cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rani Santika
Contact Email
rsantika851@gmail.com
Phone
+6285793538817
Journal Mail Official
journaliss01@gmail.com
Editorial Address
Jl. Pangeran Cakrabuana Blok E6, Sendang, Kec. Sumber, Cirebon, Jawa Barat 45611
Location
Unknown,
Unknown
INDONESIA
Interdisciplinary Social Studies
Published by CV. International Journal Labs
ISSN : 28080467     EISSN : 28085051     DOI : 10.55324
Core Subject : Health, Education, Social,
Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences
nterdisciplinary Social Studies (ISS) is an interdisciplinary publication of social studies and writing which publishes papers to international audiences of social researchers. ISS aims to provide a forum for scholarly understanding of social studies and plays an important role in promoting the process that accumulated knowledge, values, and skills are transmitted from one generation to another; and making methods and contents of evaluation and research in social, available to socialist and research workers. The journal encompasses a variety of topics, including education, management, cultural studies, law, social health, psychology, and geography, to economics belonging to the social context. Papers accepted: 1) Report evaluation and original research; 2) Literature review; and 3) An extensive book reviews section on social materials and equipment.
Arjuna Subject : Ilmu Sosial - Ilmu Sosial (Lain-Lain)
Articles 1 Documents
 1 
Search results for , issue "Vol. 5 No. 4 (2026): Interdisciplinary Social Studies" : 1 Documents clear
Analysis of Employee Resignation Probability Using the Random Forest Method: A Case Study at PT Top Remit Adriel, Matthew; Gunani Partiwi, Sri
Interdisciplinary Social Studies Vol. 5 No. 4 (2026): Interdisciplinary Social Studies
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/iss.v5i4.1064

Abstract

High employee turnover can negatively affect organizational stability and productivity, making data-driven predictive strategies essential to identify factors influencing employees’ resignation decisions. This study aims to analyze employee data at PT. Top Remit to predict resignation probability using the Random Forest algorithm, which is known for its strong performance in handling complex and heterogeneous data. Using a machine learning approach, this research processes historical employee data that include demographic factors, job satisfaction, compensation, and performance indicators. The dataset consists of labeled employee records based on their employment status. The Random Forest model was applied to identify patterns contributing to employee turnover and is evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The results indicate that the optimized Random Forest model, after hyperparameter tuning using Grid Search, achieves a test accuracy of 82.5%, with a resignation-class precision of 1.00 and a recall value of 0.6316. Furthermore, feature importance analysis reveals that the most influential factors affecting resignation decisions are annual performance scores, average review scores, compensation satisfaction, job satisfaction, and work engagement. Based on these findings, the predictive model can be utilized as a decision-support tool to design more targeted and data-driven employee retention strategies. This study is expected to contribute to the development of predictive analytics in human resource management, particularly in the application of machine learning methods to support workforce management and sustainability.

Page 1 of 1 | Total Record : 1

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 5 No. 4 (2026): Interdisciplinary Social Studies Vol. 5 No. 3 (2026): Interdisciplinary Social Studies Vol. 5 No. 2 (2026): Interdisciplinary Social Studies Vol. 5 No. 1 (2025): Regular Issue: October-December 2025 Vol. 4 No. 4 (2025): Regular Issue: July-September 2025 Vol. 4 No. 3 (2025): Regular Issue: April-June 2025 Vol. 4 No. 2 (2025): Regular Issue: January-March 2025 Vol. 4 No. 1 (2024): Regular Issue: October-December 2024 Vol. 3 No. 4 (2024): Regular Issue: July-September 2024 Vol. 3 No. 3 (2024): April-June 2024 (Regular Issue) Vol. 3 No. 2 (2024): January-March 2024 (Regular Issue) Vol. 3 No. 1 (2023): October-December 2023 (Regular Issue) Vol. 2 No. 12 (2023): Special Issue Vol. 2 No. 11 (2023): Special Issue Vol. 2 No. 10 (2023): Special Issue Vol. 2 No. 9 (2023): Special Issue Vol. 2 No. 8 (2023): Special Issue Vol. 2 No. 7 (2023): Special Issue Vol. 2 No. 6 (2023): Special Issue Vol. 2 No. 5 (2023): Special Issue Vol. 2 No. 4 (2023): Regular Issue Vol. 1 No. 12 (2022): Special Issue Vol. 1 No. 11 (2022): Interdisciplinary Social Studies Vol. 1 No. 11 (2022): Special Issue Vol. 1 No. 10 (2022): Interdisciplinary Social Studies Vol. 1 No. 10 (2022): Special Issue Vol. 2 No. 3 (2022): Regular Issue Vol. 2 No. 2 (2022): Interdisciplinary Social Studies Vol. 2 No. 2 (2022): Regular Issue Vol. 2 No. 1 (2022): Regular Issue Vol. 1 No. 9 (2022): Special Issue Vol. 1 No. 8 (2022): Special Issue Vol. 1 No. 7 (2022): Special Issue Vol. 1 No. 6 (2022): Special Issue Vol. 1 No. 5 (2022): Special Issue Vol. 1 No. 4 (2022): Reguler Issue Vol. 1 No. 3 (2021): Reguler Issue Vol. 1 No. 2 (2021): Reguler Issue Vol. 1 No. 1 (2021): Reguler Issue Vol. 1 No. 1 (2021): Interdisciplinary Social Studies More Issue