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The development of digital technology has brought about significant transformations in the global entertainment industry, including in Indonesia. One manifestation of this change is evident in the presence of streaming platforms like Netflix, which have altered consumer consumption patterns for audio-visual content. This study aims to analyze how Netflix Indonesia utilizes Business Intelligence (BI) and Knowledge Management (KM) to maintain and increase customer loyalty. This research uses a qualitative descriptive method, collecting data from various scientific literature, industry reports, and relevant online sources. The results show that the implementation of BI enables Netflix to analyze user behavior, understand viewing preferences, and provide more personalized content recommendations. Meanwhile, KM plays a crucial role in internal knowledge management, content development, and service innovation. The synergy between BI and KM has been proven to support Netflix's strategy in improving user experience, retaining existing customers, and attracting new ones in the increasingly competitive Indonesian market.

This study presents the integration of a Large Language Model (LLM) Ollama with the OpenStreetMap (OSM) API within a Business Intelligence (BI) framework to develop an intelligent, location-based recommendation system. The system is designed to assist users in finding dining, leisure, and resting places through natural language interaction and contextual understanding. The LLM interprets user input semantically, transforms it into structured spatial queries, and retrieves relevant geospatial data from OSM. The data are then analyzed, categorized, and visualized using BI methods to enhance interpretability and decision-making. The system was implemented using Next.js, Leaflet.js, ensuring interactivity and scalability for web-based deployment. Technical evaluation focused on system accuracy, response time, and output consistency. Results demonstrate an average response time of 1.74 seconds, 80% accuracy, and 80% consistency, proving the model’s efficiency in producing relevant, context-aware recommendations. This integration highlights the potential of combining open geospatial data, local LLMs, and BI analytics to create intelligent, data-driven decision support systems applicable to tourism, urban planning, and spatial information management.

Education is used as a means of improving human resources. In increasing educational progress, a data management system is needed such as student, class, educator and education data, as well as recapitulation of school data reports for early childhood education levels. This Early Childhood Monitoring Information System was built for the process of monitoring Early Kindergarten (TK) reports to control student activities at school and make it easier to provide information on the development of children's activities that are reported in student activities, without having to use the old system by recording all student progress. manually because it is less efficient in its use and makes it easier for teachers to monitor students. The method used in system development is prototyping. The use of information systems makes it easier for teachers to input monitoring data and results of grades so that teacher users can easily monitor the progress of their students.  

Fraud in digital transactions has become a serious issue threatening the security and integrity of the fintech and e-commerce sectors. To address this problem, machine learning technology has emerged as an effective solution for automatically detecting anomalies and fraudulent transactions. This study aims to analyze the application of machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and Ensemble Learning, in detecting fraud in digital transactions. The research adopts a quantitative approach with experimentation, testing the effectiveness of the three algorithms using a digital transaction dataset consisting of both fraudulent and non-fraudulent transactions. The results show that the Random Forest algorithm performs the best in terms of accuracy and recall, followed by Ensemble Learning, which enhances fraud detection performance by combining multiple prediction models. Meanwhile, SVM demonstrates satisfactory performance but is prone to overfitting issues when handling large and complex datasets. The study also finds that the problem of imbalanced data can affect model accuracy, and data balancing techniques such as oversampling are required to improve fraud detection performance. Overall, the findings suggest that machine learning, particularly Random Forest and Ensemble Learning algorithms, can be relied upon to improve fraud detection in digital transactions. However, challenges such as model interpretability and the need for periodic algorithm updates still need to be addressed to enhance the effectiveness of fraud prevention systems in countering the ever-evolving nature of fraud.