Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Innovative and Creativity
Muhammad Rasyid Ridho
Institut Teknologi Muhammadiyah Sumatera
Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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The Role of Digital Startups in Supporting Technology-Based Economic Transformation in Indonesia: A Focus on the Digital Creative Ecosystem Pika Meri Yanti; Muhammad Rasyid Ridho; Prantiastio Prantiastio; Solina Balqis; Rafikha Anggraini; Apep Indra Saputra
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.996

Abstract

The rapid development of the digital economy has redefined economic structures and value creation models across sectors in Indonesia. This study explores the pivotal role of digital startups in supporting technology-based economic transformation, with a specific focus on the digital creative ecosystem. Employing a descriptive qualitative methodology through library research, this paper identifies how startups empower content creators, promote digital literacy, and build resilient innovation ecosystems. The findings highlight the significance of transformational leadership, dynamic capabilities, and absorptive capacity in enhancing startup sustainability. Furthermore, digital startups function not only as platforms for economic participation but also as catalysts for inclusive growth, particularly among youth and micro, small, and medium enterprises (MSMEs). Despite these opportunities, structural challenges persist, including limited funding, low digital skills, and regulatory constraints. This study proposes actionable recommendations to strengthen startup-driven innovation, improve digital education, and design inclusive policy frameworks that support a sustainable digital creative economy in Indonesia.
The The Role of Artificial Intelligence and Machine Learning in Improving the Accuracy of Cost Prediction and Budgeting : A Review of the Literature Lisa Aulia Julica; Pika Meri Yanti; Muhamad Habibullah AR; Muhammad Rasyid Ridho; Perdian Okta Jaya
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.6282

Abstract

The complexity of the contemporary business environment demands a more precise approach to cost prediction and budgeting to maintain an organizational competitive advantage. This study examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in improving the accuracy of cost prediction and budgeting through a Systematic Literature Review of ten high-quality journal articles published for the period 2020-2025. The PRISMA methodology was applied with the stages of identification of 309 articles, elimination of 102 duplicates, screening of 207 articles, evaluation of 112 full-texts, and final selection of 10 articles that met the eligibility criteria. The results of the literature synthesis revealed that deep learning models such as LSTM and ensemble methods such as XGBoost achieved superior accuracy with a MAPE of 2.88-9% and an R² score of 0.90-0.95, significantly outperforming conventional methods. The effectiveness of AI techniques is context-specific with optimal deep learning for high complexity, classical machine learning for organizations with infrastructure limitations, and regression models for transparency priorities. Implementation challenges include data quality, the black box nature of algorithms, and substantial investment requirements. Optimization strategies include a hybrid workflow approach, gradual implementation, and data governance strengthening. The integration of AI with big data analytics enables dynamic budgeting that is adaptive to market volatility, providing strategic implications for financial management practitioners in optimizing resource allocation and data-driven decision-making.
Co-Authors Apep Indra Saputra Lisa Aulia Julica Muhamad Habibullah AR Perdian Okta Jaya Pika Meri Yanti Prantiastio Prantiastio Rafikha Anggraini Solina Balqis