This paper discusses that the Digital marketplaces play a crucial role in connecting startups with potential investors, yet their adoption success depends on understanding the key factors influencing user intention. Predicting adoption behaviors accurately can help improve engagement and ensure platform sustainability. The study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to identify key adoption factors including Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Trust (TR), and Government Support (GS).and this has been widely applied to study technology adoption, limited research integrates this framework with machine learning models to predict adoption intention in e-marketplaces for startups. This study aims to develop machine learning-based prediction models for StartSmart an e-marketplace linking startups and investors and identify the most influential factors affecting adoption intention based on the UTAUT framework. Data from 542 respondents were analyzed using six machine learning techniques: Decision Trees (DT), Random Forests (RF), Gradient Boosting (GRB), XGBoost (XGB), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).Results indicate that DT achieved the highest accuracy (0.93) and precision (0.94), while RF obtained the highest AUC-ROC score (0.98). Analysis of feature importance revealed that PE and EE were the most significant predictors of adoption, followed by TR and GS. These findings provide valuable insights for platform developers to prioritize usability and performance improvements, and for policymakers to strengthen trust and government support. The study also highlights the potential of combining UTAUT with machine learning to enhance predictive accuracy in digital adoption research.

This study presents a profound exploration into the synthesis of cultural mythology and video gaming, epitomized by the creation of “Anito: Battle of the Gods.” With meticulous attention to detail, the game artfully merges immersive gameplay with the captivating narrative of the Philippines' rich cultural heritage. Grounded in the Scrum methodology, the project unfolded through iterative collaboration, ensuring both efficiency and alignment with player preferences. Extensive quantitative data was amassed through game evaluation sheets and purposive sampling, spotlighting the game's diverse aspects. Results unveiled resounding satisfaction among participants in categories spanning gameplay, aesthetics, user interface, player experience, and website usability. Theoretical implications accentuate the efficacy of cultural storytelling within games, while practical insights highlight the successful amalgamation of entertainment and education. Technically, the study underscores the potency of agile methodologies in crafting an engaging gaming experience. These findings hold far-reaching significance, demonstrating the power of culturally infused games to captivate and educate players. In this tapestry of research, the study signifies the convergence of creativity, culture, and technology, elevating gaming beyond leisure to an instrument of cultural exploration and enrichment.

One of the world's most serious challenges is the exponential expansion of the population. The Philippines is included in this category of "less developed countries." Moreover, the said country's population expansion is unstoppable. With at least three newborns born per minute, the country has one of the greatest population growth rates. Consequently, the researcher's objective is to use a machine-learning algorithm to forecast the possible Philippine population growth for the upcoming year. Employing Knowledge Discovery in Database (KDD) as the step-by-step process in determining solutions and answering each of the research questions of the study.With this, the researchers were able to forecast data using time series data analysis about the historical populations of the Philippines utilizing a 12-point moving average. Based on the accuracy performance of the model, the algorithm can be used as a reliable source and was considered a good fit with a 90.27% accuracy.