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All Journal ADI Journal on Recent Innovation (AJRI) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi
Rodriguez, Marta
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Analysis of Omni Channel Strategy in Digital Retail on Modern Indonesian Consumer Behavior: Analisis Strategi Omni Channel dalam Ritel Digital terhadap Perilaku Konsumen Indonesia Modern Baso, Farisha Andi; Sitorus, Santa Lusianna; Meilinda, Vivi; Anjani, Sheila Aulia; Rodriguez, Marta
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 1 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i1.1226

Abstract

The development of digital technology has driven a profound transformation in the retail industry, giving rise to the omni-channel strategy as a holistic ap proach to meet modern consumer expectations. This study aims to analyze the influence of online and offline channel integration on Indonesian consumer behavior in the context of digital retail. Using a qualitative approach through literature review and secondary data analysis, this study reveals that today’s In donesian consumers show a high preference for convenience, personalization, and speed of service in the shopping experience. An omni-channel strategy enables companies to provide a consistent, adaptive, and integrated consumer journey across various interaction points, which has a positive impact on customer loyalty and operational efficiency. Furthermore, the implementation of this strategy aligns with sustainability principles, particularly in supporting Sustainable Development Goals (SDGs) 9 (industrial and infrastructure innova tion) and point 12 (responsible consumption and production). These findings confirm that the success of digital retail in Indonesia is highly dependent on the ability of business actors to strategically manage channel integration, under stand evolving consumer behavior, and prioritize sustainability values in their operations.
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.
Predicting Supply Chain Risks Using Machine Learning for Resilient Operations Widayanti, Riya; Setiyowati, Harlis; Yusup, Muhamad; Rodriguez, Marta
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1376

Abstract

Rising supply chain disruptions highlight increasing vulnerabilities in global logistics networks caused by geopolitical conflicts, fluctuating demand, transportation failures, and environmental instability. These challenges reveal the limitations of conventional risk assessment approaches that rely heavily on manual analysis and historical data. Machine Learning (ML) offers a promising approach to enhance predictive intelligence and support more accurate decision making in complex supply chain environments. This study aims to develop and evaluate a Machine Learning based risk prediction model capable of identifying potential supply chain disruptions and enabling early detection of critical risk factors in global logistics operations. A quantitative experimental approach was employed using supply chain datasets integrated with disruption indicators from international logistics activities. The dataset consisted of more than 5,000 operational records collected between 2018 and 2024. Several machine learning algorithms were implemented and compared, including Random Forest, Gradient Boosting, and Support Vector Machines. Experimental results indicate that the Gradient Boosting algorithm achieved the highest predictive performance with an accuracy of 94.2%. The model successfully identified key determinants of supply chain risk, including demand variability, supplier reliability, and transportation delays. These findings confirm that machine learning based predictive models can enhance supply chain resilience by enabling early risk detection and supporting proactive decision making in global logistics operations.
Co-Authors Anjani, Sheila Aulia Farisha Andi Baso Harlis Setiyowati Meilinda, Vivi Muhamad Yusup Riya Widayanti Rizky, Agung Sitorus, Santa Lusianna Sumliyah, Sumliyah Yul Ifda Tanjung, Yul Ifda