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All Journal Journal of Social Research Asian Journal of Social and Humanities
Bambang Suharjo
Universitas Pertahanan Republik Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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The Influence of Transformational Leadership, Transactional Leadership and Lecturers’ Competence on the Performance of Naval Staff and Command School Lecturers Mediated by Motivation Rudi Lazuardi; Willy Arafah; Bambang Suharjo
Journal of Social Research Vol. 2 No. 7 (2023): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v2i7.1220

Abstract

The purpose of this study was to analyze the performance of Seskoal lecturers by observing the influence of the aspects of Transactional Leadership, Transactional Leadership, Competence, and Motivation of the lecturers as mediating variables. The research was conducted within the scope of the Naval Staff and Command School with 105 respondents consisting of Seskoal lecturers and structural officials involved in teaching, training, and nurturing activities. The data obtained were analyzed using Structural Equation Model-Partial Least Square (SEM-PLS) software. The independent variables Transformational Leadership and Transactional Leadership were found to have no direct effect on performance. Instead, the independent variables Competence and Motivation positively and significantly affected performance. This argument is due to the fact that the performance dimension in the form of the Tridarma of Higher Education has yet to be widely understood. The working period of lecturers is generally short, so the impact of the transformation has yet to be felt. It is also very rare for transactions to occur in this performance. Variable influence. The effect of the independent variable Transformational Leadership also has no positive and significant effect on motivation as the dependent variable, whereas Transactional Leadership and Competence have a positive and significant effect on Motivation. This result shows that transactions will increase lecturers' motivation, and lecturers with high competence will automatically increase their motivation. Interestingly, the indirect effect, where motivation acts as an intervening variable, shows that there is no effect of Transformational Leadership on Performance through motivation. However, vice versa for the variables Transactional Leadership and Competence affect Performance through Motivation. This result shows that the effect of the transformation has not been felt directly or indirectly as explained otherwise, for transactions will increase motivation which indirectly increases performance and competence.
Data Mining Applications to Prediction Stock Prices Using Decision Trees and Neural Networks Dadan Shavkat Riswantoro; Harry Pratomo Bagaskoro; Bambang Suharjo; Danang Rimbawa
Asian Journal of Social and Humanities Vol. 4 No. 10 (2026): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v4i10.749

Abstract

Stock price prediction remains a significant challenge in financial markets due to the high volatility and complexity of influencing factors. This study explores the application of hybrid models combining Decision Tree (DT) and Neural Network (NN) methodologies to enhance stock price prediction accuracy. The research utilizes extensive historical market data as the foundational input for training both models individually. The Decision Tree model is employed for its interpretability and ability to handle non-linear relationships, while the Neural Network model capitalizes on its capacity to learn complex patterns through its layered architecture. After training and evaluating each model separately, a hybrid approach is introduced, which averages the predictions from both the DT and NN models. Performance is quantitatively assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the hybrid model consistently outperforms individual models, achieving an MAE of 5.6 and RMSE of 7.94, with an overall accuracy of 91.5%. This fusion of methodologies demonstrates improved accuracy and significantly reduces error margins, showcasing the complementary strengths of both algorithms. The findings suggest that leveraging hybrid models can effectively mitigate risks associated with market fluctuations and enhance investment strategies. This research contributes to the field of financial forecasting by providing investors with more robust tools for making informed decisions, and offers recommendations for future research directions in integrating machine learning techniques for financial prediction.
Co-Authors Dadan Shavkat Riswantoro H. A. Danang Rimbawa Harry Pratomo Bagaskoro Rudi Lazuardi Willy Arafah