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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Business and Finance Journal
Bahari, Annisa Indira Larashati
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Analysis of Turnover Intention Factors Using the Partial Least Square Structural Equation Modeling (SEM-PLS) Method Bahari, Annisa Indira Larashati; Nadlifatin, Reny; Anshori, Mohamad Yusak
Jurnal Bisnis dan Keuangan Vol 9 No 2 (2024): Business and Finance Journal
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/bfj.v9i2.6365

Abstract

The decline in human resources and high turnover rates continue to be global issues that organizations need to address. PT XYZ faces a significant challenge with a fixed employee turnover rate of 10% in 2023. Given the high turnover rate, it's crucial to identify the factors influencing employees' intentions to leave in order to mitigate this trend. This study aims to develop a research model that incorporates six variables: job stress, job satisfaction, compensation, commitment, work environment, and turnover intention, which will be tested through 13 hypotheses. Data collection will be conducted through purposive sampling of employees at PT XYZ, using questionnaires distributed via Google Forms and analyzed using the SEM-PLS method. The findings indicate that job satisfaction, compensation, and a supportive work environment significantly negatively impact job stress and turnover intention, while job stress significantly positively affects turnover intention. No significant effects of commitment on job stress or turnover intention were found, suggesting that commitment alone may not be a strong independent factor in managing job stress or retention. The implications of this study suggest that the company should enhance job satisfaction, adjust compensation, and improve the work environment while reducing job stress to decrease turnover intention.
Neural Networks-Based Forecasting Platform for EV Battery Commodity Price Prediction Togatorop, Andrew Reinhard Marulak; Bahari, Annisa Indira Larashati; Choiruddin, Achmad
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 7 No 2 (2023): August 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v7i2.19999

Abstract

This study explores the impact of green energy-based economies on the growing use of electric vehicle (EV) batteries in transportation and electronic devices. Despite the environmental benefits, concerns have emerged regarding the supply, pricing, and volatility of raw materials used in battery manufacturing, exacerbated by geopolitical events such as the Russian-Ukrainian war. Given the high uncertainty surrounding EV commodity materials, this research aims to develop forecasting tools for predicting the prices of essential lithium-based EV battery commodities, including Lithium, Cobalt, Nickel, Aluminum, and Copper. The study builds on previous research on commodity price forecasting. Using Neural Networks such as LSTM that run using analytics platforms like RapidMiner, a robust and accurate models is able to be produced while require little to no programming ability. This will solve the needs to produce advanced predictions models for making decisions. As the results from the research, the models that are produced are successful in generating good prediction models, in terms of RMSE of 0,03 – 0,09 and relative errors of 4-14%.
Co-Authors Choiruddin, Achmad Mohamad Yusak Anshori Reny Nadlifatin Togatorop, Andrew Reinhard Marulak