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All Journal Branding: Jurnal Manajemen & Bisnis Inovbiz : Jurnal Inovasi Bisnis
Hardian Dwi Utomo
Universitas PGRI Banyuwangi
Economics, Econometrics & Finance

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AUTHENTICITY OR PROMOTIONAL CONTENT? THE INFLUENCE OF USER-GENERATED CONTENT AND CONTENT WRITING ON CUSTOMER ENGAGEMENT IN MALANG COFFEE SHOP Reni Rupianti; Hardian Dwi Utomo; Nur’ Adilla; Firda Meisaroh
Branding: Jurnal Manajemen dan Bisnis Vol 5 No 1 (2026): BRANDING: Jurnal Manajemen dan Bisnis
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jb.v5i1.57383

Abstract

This study aims to examine the influence of User-Generated Content (UGC) and Content Writing on Customer Engagement in coffee shops in Malang City. As the coffee industry in Malang continues to grow rapidly, businesses are increasingly utilizing social media to build relationships with consumers. This research employed a quantitative approach using a survey method involving 186 respondents selected through purposive sampling. Data analysis was conducted using Multiple Linear Regression. The results indicate that both UGC and content writing have a positive and significant influence on customer engagement. Furthermore, the findings reveal that UGC has a stronger influence than content writing in driving customer engagement. The study suggests that coffee shop owners should prioritize organic content from users to strengthen their brand presence. This research provides a practical model for coffee shop owners in Malang to optimize their digital marketing strategies by leveraging business dashboard analysis to monitor engagement metrics.
Early Detection of Financial Distress at PT. Garudafood Putra Putri Jaya Tbk Using Machine Learning Algorithms Muhammad Bahanan; Mas’ud Hermansyah; Lukman Hakim; Hardian Dwi Utomo
Inovbiz: Jurnal Inovasi Bisnis Vol 14 No 1 (2026): Edition June 2026
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/inovbiz.v14.i1.1560

Abstract

This study aims to develop an early warning system to detect potential financial distress at PT Garudafood Putra Putri Jaya Tbk using Machine Learning (ML) algorithms. A quantitative case study approach was applied using the company’s publicly available quarterly financial reports from 2019 to 2024. Five ML algorithms were tested: Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The Random Forest algorithm achieved the highest accuracy of 85.71%, with a precision of 100%, recall of 66.67%, and an F1-score of 80%. Operating Profit Margin and Debt to Asset Ratio were identified as the most significant indicators in detecting financial distress. The proposed model can serve as a decision-support tool for management, investors, and regulators to proactively anticipate financial problems and reduce bankruptcy risk through early intervention. Keywords: Abstract, aim, scope, result
Co-Authors Firda Meisaroh Lukman Hakim Mas’ud Hermansyah Muhammad Bahanan Nur’ Adilla Reni Rupianti