Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MANAJEMEN (EDISI ELEKTRONIK) JBMR: Journal of Business and Management Review
Rian Oktafiani
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Career Management in Digital Transformation for Employee Wellbeing: A Systematic Literature Review Lesmana, Angga; Nur Wening; Rian Oktafiani
Journal of Business and Management Review Vol. 6 No. 2 (2025): (Issue-February)
Publisher : Profesional Muda Cendekia Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47153/jbmr.v6i2.1417

Abstract

Research Aims: to examine how career management and employee well-being are related and to emphasize the critical role that career management plays. Design/methodology/approach: This study gathered 201 articles from Scopus-indexed journals between 2015 and 2025 and examined 12 of them using a systematic literature review methodology. Research Findings: The findings demonstrated that employees' subjective well-being, work engagement, and career satisfaction are positively impacted by successful career management practices, such as job designing, career skill development, and career adaptability. However, there is the discrepancy between theory and practice, especially in the implementation of career strategies that support digital skills needs in organizations. In addition, career development programs such as careers skill have been shown to increase employees' self-efficacy and resilience in the face of change. Theoretical Contribution/Originality: pointing out the gap between theory and reality in digital career management and suggesting a comprehensive strategy to address how career management affects workers' well-being. The studies have important implication for human capital managers as they create career management policies and procedures to enhance workers' well-being during the digital transformation.
Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest Rian Oktafiani; Arief Hermawan; Donny Avianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5574

Abstract

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.
The Silent Exodus: A Systematic Review of Quiet Quitting and Its Impact on Employee Productivity and Organizational Culture Han Purnomo; Nur Wening; Rian Oktafiani
Jurnal Manajemen Vol. 16 No. 2 (2025): Jurnal Manajemen (Edisi Elektronik)
Publisher : UPT Jurnal & Publikasi Ilmiah SPs Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/jm-uika.v16i2.19211

Abstract

Quiet quitting refers to employees performing only their assigned duties without additional engagement, often triggered by a lack of appreciation, unfair compensation, job burnout, and an unsupportive organizational culture, which in turn affects productivity, employee retention, and organizational stability. This study aims to identify its causes, impact on employee productivity and organizational culture, and effective mitigation strategies. Using a Systematic Literature Review (SLR) with PRISMA guidelines, 12 relevant articles were selected from 78 identified in the Scopus database (2015–2025). Findings indicate that fair compensation, employee well-being, a positive work culture, and supportive leadership can reduce quiet quitting, while policies promoting work-life balance and transparent communication enhance employee engagement. These insights contribute to HR management strategies, with future research recommended to explore organizational interventions across industries for a more comprehensive approach.
Co-Authors Donny Avianto Han Purnomo Lesmana, Angga Nur Wening