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All Journal Winter Journal: IMWI Student Research Journal Jurnal Pijar : Studi Manajemen dan Bisnis
Zeffanya Raphael Wijaya
Universitas Sains Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences

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PENGARUH FLEXING DAN GAYA HIDUP MEWAH TERHADAP BRAND AWARENESS MELALUI PERSONAL BRANDING Alika Rahmah; Zeffanya Raphael Wijaya
Jurnal Pijar Vol 4 No 3 (2026): Jurnal Pijar : Studi Manajemen dan Bisnis
Publisher : PT Naureen Digital Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65096/pmb.v4i3.2074

Abstract

This study analyzes the influence of flexing and luxurious lifestyles on brand awareness through personal branding on the TikTok platform, using Shella Saukia as a case study. A quantitative method with a causal design was used, involving 100 active TikTok users who were familiar with the @shellasaukia account. Data analysis used path analysis with SPSS and AMOS. The results showed that flexing has no significant effect on personal branding (t=1.719; p=0.086), while luxurious lifestyle has a significant effect (t=3.454; p=0.000). Personal branding has a significant effect on brand awareness (t=9.248; p=0.000). Simultaneously, flexing and luxurious lifestyle have significant effects on personal branding (F=49.87; R²=0.507) and on Brand awareness (F=59.04; R²=0.476). Personal branding acts as a partial mediator with an indirect effect of luxurious lifestyle of 0.269. These findings contribute to digital communication strategies on social media.
Research Trends and Evolution of Machine Learning–Accounting Systems for SME Anomaly Detection Budi Jejen Zaenal Abidin; Zulkarnain; Zeffanya Raphael Wijaya; Bambang Somantri
Winter Journal: IMWI Student Research Journal Vol. 7 No. 2 (2026): Winter Journal: IMWI Student Research Journal
Publisher : Institut Manajemen Wiyata Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52851/wt.v7i2.120

Abstract

Micro, Small, and Medium Enterprises (MSMEs) face persistent financial governance risks due to informal accounting practices, limited digital capabilities, and weak anomaly detection mechanisms in conventional Accounting Information Systems (AIS). This study aimed to map the development, intellectual structure, thematic evolution, and research opportunities at the intersection of Machine Learning (ML), AIS, financial anomaly detection, and MSMEs. A longitudinal descriptive quantitative design was employed using bibliometric analysis and science mapping of 202 Scopus-indexed articles and review papers published between 2018 and 2026. Data were processed using RStudio-bibliometrix, Biblioshiny, and VOSviewer through performance analysis, keyword co-occurrence analysis, thematic mapping, clustering, and thematic evolution analysis. The findings revealed rapid growth in this research field, with an annual growth rate of 60.48% and a publication peak in 2025. Four major thematic clusters were identified: financial forensics, ensemble algorithms, deep learning, and the SME operational context. Random Forest and XGBoost emerged as particularly promising approaches for resource-constrained MSMEs due to their efficiency, predictive accuracy, and interpretability. However, the field remains fragmented, computationally oriented, and limited in terms of validation using authentic MSME datasets. The study concluded that future intelligent AIS development should integrate predictive performance, accounting controls, class-imbalance handling, explainable artificial intelligence (XAI), user acceptance, and broader international collaboration to support transparent, practical, and preventive financial governance in small businesses worldwide.
Co-Authors Alika Rahmah Bambang Somantri Budi Jejen Zaenal Abidin Zulkarnain