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All Journal Journal of Information Systems and Informatics International Journal of Business, Management and Economics International Journal of Education, Teaching, and Social Sciences
Lainjo, Bongs
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Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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The The Application of Artificial Intelligence and Machine Learning to Enhance Results-Based Management Lainjo, Bongs
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.609

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) technologies have revolutionized numerous industries and sectors, offering transformative potential for Results-Based Management (RBM). RBM is a management paradigm wherein organizations and government entities plan and assess the effectiveness of their projects, policies, or programs in achieving outcomes. Integrating AI and ML into RBM can significantly enhance outcomes, fostering data-driven and informed decision-making. AI and ML integration into RBM practices facilitates improved decision-making, resource optimization, accountability, and transparency. These technologies enhance RBM by enabling predictive analytics, real-time monitoring, task automation, customization, and scalability. The dynamic synergy of AI and ML extends beyond RBM into sectors like agriculture, public health, academia, and public administration. Despite their immense potential, AI and ML tools face challenges such as perpetuating inaccuracies and biases due to inherent biases or low data quality. Nevertheless, their application in RBM empowers organizations to plan better, monitor, evaluate, and refine projects and programs, optimizing resource allocation and performance. Ongoing research, ethical considerations, data quality, and accountability are essential priorities for harnessing the full benefits of AI and ML in RBM. Therefore, this research paper investigates the potential of AI and ML tools and technologies in improving results-based management. It comprehensively reviews existing literature, practical applications, and case studies to elucidate how AI and ML can enhance results-based management practices and contribute to better decision-making.
From Data Analysis to Creative Arts: The Ubiquity and Impact of Artificial Intelligence in Academia Lainjo, Bongs
International Journal of Education, Teaching, and Social Sciences Vol. 4 No. 4 (2024): International Journal of Education, Teaching, and Social Science
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/ijets.v4i4.2244

Abstract

Integrating Artificial Intelligence in academia has revolutionized various fields with new opportunities for innovation, research, and learning.  The capability of AI to analyze enormous amounts of data at such incredibly short times contributes to research advancement across natural sciences, humanities, social sciences, engineering, and healthcare sciences. For instance, in natural sciences, AI algorithms support various types of data analysis and simulation, helping to make new discoveries and provide methods and new approaches to look at existing research methods. AI advances in social sciences employ prediction modeling and machine learning to enhance economic models and other behavioral analyses. AI has presented humanities advancements in text analysis and interpretation of history work, augmenting the research based on historical data with data analysis. In engineering and technology, AI's role is twofold: enhancing physical security and, at the same time, posing new threats in the form of complex cyber threats. In a related context, AI’s application for diagnosis and treatment planning has been observed in the healthcare sector. It has shown the potential capability of improving the care of patients far beyond any imagined capabilities. Nevertheless, the application of AI in academia comes with some challenges. Privacy, protection, ethical views, and prejudice enhancement are some of the most significant issues that should be considered.  Despite these challenges, AI creates multi-professional collaboration and advances in knowledge and performance in various scientific disciplines. AI continues to thrive in the future of academia, as future advancement holds possible new research horizons, educational improvement, and world problem-solving. With the rapid evolution of AI, its incorporation into academia and its abuses, biases, and risks need to be constantly reviewed
Application of Machine Learning in Predicting the Number of Bike Share Riders Lainjo, Bongs
International Journal of Business, Management and Economics Vol. 3 No. 4 (2022): International Journal of Business, Management and Economics
Publisher : Training & Research Institute - Jeramba Ilmu Sukses (TRI-JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/ijbme.v3i4.865

Abstract

This study aims to investigate the factors influencing the demand for bike sharing to identify the variables that significantly predict the need for shared bicycles. The study aims to create more in-depth knowledge about bike-sharing models to enhance the ideas on designing, developing, implementing and utilization of bike-sharing models. A multiple regression model was used to model the demand for 703 Capital Bikeshare’s shared bikes in the USA. The variations in the need for the bikes were assessed based on certain variables, such as the total number of registered and unregistered bikers and renters. Linear regression analyses were conducted to determine the factors that statistically and significantly predict the number of bikes rented. Machine learning classifiers: Random Forest, Decision Trees, Nearest Neighbor and XGBoost were used to determine the most important predictors of bike demand and the data analyzed in SPSS V25, R and PYTHON. Increase in demand for shared bikes was attributed to factors such as increase in temperature (p =0.000), days of the week except the first day of the week (p < 0.05), the month of September (p = 0.036), spring season ((p = 0.000), fall seasons (p = 0.001) and humidity (p = 0.000). A significant decrease in the demand for shared bikes was observed on the first day of the week (p = 0.218) and days with strong winds (p = 0. 113). More people are likely to rent shared bikes on hot days, in September, during spring and fall seasons, on humid days, and all days of the week except on the first day of the week.
A Meta-Study of the Evolutionary Transformative Academic Landscape by Artificial Intelligence and Machine Learning Lainjo, Bongs; Tmouche, Hanan
International Journal of Education, Teaching, and Social Sciences Vol. 4 No. 1 (2024): International Journal of Education, Teaching and Social Sciences
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/ijets.v4i1.1626

Abstract

This article titled “The Evolutionary Transformative Academic Landscape by Artificial Intelligence and Machine Learning (Meta-Study)" explores the profound impact of AI and ML on education, particularly in the context of remote learning and the COVID-19 pandemic. The study systematically reviews the literature on AI in higher education, aiming to understand its pedagogical advantages and ethical implications. The objectives include assessing digital transformation in classrooms, evaluating the effectiveness of AI and ML in enhancing learning outcomes, examining their role in personalized learning and identifying areas for improvement. The research questions focus on the contribution of AI and ML in digital classrooms, their effectiveness in enhancing learning outcomes, and their role in supporting individualized learning. The literature review delves into AI and ML's role in academia, their impact on teaching, learning, and research, and the ethical considerations of their application. The study employs a meta-systematic review approach, incorporating statistical analyses and addressing ethical concerns to ensure AI's effective and ethical utilization in education. This study's findings indicate a positive correlation between implementing AI and ML technologies and improving student engagement, academic performance, research productivity, and teacher satisfaction. It highlights the necessity of further research to optimize AI use in education, considering software quality, student learning styles, and teacher integration skills. The study contributes to understanding the transformative role of AI and ML in reshaping education and fostering a more advanced, dynamic academic environment.
Co-Authors Tmouche, Hanan