Analysis of Development of Artificial Intelligence in the Game Industry

Yakan, Sayid Adli

doi:10.34306/ijcitsm.v2i2.100

Cited by 13 publications

(12 citation statements)

References 11 publications

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“…company. This is in agreement with previous research which explains that the work environment has a negative relationship to turnover intention [6], [29], [30].…”

Section: Discussionsupporting

confidence: 93%

Influence of Work Environment and Work Characteristics on Turnover Intention System with Mediation Role of Work Engagement

Indriyani¹,

Meria²

2022

IJCITSM

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The millennial generation currently dominates human resources in the company. Millennial generation employees have many advantages, but millennials are considered less committed to their work, so they are synonymous with high turnover intentions. The research was conducted to know the effect of the work environment and job characteristics on the turnover intention with the mediating role of work engagement in millennial generation employees, work environment, and job characteristics as independent variables. Turnover intention is the dependent variable, and work engagement is a mediating variable. This study uses a survey method by distributing questionnaires where the respondents used 204 employees in the millennial generation with domiciles in the West Jakarta area. Then, the data was processed using the SEM-PLS method. The results showed that the work environment hurt turnover intention. Job characteristics do not affect turnover intention. Job engagement hurts turnover intention. The work environment has a positive effect on work engagement. Work characteristics have a positive effect on work engagement. Furthermore, work engagement mediates the relationship between the work environment and turnover intention. Job engagement also mediates the relationship between job characteristics and turnover intention.

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“…company. This is in agreement with previous research which explains that the work environment has a negative relationship to turnover intention [6], [29], [30].…”

Section: Discussionsupporting

confidence: 93%

Influence of Work Environment and Work Characteristics on Turnover Intention System with Mediation Role of Work Engagement

Indriyani¹,

Meria²

2022

IJCITSM

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“…The outcomes revealed an accuracy rate of 70%, showcasing the algorithm's effectiveness in identifying heart disease patients. Additionally, the Area Under the Curve (AUC) value stood at 0.875, indicating excellent classification performance in heart disease detection [13], [14].…”

Section: Iics Semnastik 2023mentioning

confidence: 99%

Implementation of the Naive Bayes Algorithm to Predict the Safety of Heart Failure Patients

Barus,

Lauwren,

Pangaribuan

et al. 2023

conferenceseries

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Heart disease stands as a prominent contributor to global mortality, as indicated by data released by the World Health Organization (WHO). In 2019 alone, an estimated 17.9 million individuals succumbed to cardiovascular disease, accounting for 32% of all worldwide deaths. Of these fatalities, 85% were attributed to heart disease and stroke. Individuals harboring the potential for heart failure often persist in unhealthy lifestyles, regardless of their awareness of underlying heart conditions. To address this issue, the research explores the application of machine learning to identify an optimal method for classifying heart failure patients, employing the Naive Bayes technique. This algorithm has found extensive use in the health sector, demonstrating success in classifying various conditions such as hepatitis, stroke, respiratory infections, and more. The Naive Bayes algorithm, applied in this study, exhibited notable accuracy, precision, sensitivity, and overall classification efficacy. Specifically, the classification accuracy for heart failure patients reached 74.58%, the precision level was 97.67%, sensitivity achieved 75%, and the AUC (Area Under ROC Curve) stood at 0.857, indicating excellent classification within the 0.80 to 0.90 range. These findings can serve as an early warning system for individuals at risk of heart failure.

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“…Artificial Intelligence (AI) is designed to replicate human intelligence by imitating human thought processes. This technology employs data as a source of knowledge, enabling artificial intelligence to learn from previous mistakes and improve over time [16]. Artificial intelligence can be activated by human commands or autonomously based on prior experiences.…”

Section: Artificial Intelligencementioning

confidence: 99%

Implementations of Artificial Intelligence in Various Domains of IT Governance: A Systematic Literature Review

Hariyanti,

Janeswari,

Moningka

et al. 2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: Artificial intelligence (AI) has become increasingly prevalent in various industries, including IT governance. By integrating AI into the governance environment, organizations can benefit from the consolidation of frameworks and best practices. However, the adoption of AI across different stages of the governance process is unevenly distributed. Objective: The primary objective of this study is to perform a systematic literature review on applying artificial intelligence (AI) in IT governance processes, explicitly focusing on the Deming cycle. This study overlooks the specific details of the AI methods used in the various stages of IT governance processes. Methods: The search approach acquires relevant papers from Elsevier, Emerald, Google Scholar, Springer, and IEEE Xplore. The obtained results were then filtered using predefined inclusion and exclusion criteria to ensure the selection of relevant studies. Results: The search yielded 359 papers. Following our inclusion and exclusion criteria, we pinpointed 42 primary studies that discuss how AI is implemented in every domain of IT Governance related to the Deming cycle. Conclusion: We found that AI implementation is more dominant in the plan, do, and check stages of the Deming cycle, with a particular emphasis on domains such as risk management, strategy alignment, and performance measurement since most AI applications are not able to perform well in different contexts as well as the other usage driven by its unique capabilities. Keywords: Artificial Intelligence, Deming cycle, Governance, IT Governance domain, Systematic literature review

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Analysis of Development of Artificial Intelligence in the Game Industry

Cited by 13 publications

References 11 publications

Influence of Work Environment and Work Characteristics on Turnover Intention System with Mediation Role of Work Engagement

Influence of Work Environment and Work Characteristics on Turnover Intention System with Mediation Role of Work Engagement

Implementation of the Naive Bayes Algorithm to Predict the Safety of Heart Failure Patients

Implementations of Artificial Intelligence in Various Domains of IT Governance: A Systematic Literature Review

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