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All Journal Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Journal of Computer Science and Technology Application
Rizky, Agung
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

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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; Ndlovu, Zinhle; Ramadan, Ahmad
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.
Digital Transformation Journey of SMEs in Indonesia During the Post Pandemic Era Rizky, Agung; Gunawan, Ahmad; Santiago Ikhsan, Ramiro
APTISI Transactions on Management (ATM) Vol 9 No 3 (2025): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/522w1t49

Abstract

The COVID-19 pandemic marked a significant turning point for small and medium enterprises (SMEs) in Indonesia, compelling many to rapidly transi- tion from traditional offline operations to digital business models. This study aims to analyze the digitalization journey of SMEs in the post-pandemic era by examining the opportunities and challenges they encounter during this transformation. A descriptive qualitative approach was employed, using secondary data sourced from official reports such as BPS, the Ministry of Cooperatives and SMEs, and the economy SEA Report, complemented by relevant academic literature. The findings reveal that digitalization has opened substantial oppor- tunities for SMEs, including expanded market reach, improved operational ef- ficiency, and enhanced customer engagement through e-commerce platforms, digital payment systems, and social media marketing. However, persistent bar- riers remain, such as limited digital literacy, uneven internet infrastructure, high implementation costs, and consumer trust issues in online transactions. This study concludes that while digitalization offers a vital pathway for SME growth and resilience, addressing these barriers requires collaborative efforts among business owners, policymakers, and digital platform providers. Future research should employ quantitative methods and explore sector-specific case studies to deepen understanding of digital transformation strategies.
Optimization of Machine Learning Algorithms for Fraud Detection in E-Payment Systems Rizky, Agung; Gunawan, Ahmad; Komara, Maulana Arif; Madani, Muchlisina; Harris, Ethan
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.68

Abstract

This study explores the optimization of machine learning algorithms for fraud detection in electronic payment (e-payment) systems. The rapid growth of e-payment platforms has introduced significant challenges in ensuring the security and integrity of financial transactions. Fraud detection plays a pivotal role in mitigating these risks, and the application of machine learning (ML) has emerged as a powerful tool to identify fraudulent activities. This research examines how Data Quality (DQ), Algorithm Selection (AS), and Optimization Techniques (OT) influence Model Performance (MP) and, subsequently, Fraud Detection Effectiveness (FDE). The study utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 3 to analyze the relationships between these variables. The results demonstrate that high Data Quality significantly enhances Model Performance, while Algorithm Selection and Optimization Techniques also contribute positively, albeit to a lesser extent. The findings reveal that Model Performance plays a crucial mediating role between these factors and the effectiveness of fraud detection. Fraud Detection Effectiveness is found to be significantly impacted by Model Performance, suggesting that improving model accuracy and efficiency is essential for better fraud detection outcomes. Reliability and validity tests show strong internal consistency for all constructs, with Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE) all reaching satisfactory levels. The study highlights the importance of data preprocessing, the careful selection of machine learning models, and optimization techniques in achieving high-performing fraud detection systems. The results provide valuable insights for the development of more robust and scalable fraud detection mechanisms in e-payment systems, contributing to the broader field of machine learning and cybersecurity. Future research could explore advanced techniques like deep learning and blockchain integration for further enhancement of fraud detection systems.
Harnessing AI to Improve Operational Effectiveness and Strengthen Organizational Adaptability Rizky, Agung; Arifin, Ridwan; Arif Andika; Maria Daeli, Ora Plane; Hua, Chua Toh
CORISINTA Vol 2 No 2 (2025): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i2.129

Abstract

This study explores the dual role of Artificial Intelligence (AI) in improving operational effectiveness and fostering organizational agility, two critical factors for success in today’s dynamic business environment. By leveraging technologies such as machine learning, predictive analytics, and robotic process automation, organizations can streamline workflows, enhance cost efficiency, and enable data-driven decision-making. The research adopts a qualitative approach, analyzing case studies and expert insights to uncover key findings. Results indicate that AI implementation significantly enhances process speed, decision accuracy, and adaptability while reducing operational costs. However, challenges such as resistance to change, high implementation costs, and ethical concerns—particularly regarding data privacy—pose barriers to adoption. To address these, organizations must adopt strategic measures such as phased implementation, robust training programs, and ethical frameworks. The study introduces a conceptual model that illustrates AI's central role in driving efficiency and adaptability, supported by comparative performance metrics demonstrating tangible benefits. This research contributes to the broader understanding of AI’s transformative impact, emphasizing its potential as a catalyst for innovation and competitiveness. Furthermore, it provides practical recommendations for overcoming barriers to adoption, ensuring sustainable integration of AI technologies. By addressing both opportunities and challenges, the findings serve as a roadmap for organizations aiming to harness AI's full potential. Future research should focus on industry-specific applications and strategies to tailor AI adoption to unique organizational needs, thereby maximizing its impact across diverse sectors. This study concludes that AI is indispensable for organizations striving to thrive in a rapidly evolving digital landscape.
Enhancing Brand Loyalty through Customer Satisfaction Strategies in Digital Business Dyatmika, Sutama Wisnu; Suyanto, Bagong; Setijaningrum, Erna; Rizky, Agung; Mkhize, Thabo
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 2 (2025): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i2.558

Abstract

In the era of rapidly developing digital business, understanding the relationship between Customer Satisfaction Management Strategy (CSMS) and Brand Loyalty (BL) is crucial for a company success. Digital business platforms present unique opportunities and challenges, making it essential to explore strategies that foster strong customer relationships and loyalty. This research aims to investigate the impact of CSMS on BL in a digital business context using the SmartPLS approach. The study seeks to provide both theoretical and practical insights into the dynamics of customer satisfaction and loyalty in digital environments. Data was collected through online surveys of customers from various digital business platforms. The analysis was conducted using Structural Equation Modeling (SEM) with SmartPLS, which is particularly suited for examining complex relationships between latent variables. The results of the analysis show that there is a significant relationship between CSMS and BL. These findings highlight the importance of fostering meaningful interactions between brands and customers to enhance loyalty in digital business environments. The practical implication of this research is the necessity for companies to design marketing strategies that prioritize meeting customer needs and preferences. This approach not only strengthens customer loyalty but also equips businesses to navigate the challenges of competitive digital markets. The study contributes valuable insights for business practitioners and adds to the literature on marketing management in the ever-evolving digital era.
Co-Authors Amsyar, Izwan Arif Andika Audiah, Sipah Aulia Lindzani, Nur Bagong Suyanto Beldiq, Eiser Aaron Cristhopher, Ethan Dyatmika, Sutama Wisnu Ekawaty, Anita Gunawan, Ahmad Harris, Ethan Hua, Chua Toh Komara, Maulana Arif Madani, Muchlisina Maria Daeli, Ora Plane Mkhize, Thabo Mochamad Heru Riza Chakim Ndlovu, Zinhle Ninda Lutfiani Pasha, Lukita Ramadan, Ahmad Ridwan Arifin Rodriguez, Marta Santiago Ikhsan, Ramiro Setijaningrum, Erna Sumliyah, Sumliyah Tanjung, Azrul Tantri Wenny Sitanggang Untung Rahardja Utami, Rahayu Tri Yul Ifda Tanjung, Yul Ifda Zaki Firli, Muhammad