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Application of Building Workers Services in Facing Industrial Revolution 4.0 Amsyar, Izwan; Cristhopher, Ethan; Rahardja, Untung; Lutfiani, Ninda; Rizky, Agung
Aptisi Transactions On Technopreneurship (ATT) Vol 3 No 1 (2021): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v3i1.117

Abstract

   The services of skilled construction workers are only known around them in the sense that they are not yet widespread in the community, therefore an application is needed that can help construction workers services and can make it easier for the wider community to find construction workers services. The purpose of this research is to build applications as building laborer services based on android and to make construction workers able to compete in this era of industrial revolution 4.0. The method used in this research is the waterfall methodology, where in this methodology each stage of the research is carried out sequentially, starting from the stages of analysis, design, writing program code, testing, and maintenance. This application uses Adobe Illustrator, AirDroid, Android Studio and Sublime Text, but the problem in this research is that it is very difficult to find experienced and professional construction workers and most of these construction workers do not understand the digital world so it is difficult to develop this research in old school community (do not understand the world of technology today).
Digital Transformation Strategies for Effective Business Management in SMEs: A SmartPLS Approach Ekawaty, Anita; Rizky, Agung; Ramadan, Ahmad; Ndlovu, Zinhle
APTISI Transactions on Management (ATM) Vol 9 No 1 (2025): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v9i1.2410

Abstract

Small and Medium Enterprises (SMEs) in Tangerang, Indonesia, significantly contribute to economic development but face barriers in adopting digital technologies, such as limited resources, lack of digital skills, and organizational resistance. Digital transformation, encompassing cloud computing, digital marketing, automation, and data analytics, is increasingly recognized as essential for business growth and efficiency.  The primary objective of this study is to examine the impact of digital transformation strategies on operational efficiency, customer engagement, and overall performance in SMEs. Using a quantitative research approach, Partial Least Squares Structural Equation Modeling (SmartPLS) was applied to analyze data from 150 SMEs in Tangerang. The research measured the relationship between digital transformation strategies and key performance indicators. The results indicate that cloud computing and data analytics significantly improve operational efficiency by streamlining processes and enabling data-driven decision-making. Meanwhile, digital marketing and automation enhance customer engagement and market competitiveness, with SMEs showing stronger customer relationships and improved market positions. This study highlights the importance of adopting digital transformation to enhance SME business management and achieve sustainable growth. It offers practical insights for managers and policymakers and encourages future research on the long-term effects of digital transformation on SME resilience and innovation.
Innovation Behavior Research: Global Trends and Emerging Themes in Entrepreneurial Business Practices Chakim, Mochamad Heru Riza; Utami, Rahayu Tri; Sitanggang, Tantri Wenny; Tanjung, Azrul; Rizky, Agung; Beldiq, Eiser Aaron
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.476

Abstract

Innovation behavior research has emerged as a critical field due to its role in enhancing business competitiveness and organizational performance. This bibliometric analysis aims to provide a comprehensive understanding of global trends and emerging themes in innovation behavior research from 2015 to 2024. Using the Dimensions database, 11,798 articles were identified and analyzed with VOSviewer software. The results reveal that Australia, China, the United Kingdom, and the United States are the top contributors to this research, with strong international collaboration networks highlighting its global nature. Brock University, Jiangsu University, and Erasmus University Rotterdam stand out for their collaborative networks and strategic research directions. In the author-based analysis, research efforts primarily focus on the relationship between entrepreneurial orientation, transformational leadership, and innovation behavior. Frontiers in Psychology and Sustainability emerge as influential journals, covering topics such as digital transformation, organizational behavior, and sustainable business practices. Groundbreaking studies have developed frameworks linking innovation behavior with knowledge sharing, digital transformation, and conflict management. Keyword co-occurrence analysis highlights "innovation", "performance", and "leadership" as frequently occurring terms, reflecting a core focus on understanding how innovation directly impacts organizational outcomes and the importance of transformational leadership in fostering innovation behavior. The findings provide valuable insights into global innovation behavior research, identifying emerging interdisciplinary themes like digital transformation and sustainability. Future research should explore global collaboration networks, interdisciplinary approaches, and the influence of leadership styles on innovation behavior.
Mengoptimalkan Platform E-Learning Melalui Pembelajaran Adaptif untuk Demografi yang Beragam. Rodriguez, Marta; Rizky, Agung; Tanjung, Yul Ifda; Sumliyah, Sumliyah
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 1 (2025): September
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i1.905

Abstract

The rapid growth of e-learning and distance education platforms highlights the need for educational systems that are more inclusive and accessible to diverse demographics and ethnic groups. However, as adoption increases, challenges related to accessibility and personalized learning experiences have become more evident, especially among underrepresented groups. This study explores how adaptive learning technologies can optimize e-learning and distance education platforms to overcome these challenges and improve accessibility for diverse learners. A mixed-methods approach was used, combining surveys, interviews, and case studies with students and educators across various contexts. The study also reviews current adaptive learning technologies and their integration into online environments. The findings show that adaptive technologies significantly enhance learning experiences by personalizing content and delivery according to individual needs, thereby addressing barriers of accessibility and engagement. Moreover, these technologies help bridge educational gaps, ensuring a more equitable learning process for students from different backgrounds. The study concludes that implementing adaptive learning technologies can improve the inclusivity and accessibility of e-learning platforms, making them more effective for diverse demographic and ethnic groups. Further research is recommended to refine these technologies and examine their long-term effects on educational equity.
Advanced Cyber Threat Detection: Big Data-Driven AI Solutions in Complex Networks Rizky, Agung; Zaki Firli, Muhammad; Aulia Lindzani, Nur; Audiah, Sipah; Pasha, Lukita
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.42

Abstract

In the rapidly evolving digital landscape, cybersecurity has become increasingly critical, especially within complex network environments. This research presents the development of a cyber threat detection system that leverages Artificial Intelligence (AI) and Big Data analytics to enhance accuracy and speed in identifying and responding to cyber threats. The system was evaluated through rigorous testing, demonstrating a high detection accuracy of 95\% for malware and unauthorized access attempts, along with an impressive detection speed of 2 seconds on average for most threats. Additionally, the system exhibited strong scalability, maintaining optimal performance even with increasing network complexity. These findings underscore the system's robustness and practical applicability in real-world scenarios. However, further refinement is suggested to improve anomaly detection and reduce response times for more complex threats. This study contributes valuable insights into the integration of AI and Big Data in cybersecurity, providing a scalable and effective solution for protecting critical network infrastructures.