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All Journal Smart Techno (Smart Technology, Informatic and Technopreneurship) Benefit : Journal of Bussiness, Economics, and Finance Jurnal Riset Rumpun Ilmu Ekonomi (JURRIE)
UTAMI, NI PUTU MEILING
Udayana University
Computer Science & IT Economics, Econometrics & Finance Education

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Euforia atau Kehati-hatian? Studi Persepsi Gen Z terhadap Pinjaman Online berbasis Fintech di Era Digital Sukasih, Ni Kadek Dewi; Jayantini, Ni Nyoman Meita; Sari, Ni Ketut Ping Purnama; Utami, Ni Putu Meiling
Benefit: Journal of Bussiness, Economics, and Finance Vol. 3 No. 2 (2025): BENEFIT: Journal Of Business, Economics, and Finance
Publisher : Lembaga Penelitian Dan Publikasi Ilmiah (lppi) Yayasan Almahmudi Bin Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70437/benefit.v3i2.1427

Abstract

Penelitian ini mengkaji persepsi Generasi Z terhadap penggunaan pinjaman online berbasis fintech di era digital, dengan fokus pada tiga variabel utama: literasi keuangan, kepercayaan terhadap fintech, dan gaya hidup konsumtif. Melalui pendekatan kuantitatif dan analisis regresi, ditemukan bahwa literasi keuangan memiliki pengaruh paling signifikan dalam membentuk persepsi dan keputusan finansial Gen Z. Kepercayaan terhadap platform fintech juga berperan penting, menunjukkan bahwa aspek keamanan dan kredibilitas menjadi pertimbangan utama. Sementara itu, gaya hidup konsumtif turut berkontribusi, namun dampaknya relatif lebih rendah dibanding dua variabel lainnya. Model regresi yang digunakan menunjukkan tingkat prediktabilitas sebesar 47,2%, menandakan kelayakan model dalam menjelaskan fenomena yang diteliti. Temuan ini memberikan implikasi penting bagi pengembangan edukasi keuangan dan strategi pemasaran fintech yang lebih etis dan berkelanjutan.
Peran Penyaluran Kredit Perbankan kepada Sektor Prioritas dalam Mendorong Pemulihan Ekonomi di Pulau Sumatera Utami, Ni Putu Meiling; Ni Nyoman Dian Sudewi; Ni Ketut Ping Purnama Sari; Ica Rika Candraningrat
Jurnal Riset Rumpun Ilmu Ekonomi Vol. 4 No. 2 (2025): Oktober: Jurnal Riset Rumpun Ilmu Ekonomi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrie.v4i2.5996

Abstract

The financial sector plays an important role in promoting Indonesia's economic recovery. This study was conducted to determine the effect of credit to priority sectors on economic growth on the island of Sumatera. The data analysis technique used is dynamic panel data regression analysis with the system-GMM approach. The results of this study indicate that credit for the agricultural, forestry, and fishery sectors; manufacturing sector; the accommodation and food and drink provision sector can increase economic growth on the island of Sumatera, while the construction sector credit; wholesale and retail trade sector; and the transportation and warehousing sectors have a negative influence on economic growth. This shows that the credit of the banking sector has a positive and negative influence on economic growth on the island of Sumatera. Therefore, banks and the government are expected to increase financial literacy to the community and improve internet facilities, especially in rural areas.
Predicting Employee Attrition Using the Random Forest Algorithm Based on IBM HR Analytics Data Putu Satya Saputra; I Putu Gede Abdi Sudiatmika; Ni Putu Meiling Utami; I Putu Okta Priyana
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.173

Abstract

The phenomenon of employee attrition has become a serious challenge for organizations, as it directly affects productivity, recruitment costs, and long-term performance stability. Understanding the factors that lead to employee turnover can no longer rely solely on manual observation; therefore, data-driven approaches are required to identify hidden patterns within workforce data. This study aims to predict employee attrition using the Random Forest algorithm applied to the IBM HR Analytics Employee Attrition & Performance dataset, which consists of 1,470 records and 35 attributes. The research stages include data preprocessing, handling class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), model training, and performance evaluation using accuracy, precision, recall, F1-score, ROC-AUC, and a confusion matrix. The results indicate that the baseline model without SMOTE exhibits low recall for the attrition class, whereas the application of SMOTE significantly improves model performance, particularly for the minority class, achieving a final accuracy of 83.96%. The most influential features identified are Stock Option Level, MonthlyIncome, and JobSatisfaction. These findings provide a comprehensive understanding of the factors influencing employee attrition and can serve as a foundation for organizations in designing more adaptive and data-driven employee retention strategies.
Co-Authors ABDI SUDIATMIKA, I PUTU GEDE I Gusti Ayu Made Srinadi I Putu Okta Priyana I Wayan Sumarjaya Ica Rika Candraningrat Jayantini, Ni Nyoman Meita Ni Kadek Dewi Sukasih Ni Nyoman Dian Sudewi Putu Satya Saputra Sari, Ni Ketut Ping Purnama