Machine learning for fundraising network development in Indonesian educational and social foundations

Mulyono, Mulyono ORCID: https://orcid.org/0000-0002-4509-7390 (2026) Machine learning for fundraising network development in Indonesian educational and social foundations. International Journal of Research and Innovation in Social Science (IJRISS), 10 (1). pp. 6302-6328. ISSN |2454–6186

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Abstract

This study explores the application of machine learning (ML) in developing fundraising networks for educational and social foundations in Indonesia, using Yayasan Pendidikan Sosial Dan Dakwah Ulul Albab as a case study. Operating in Malang City and Ponorogo Regency with eight programs requiring approximately IDR 815 million annually, the foundation faces persistent fundraising challenges. Employing a mixed-method approach, we developed an ML-based donor prediction model using Random Forest, Gradient Boosting, and Neural Network algorithms, simulated with synthetic data (n=5,000) representing Indonesian donor characteristics. Results demonstrate 87.3% accuracy in donor propensity prediction and 82.6% in donation amount forecasting. Qualitative analysis through stakeholder interviews (n=15) revealed implementation barriers including digital literacy gaps and data infrastructure limitations. The proposed ML-Integrated Fundraising Framework (ML-IFF) combines predictive analytics with culturally adapted engagement strategies, projecting 45-60% improvement in fundraising efficiency. This research contributes a contextual ML application framework for Indonesian nonprofit organizations, addressing the intersection of technological innovation and social sector sustainability in emerging markets.

Item Type:	Journal Article
Keywords:	machine learning; fundraising networks; nonprofit organizations; donor prediction; educational foundations
Subjects:	13 EDUCATION > 1301 Education Systems > 130108 Technical, Further and Workplace Education
Divisions:	Faculty of Tarbiyah and Teaching Training > Department of Islamic Education Management
Depositing User:	Dr. H. Mulyono, MA.
Date Deposited:	23 Sep 2026 08:26

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