This activity aims to analyze the results of the study of Science Olympiad content outline on the subject of Quantities, Units and Measurement. This video was made using the PowerPoint application for the content, and the Ice Cream screen recorder to record. The research method uses a descriptive qualitative method with a sample of all viewers, the majority of whom are junior high school students. This activity can be concluded as effective by looking at the number of viewers reaching more than 28 thousand, the number of likes 920 without dislikes and 40 positive comments from viewers who accessed on March 12, 2025 at 21:37.

Customer churn is a major challenge in e-commerce, directly affecting revenue and profit. This study aims to develop a machine learning model using XGBoost to predict churn probability. To handle class imbalance, SMOTE was applied as a resampling method, and hyperparameter tuning was performed to enhance performance. The model was evaluated using the F2-score, prioritizing recall while maintaining precision. The results show that the XGBoost model with SMOTE achieves strong performance, with an F2-score of 0.849 on the tuned test data. This model can help businesses identify at-risk customers early, enabling proactive retention strategies.

Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.

Kartu Indonesia Pintar Kuliah (KIP-K) is a scholarship given by the government to high school graduates and equivalent who excel but have economic limitations to continue their studies at university level, both at state and private universities. The Indonesian government has issued the KIP-K since 2020 as a form of educational assistance. In North Maluku Province, especially on Sula Island, there is still minimal information regarding KIP-K. This service activity aims to share information regarding the benefits of higher education and how to obtain a KIP-K scholarship for students who have financial limitations. This community service is carried out at SMAN 1 Kepulauan Sula, MAN 1 Kepulauan Sula, SMAN 7 Kepulauan Sula, SMAN 9 Kepulauan Sula and SMAN 11 Kepulauan Sula. This activity was carried out in two stages, namely socialization and mentoring. The implementation of this activity went well, smoothly and was full of enthusiasm from the participants, especially students who wanted to continue their studies at university level. The final result of this socialization and mentoring is that the participants have succeeded in creating their own accounts, filling in data and registering for KIP-K.

Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various significant associations between items that are frequently purchased together. The results provide valuable insights for retailers to develop targeted promotions and improve customer shopping experiences, while emphasizing the importance of selecting the right parameters to obtain accurate and relevant results.

This study analyzes the "NYC TLC Trip Record" dataset for the period January 1, 2023 to January 31, 2023 to understand taxi usage patterns in New York City. The objectives to be achieved in this analysis include: (1) Identify the days and times with the highest demand for taxi services, (2) Identify the boroughs with the highest demand for taxi services. We applied univariate analysis for this analysis. The results show that the day with the highest demand occurs on Tuesday for the densest time occurs in the vulnerable time of 3 pm to 6 pm. The boroughs with the highest taxi demand are Manhattan, Queens, and Brooklyn. This analysis provides the results for NYC TLC to develop a data-driven optimization strategy. This analysis not only helps in identifying demand hotspots but also provides insights for more efficient taxi scheduling and placement. With this analysis, it is expected that more effective pick-up time and location optimization strategies can be developed, thereby improving operational efficiency and customer satisfaction in taxi services in New York City.