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p-Index From 2021 - 2026
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This Author published in this journals
All Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) International Transactions on Artificial Intelligence (ITALIC)
Dariari Robert
EESP.IO Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science

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The Role of Digital Transformation in Business Model Innovation and Organizational Performance Nurbaity Nurbaity; Riski Wahdini; Windy Septia; Dariari Robert
ADI Bisnis Digital Interdisiplin Jurnal Vol 7 No 1 (2026): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v7i1.1496

Abstract

This abstract is developed with a background that the rapid advancement of digital technologies has compelled organizations to pursue digital transformation as a strategic response to increasingly dynamic and complex competition, requiring sustainable business model innovation to enhance organizational performance. The Object of this study is to examine the role of digital transformation in driving business model innovation and its impact on organizational performance, with a focus on identifying key factors influencing the effectiveness of the transformation. The Method employed is a qualitative approach through a systematic literature review of internationally reputable Scopus-indexed journal articles published between 2021 and 2025, analyzed using thematic analysis to identify patterns, strategies, and relationships among digital transformation, business model innovation, and organizational performance. The Result indicates that digital transformation significantly fosters business model innovation through the utilization of digital platforms, big data, and artificial intelligence, which in turn positively impacts organizational performance, including operational efficiency, decision-making quality, and customer satisfaction. The study also finds that leadership, employee digital capabilities, and an adaptive organizational culture are key determinants of successful digital transformation. The Conclusion emphasizes that digital transformation is not merely a technological tool but an integral strategy that strengthens business model innovation and enhances organizational performance, making it a strategic necessity for companies in navigating the evolving digital economy.
A Hybrid Artificial Intelligence Framework for Predicting Crop Yield Under Environmental Uncertainty Dariari Robert; Victoriani Victoriani
International Transactions on Artificial Intelligence Vol. 5 No. 1 (2026): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v5i1.1100

Abstract

Agricultural productivity is increasingly challenged by environmental uncertainty caused by fluctuations in temperature, rainfall, humidity, and soil conditions, making accurate crop yield prediction difficult when relying solely on conventional statistical approaches. This study aims to develop a hybrid artificial intelligence framework capable of improving crop yield prediction performance under uncertain environmental conditions by integrating machine learning techniques with fuzzy modelling to better capture nonlinear relationships and ambiguous environmental patterns. The proposed framework employs a hybrid architecture combining a neural learning mechanism with fuzzy inference to process multiple environmental variables, including temperature, precipitation, soil moisture, humidity, and fertilizer usage, as predictive inputs for crop yield estimation. Experimental evaluation was conducted using agricultural datasets containing historical environmental and production records, while model performance was assessed using Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and the coefficient of determination. The results demonstrate that the hybrid framework achieved superior predictive accuracy and robustness compared with conventional machine learning models, particularly when handling noisy and uncertain environmental data. Furthermore, the incorporation of fuzzy reasoning enhanced the model's capability to represent uncertainty and improve decision-making reliability in agricultural management. The study concludes that hybrid artificial intelligence approaches provide an effective and scalable solution for supporting sustainable agriculture through more accurate and adaptive crop yield prediction under environmental uncertainty.
Co-Authors Nurbaity Nurbaity Riski Wahdini Victoriani Victoriani Windy Septia