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All Journal Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) ADI Journal on Recent Innovation (AJRI) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Journal of Computer Science and Technology Application
Rizky, Agung
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Leveraging Big Data Analytics to Strategically Expand Digital Microcredit Access for MSMEs Rizky, Agung; Ramaditya, Muhammad; Kamal, Abdullah Arif
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1325

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a pivotal role in driving economic development and job creation, especially in emerging economies. However, limited access to formal credit remains a persistent challenge due to the reliance on conventional financial assessments that often exclude MSMEs with informal or incomplete financial histories. This study aims to investigate how big data analytics can be effectively leveraged to strategically expand digital microcredit access for MSMEs, offering more inclusive and accurate credit evaluation models. The research adopts a qualitative descriptive methodology, incorporating a comprehensive literature review and multiple case studies of fintech platforms that utilize alternative data sources such as e commerce transactions, mobile phone activity, utility bill payments, and social media engagement to construct alternative credit scoring systems. The findings indicate that big data enables improved risk profiling, faster loan processing, and wider financial inclusion by reaching unbanked and underbanked MSMEs. Additionally, the integration of machine learning algorithms in analyzing real time behavioral data enhances decision making precision and operational efficiency in digital lending. However, the study also raises critical issues regarding data privacy, ethical use, and transparency in automated credit decisions. In conclusion, the use of big data analytics offers transformative potential to reshape digital microcredit services, empowering MSMEs through accessible, scalable, and intelligent financial solutions that align with broader goals of sustainable economic inclusion and digital transformation.
A Data Driven Information System for Cybersecurity Vulnerability Management Aini, Qurotul; Rizky, Agung; Rusdian, Suca; Aulia, Azwani; Erica, Archa
APTISI Transactions on Management (ATM) Vol 10 No 1 (2026): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/f3yjz324

Abstract

The rapid growth of digital infrastructures has amplified cybersecurity vulnerabilities, challenging organizations to manage risks effectively. Traditional vulnerability assessment methods, such as static scoring systems, often overlook dynamic threat information, leading to suboptimal prioritization. This study addresses the gap in existing vulnerability management approaches by introducing a data-driven framework that combines internal system data, public vulnerability databases, and external threat intelligence using predictive analytics. The proposed decision support information system employs machine learning as an analytical component to estimate the likelihood of vulnerability exploitation and support vulnerability prioritization decisions. The novelty of this approach lies in its ability to prioritize vulnerabilities not only based on technical severity but also considering the context of real-world threat activity. When benchmarked against conventional methods, this approach demonstrates superior performance in identifying exploitable vulnerabilities, improving accuracy and recall, thus optimizing resource allocation. By adopting a proactive, risk-based strategy, the framework prioritizes the most critical vulnerabilities in complex IT environments. The results highlight the potential of predictive models in enhancing cybersecurity management and supporting sustainable infrastructure, driving a shift toward more efficient, data-driven decision-making.  
Co-Authors Ahmad Gunawan Amsyar, Izwan Ardiansyah, Zulfadli Arif Andika Asri asri Audiah, Sipah Aulia Lindzani, Nur Aulia, Azwani Bagong Suyanto Beldiq, Eiser Aaron Cristhopher, Ethan Dyatmika, Sutama Wisnu Ekawaty, Anita Erica, Archa Harris, Ethan Hua, Chua Toh Kamal, Abdullah Arif Komara, Maulana Arif Madani, Muchlisina Maria Daeli, Ora Plane Mkhize, Thabo Mochamad Heru Riza Chakim Ndlovu, Zinhle Ninda Lutfiani Pasha, Lukita Qurotul Aini Ramadan, Ahmad Ridwan Arifin Rodriguez, Marta Santiago Ikhsan, Ramiro Setijaningrum, Erna Suca Rusdian Sukendri, Nengah Sumliyah, Sumliyah Tanjung, Azrul Tantri Wenny Sitanggang Untung Rahardja Utami, Rahayu Tri Yul Ifda Tanjung, Yul Ifda Zainarthur, Henry Zaki Firli, Muhammad