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All Journal ADI Journal on Recent Innovation (AJRI) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) International Transactions on Artificial Intelligence (ITALIC)
Kamal, Abdullah Arif
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Library & Information Science

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Reliability Assessment of Attendance Systems Based on Face Recognition Under Varying Lighting Conditions Afiyanto, Rafid; Astuti, Eka Dian; Kamal, Abdullah Arif; Santoso, Nuke Puji Lestari
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.v4i1.924

Abstract

The rapid adoption of face recognition technology for attendance systems has raised concerns about its reliability under varying lighting conditions, which often affect real world deployment. This study aims to analyze the reliability of a face recognition based attendance system under diverse lighting scenarios, addressing challenges in accuracy and robustness. The research employs a deep learning approach, utilizing a Convolutional Neural Network (CNN) trained on a dataset of facial images captured under controlled and uncontrolled lighting conditions, ranging from low to high illumination levels. The methodology includes preprocessing techniques for illumination normalization and feature extraction, followed by performance evaluation using metrics such as accuracy, precision, and false acceptance rate. Experimental results demonstrate that the proposed system achieves an accuracy of 92% in optimal lighting but drops to 78% under low light conditions, highlighting the impact of illumination on recognition performance. The integration of adaptive preprocessing techniques improves reliability by 12% in challenging scenarios. This study concludes that while face recognition based attendance systems are highly effective, their reliability in diverse lighting conditions can be significantly enhanced through advanced preprocessing and robust algorithm design, offering practical implications for real time biometric applications in dynamic educational and workplace settings.
Digital Business Transformation through Shopee’s Integrated Strategy in Global E-Commerce Andayani, Dwi; Ubed, Roby Syaiful; Pranata, Sudadi; Hikam, Ihsan Nuril; Kamal, Abdullah Arif
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 2 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i2.1334

Abstract

In the rapidly evolving landscape of global e-commerce, businesses are compelled to undergo digital transformations to remain competitive. This study delves into the digital business transformation journey of Shopee, a prominent player in the e-commerce realm, focusing on its integrated strategy amidst the global market. Despite extensive research on digital marketing, there remains a gap in comprehending how digital marketing strategies, customer experience, technological innovation, and digital marketing engagement synergize within the context of Shopee’s digital ecosystem. This study aims to address this gap by analyzing the interplay between these variables and their impact on digital marketing engagement. Employing Structural Equation Modeling (SEM) using SmartPLS version 4, the research scrutinizes key variables, namely Digital Marketing Strategies, Customer Experience, Technological Innovation, and Digital Marketing Engagement. The analysis is supported by insights synthesized from academic literature, industry reports, and empirical data. The findings provide valuable insights into how the integration of digital marketing strategies, technological innovation, and customer experience contributes to strengthening digital marketing engagement within Shopee’s platform. This study offers important implications for practitioners and scholars navigating the complexities of digital business transformation. However, while acknowledging the significance of these findings, it is important to note the study’s limitations, including sample size and potential biases inherent in self-reported data. Nevertheless, this research contributes to the ongoing discourse on digital marketing strategies by offering a nuanced understanding of the multifaceted dynamics shaping the global e-commerce landscape.
Leveraging Big Data Analytics to Strategically Expand Digital Microcredit Access for MSMEs Rizky, Agung; Ramaditya, Muhammad; Kamal, Abdullah Arif
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1325

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a pivotal role in driving economic development and job creation, especially in emerging economies. However, limited access to formal credit remains a persistent challenge due to the reliance on conventional financial assessments that often exclude MSMEs with informal or incomplete financial histories. This study aims to investigate how big data analytics can be effectively leveraged to strategically expand digital microcredit access for MSMEs, offering more inclusive and accurate credit evaluation models. The research adopts a qualitative descriptive methodology, incorporating a comprehensive literature review and multiple case studies of fintech platforms that utilize alternative data sources such as e commerce transactions, mobile phone activity, utility bill payments, and social media engagement to construct alternative credit scoring systems. The findings indicate that big data enables improved risk profiling, faster loan processing, and wider financial inclusion by reaching unbanked and underbanked MSMEs. Additionally, the integration of machine learning algorithms in analyzing real time behavioral data enhances decision making precision and operational efficiency in digital lending. However, the study also raises critical issues regarding data privacy, ethical use, and transparency in automated credit decisions. In conclusion, the use of big data analytics offers transformative potential to reshape digital microcredit services, empowering MSMEs through accessible, scalable, and intelligent financial solutions that align with broader goals of sustainable economic inclusion and digital transformation.
Co-Authors Afiyanto, Rafid Astuti, Eka Dian Dwi Andayani, Dwi Hikam, Ihsan Nuril Pranata, Sudadi Rizky, Agung Roby Syaiful Ubed, Roby Syaiful Santoso, Nuke Puji Lestari