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All Journal Aptisi Transactions on Technopreneurship (ATT) Journal of Computer Science and Technology Application
Martono, Dedi
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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QR Code As the Delivery of Information to Universities Sudarto, Ferry; Martono, Dedi; Hartatik, Rika Ayu Desi
Aptisi Transactions On Technopreneurship (ATT) Vol 1 No 2 (2019): September
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v1i2.34

Abstract

Wall or magazine magazines are a means of delivering information on Raharja College campus. In the delivery of information, the magazine has been widely spread in several applications of Raharja College, iRan and GreenDoc. However, this is still not optimal because the method used is still using paper. And also the lack of awareness of students will read the information on the magazine. QR codes are a type of two-dimensional matrix code or bar code developed by Denso Wave in 1994. With that, the form of information is reduced to a QR code that can store detailed information displayed on a monitor screen available at several strategic corners of the campus and scan the code on the main page of the online magazine website. The method used is the waterfall method, observation, and literature study and journal references as supporting in writing. This research was designed to make it easier to access information and attract reading interest to students of Raharja College.
Predictive Analysis of Startup Ecosystems: Integration of Technology Acceptance Models with Random Forest Techniques Bennet, Daniel; Anjani, Sheila Aulia; Daeli, Ora Pertiwi; Martono, Dedi; Bangun, Cicilia Sriliasta
CORISINTA Vol 1 No 1 (2024): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i1.8

Abstract

In the dynamic realm of startup ecosystems, forecasting trends and measuring success pose significant challenges. To tackle this multifaceted issue, a novel research method proposes integrating the Technology Acceptance Model (TAM) with the robust Random Forest algorithm, thereby enhancing predictive accuracy. This innovative approach encompasses various aspects including technical intricacies, financial dynamics, stakeholder interactions, and entrepreneurial challenges. Employing empirical data, such as revenue growth, capital raised, innovation rate, and active users, forms the foundation of this methodology. The model’s efficacy is demonstrated through a process involving training on 80% of the dataset and testing on the remaining 20%, showcasing superior predictive capabilities compared to conventional methods. Comparative analysis with established models like logistic regression further highlights the superiority of the integrated TAM and Random Forest approach, particularly in predicting startup success. These findings offer invaluable insights for entrepreneurs navigating the complexities of the startup landscape, as well as for investors, policymakers, and educators. Understanding and supporting growth dynamics within the startup ecosystem can foster innovation and prosperity. Moreover, in the academic sphere, this research contributes a novel framework for startup prediction, enriching existing knowledge and facilitating informed decision-making. Overall, this research not only provides practical applications for immediate stakeholders but also contributes to advancing the theoretical foundations of startup prognostication, thus serving as a significant milestone in the field. 
Co-Authors Anjani, Sheila Aulia Bangun, Cicilia Sriliasta Bennet, Daniel Daeli, Ora Pertiwi Ferry Sudarto, Ferry Hartatik, Rika Ayu Desi Purba, Eduard Eduard Sukma, Heru