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All Journal Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) IAIC Transactions on Sustainable Digital Innovation (ITSDI) International Transactions on Artificial Intelligence (ITALIC)
Callula, Brigitta
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology Social Sciences

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Unlocking Organizational Potential: Assessing the Impact of Technology through SmartPLS in Advancing Management Excellence Callula, Brigitta; Aprila Yusuf, Natasya; Rahmania Az Zahra, Achani; Aaron Beldiq, Eiser
APTISI Transactions on Management (ATM) Vol 8 No 1 (2024): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i1.2195

Abstract

This study aims to explore the organizational potential in facing the impact of implementing Business Intelligence System (BIS) technology using Partial Least Squares Structural Equation Modeling (SmartPLS) as the analytical tool. We conducted research on several organizations that have adopted Business Intelligence (BIS) with the goal of enhancing managerial excellence. Data collection was carried out online, involving 150 respondents from 5 organizations in Indonesia, the majority of whom are already familiar with and have implemented BIS technology in their organizations. We evaluated the impact of this technology, particularly on organizational performance, decision-making processes, and business process optimization. This research not only synthesizes knowledge from relevant literature but also provides a holistic understanding of the impact of Business Intelligence System (BIS) technology, specifically through its success within an organizational context. The findings of this research are expected to offer a profound insight into how the adoption of BIS can transform organizational paradigms in managing information, deciding strategies, and improving operational efficiency.
A Structural Framework for Effective Time Management in Dynamic Work Environments Sana, Eirene; Jacqueline, Greisy; Nathalie, Julia; Maria, Lily; Callula, Brigitta
APTISI Transactions on Management (ATM) Vol 8 No 2 (2024): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i2.2256

Abstract

This paper presents a structural framework to enhance time management profi- ciency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The evaluation demonstrates significant improvements in time management efficiency and client satisfaction across various professional contexts.  For instance, by employing the eisenhower Matrix and Pareto Principle, project managers achieved a 20% im- provement in project completion times. The framework’s adaptability is further highlighted by a 25% reduction in project turnaround time in a marketing agency and a 30% increase in project visibility in a startup. These findings underscore the framework’s practical implementation as a holistic approach to managing time effectively and achieving long-term success. Continuous refinement, real- time feedback integration, and exploring the impact of emerging technologies are recommended for further enhancing the framework’s effectiveness. This research contributes valuable insights for organizations aiming to navigate the complexities of modern work environments.
Transformation of Beauty in Digital Fine Arts Aesthetics: An Artpreneur Perspective Wiratno, Tri Aru; Callula, Brigitta
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 2 (2024): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i2.395

Abstract

The evolving nature of beauty in digital fine arts aesthetics signifies profound shifts within contemporary social and cultural contexts. Technological advancements have enabled artists to explore new mediums, creating works that reflect and respond to current societal issues. This study examines the paradigm shift in digital fine arts, focusing on the interpretation and manifestation of beauty in digital creations. It explores the interplay between digital aesthetics and societal realities, providing insights into the transformation of beauty in the digital age. An interdisciplinary approach is utilized, incorporating textual analysis of relevant literature, participatory observation at art exhibitions and online platforms, and a comprehensive literature review. These methods trace the evolution of beauty in digital fine arts and analyze how artists incorporate visual, auditory, and interactive elements. The research demonstrates that technological progress has significantly impacted the creation and perception of beauty in fine arts. This transformation includes broader definitions of aesthetics, integrating multisensory experiences and community participation. Digital fine arts now serve as a dynamic medium that reflects diverse social, political, and cultural themes. The transformation of beauty in this field underscores the complex relationships among art, technology, and society. Digital artworks provide innovative aesthetic experiences and mirror contemporary social dynamics. Despite concerns regarding the authenticity and preservation of digital art, its role in expanding artistic expression and fostering innovation is undeniable
Recent Developments in Healthcare Through Machine Learning and Artificial Intelligence Royani, Royani; Maulina, Sondang Deri; Sugiyono, Sugiyono; Anugrah, Rio Wahyudin; Callula, Brigitta
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 6 No 1 (2024): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v6i1.680

Abstract

This research is a review of recent advancements in the utilization of Machine Learning (ML) and Artificial Intelligence (AI), emphasizing their significant developments across diverse application domains. The purpose of this study is to provide a comprehensive understanding of these technologies and their transformative potential. To achieve this, we conducted an extensive analysis of scholarly literature and case studies, focusing on key applications and recent trends in AI and ML. Our findings reveal critical advancements, particularly in sectors such as business, healthcare, and automation, showcasing the profound impact of these technologies on innovation and operational efficiency. The review also highlights persistent challenges, including ethical concerns, data privacy, and infrastructure requirements. These insights are intended to assist stakeholders in identifying opportunities for the effective implementation and future development of AI and ML applications, ensuring their continued contribution to technological progress.
Life Cycle Assessment of Silicon Photovoltaics and Their Environmental Impacts Aziz, Lukmanul Hakim; Callula, Brigitta; Rozi, Achmad; Madani, Muchlisina
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.941

Abstract

The rapid expansion of silicon based Photovoltaic (PV) technologies continues to drive the global shift toward sustainable energy systems. However, the environmental implications across the full life cycle of PV modules particularly those associated with upstream silicon purification routes remain insufficiently examined. This study provides a comprehensive assessment of the environmental and process level impacts of Metallurgical Grade Silicon (MGS) and Upgraded Metallurgical Grade Silicon (UMGS), covering extraction, manufacturing, operation, and end of life stages. A process oriented Life Cycle Assessment (LCA) is conducted to analyze variations in carbon intensity, hazardous material use, and energy demand, complemented by comparative evaluations of monocrystalline and polycrystalline module production pathways. To enhance analytical precision, this study incorporates an AI-assisted predictive modeling framework using supervised machine learning to estimate Global Warming Potential (GWP) and identify key factors influencing emission variability. The AI-enhanced model reveals that electricity mix and purification route exert the strongest influence on GWP, and scenario simulations demonstrate that UMGS based processes can reduce upstream emissions by up to 89% under favorable energy conditions. Additionally, the study highlights future challenges related to increasing PV waste volumes between 2025 and 2030 and the need for improved recycling infrastructures. Overall, the integration of AI-based prediction with conventional LCA offers a more dynamic and adaptive evaluation of PV sustainability performance. The findings underscore the importance of renewable powered manufacturing, early adoption of low-energy purification technologies, and policy support to achieve long-term environmental and socio-economic benefits.
Co-Authors Aaron Beldiq, Eiser Anugrah, Rio Wahyudin Aprila Yusuf, Natasya Aziz, Lukmanul Hakim Jacqueline, Greisy Madani, Muchlisina Maria, Lily Maulina, Sondang Deri Nathalie, Julia Rahmania Az Zahra, Achani Royani Royani Rozi, Achmad Sana, Eirene Sugiyono Sugiyono Wiratno, Tri Aru