process failures, human error, outdated systems, and external disruptions—factors that can threaten business continuity and cause significant losses. This study examines how the integration of Artificial Intelligence (AI) and Machine Learning (ML) can be applied strategically to improve the accuracy, speed, and efficiency of operational risk mitigation in BEI-listed industrial firms. The main focus includes the benefits of AI/ML implementation: predictive analytics for risk identification, real-time monitoring and anomaly detection, automation of routine tasks to reduce human error, efficiency gains and cost savings, as well as improved regulatory compliance and data security. It also addresses effective implementation strategies such as starting with low-risk pilot projects, fostering cross-department collaboration and workforce training, establishing strong governance and oversight (human-in-the-loop), improving data quality and mitigating algorithmic bias, and aligning the approach with regulatory requirements and security standards. The paper further presents practical examples including predictive maintenance, sentiment analysis for reputation risk mitigation, and AI/ML-based automation for compliance reporting and auditing. Overall, the study concludes that AI/ML is not an instant solution, but a transformative approach requiring careful planning, high-quality data, integration with existing systems, and organizational readiness through training and robust governance to minimize operational risk, enhance efficiency, and maintain competitiveness.
Co-Authors Jumadiah Wardati Uning Heri Gagarin