The Legal System for the Conversion of Commercial Companies in the Light of the Rules of the Saudi Corporate System

Alomari, Mohammad W.

doi:10.47738/ijaim.v2i4.43

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…[3], [23] Student satisfaction (SSG) Student satisfaction is the level of student satisfaction with the services, facilities, and teaching provided by a school or educational institution.…”

Section: Gamification Of Software Engineeringmentioning

confidence: 99%

The role of gamification implementation in improving quality and intention in software engineering learning

Wahyuningsih,

Sediyono,

Hartomo

et al. 2024

EduLearn

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Gamification can make learning more fun and engaging for students. Software engineering can utilize gamification to help students learn and improve their skills from the complexity of software engineering. This study used quantitative research to examines perceived ease of use, student satisfaction, and perceived usefulness to measure gamification quality, which can have an impact on software engineering intention, namely intention, loyalty, and participation in following and understanding software engineering materials. The data was collected based on an online questionnaire survey, 90 data were collected and then measured and analyzed using SmartPLS 3. The results showed that perceived ease of use, student satisfaction, and perceived usefulness have a significant influence on gamification quality, which also leads to a positive impact on software engineering intention. This research guides teachers and educational institutions that gamification is very successful as a learning medium to simplify complex information to be more interactive.

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“…[3], [23] Student satisfaction (SSG) Student satisfaction is the level of student satisfaction with the services, facilities, and teaching provided by a school or educational institution.…”

Section: Gamification Of Software Engineeringmentioning

confidence: 99%

The role of gamification implementation in improving quality and intention in software engineering learning

Wahyuningsih,

Sediyono,

Hartomo

et al. 2024

EduLearn

View full text Add to dashboard Cite

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“…Information Gain (IG) is a concept in decision theory and machine learning used to measure how much information a feature provides in predicting a desired class or target [25]- [28]. IG is used to select the most informative or most influential features in separating and classifying data.…”

Section: Information Gain (Ig)mentioning

confidence: 99%

A Comparative Study of Feature Selection Techniques in Machine Learning for Predicting Stock Market Trends

Paramita

2023

J. Appl. Data Sci.

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This study aims to compare the effectiveness of three feature selection techniques, namely Principal Component Analysis (PCA), Information Gain (IG), and Recursive Feature Elimination (RFE), in predicting stock market conditions. This research uses three different datasets from Kaggle that contain stock market value prediction data. The results show that RFE performs better than PCA and IG in predicting market value with fairly precise accuracy. By using the RFE technique, this study was able to identify the most influential features in prediction, reduce the dimensionality of the data, and improve the performance of the prediction model. This provides significant benefits in the world of stocks, including improved investment decisions, reduced investment risk, improved trading strategy performance, and identification of promising investment opportunities. For future research, further comparative studies between other feature selection techniques can be conducted. This research has novelty in several aspects. First, it applies different feature selection techniques, namely Principal Component Analysis (PCA), Information Gain (IG), and Recursive Feature Elimination (RFE), in the context of stock market prediction. The use of these techniques to select the most relevant features in predicting stock market conditions provides a deeper understanding of the influence of these features on stock price movements. Furthermore, this research utilizes different datasets from Kaggle, which represent various stock market value predictions. The use of different datasets provides variation in the data and allows this research to examine the performance of feature selection techniques in various stock market contexts. In conclusion, this research provides insight into the effectiveness of feature selection techniques in stock market value prediction and provides guidance for market participants to improve investment decisions and trading performance in the stock market.

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Comparison of Effectiveness of Logistic Regression, Naive Bayes, and Random Forest Algorithms in Predicting Student Arguments

Wahyuningsih,

Manongga,

Sembiring

et al. 2024

Procedia Computer Science

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The Legal System for the Conversion of Commercial Companies in the Light of the Rules of the Saudi Corporate System

Cited by 6 publications

References 15 publications

The role of gamification implementation in improving quality and intention in software engineering learning

The role of gamification implementation in improving quality and intention in software engineering learning

A Comparative Study of Feature Selection Techniques in Machine Learning for Predicting Stock Market Trends

Comparison of Effectiveness of Logistic Regression, Naive Bayes, and Random Forest Algorithms in Predicting Student Arguments

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