WORK PROFESSIONAL

Key Responsibilities and Achievements:

• Led and mentored a core team of 5 Data Analysts, initiating a structured "One-on-One" coaching framework to align personal professional growth with organizational goals.

• Engineered the development of a centralized data architecture (Mastersheet) across 10 divisions, including specialized modules for Content, Promotion, and Leads Management, establishing a Single Source of Truth (SSOT) and improving data retrieval speed by 60%.

• Spearheaded the organizational rollout of the new data system, conducting technical training workshops for Team Leaders across 10 divisions to ensure seamless adoption of the Mastersheet protocols and workflows.

• Supervised the team in deploying interactive BI dashboards using python programming language for Social Media performance, successfully reducing manual reporting latency by 20% and enabling real-time tracking.

• Led the end-to-end project of an Executive Dashboard designed for C-Level (CEO) usage to monitor cross-divisional intern performance, utilizing data visualization to streamline talent evaluation efficiency by 40%.

• Established a robust data governance framework by authoring the official Mastersheet Guide and implementing a "Monthly Audit Data Compliance" system to enforce protocols, ensuring +85% data accuracy across 10 divisions.

• Optimized the lead identification process by filtering and verifying Marketing Qualified Leads (MQLs) for Business Development, resulting in a 20% uplift in high-quality prospect acquisition.

• Conducted diagnostic analysis on market trends to prescribe content strategies, directly contributing to a 20% increase in engagement through data-optimized content.

• Conducted comprehensive trend analysis and survey research to uncover audience preferences, providing actionable recommendations that guided product and content strategies.
  
  

Impact and Value Added:

• Transformed a fragmented data landscape (reducing excessive spreadsheet sprawl) into a centralized ecosystem with structured Google Drive architecture, cutting down administrative search time by 65%.

• Bridged the gap between raw data and business strategy across all divisions, empowering stakeholders to make high-confidence decisions based on verified metrics, resulting in a +90% shift towards data-driven culture.

• Standardized project tracking and documentation (Mastersheet Guide), creating a sustainable framework that saved 10+ hours of manual work weekly and ensured operational continuity for future teams.

Key Responsibilities and Achievements:

• Designed and executed data compilation and analysis of survey and FGD results to support financial education products, contributing to a 20% improvement in targeted content development.

• Extracted and analyzed user behavior and financial literacy data to inform content strategies across investment, personal finance, insurance, and entrepreneurship, driving a 15% increase in user engagement.

• Performed market and user data analysis to optimize class and webinar sales performance.

• Designed and implemented 20 financial calculators across 6 categories using Python, packaged into a web-based platform that enhanced user interaction by 60% and received 30+ positive reviews in the first week.

• Initiated and prototyped SOKOKU, an Android-based budgeting tracker application with features such as financial dashboards, transaction records, and financial goals, projected to increase planning efficiency by 35%.

• Built a comprehensive Project Tracker for the Data Analyst Division using BI dashboards, task trackers, and Gantt charts, improving project management efficiency by 50%.

• Conducted 10+ social media trend analyses, providing actionable insights that led to a 20% increase in engagement and reach.

• Trusted by the CEO to lead a personal data-driven project, showcasing strong reliability and execution skills.
  
  

Impact and Value Added:

• Established scalable digital assets (financial calculators & SOKOKU app) that continue to drive sustained engagement.

• Standardized project tracking and reporting systems, reducing manual effort by 30% and creating a sustainable framework for future data initiatives.

• Enhanced brand credibility and trust through data-backed strategies, reflected in 90% positive stakeholder and user feedback.

Key Responsibilities and Achievements:

• Led hands-on lab sessions for 30+ students, focusing on practical applications of MySQL using XAMPP, ensuring a strong foundation in database management.

• Designed 20+ real-world case studies, bridging theory and practice, which improved student problem-solving skills by 25%, as measured by lab performance assessments.

• Developed and delivered structured materials covering DDL, DML, DCL, JOIN, UNION, ERD, Logical Functions, Data Filtering, and Subqueries, leading to a 15% increase in student comprehension, based on mid-term evaluations

• Implemented interactive learning methods, fostering a 40% improvement in engagement and participation, as tracked by session attendance and feedback forms.

• Conducted performance analytics to monitor student progress, identifying and addressing learning gaps, resulting in a 15% increase in overall lab scores.

• Provided individualized feedback and guidance, ensuring 80% of students reported increased confidence in applying SQL for database management and optimization.
  
  

Course Highlights:

• Designed and delivered 12+ structured lab sessions, integrating SQL theory with industry-relevant case studies, significantly enhancing hands-on learning outcomes.

• Introduced data-driven assessment techniques, ensuring fair grading and constructive feedback that directly contributed to student success in database management applications.

Key Responsibilities & Learning Outcomes:

Completed a project-based internship focusing on the application of data science to solve real-world problems in the hospitality sector.
Acquired expertise in data analysis and machine learning, including:

• Basic Python for Data Cleaning & Machine Learning: Proficient in preprocessing and cleaning raw datasets.

• Classification to Machine Learning: Applied supervised learning techniques to improve predictive accuracy.

• Introduction to Machine Learning: Developed foundational knowledge of machine learning principles and practical use cases.

Highlight Project: Eye Disease Classification Using Deep Learning

Designed a solution to detect eye diseases such as cataracts, diabetic retinopathy, and glaucoma using deep learning. This initiative addressed early diagnosis challenges in healthcare, aiming to reduce preventable blindness.

Methodology:

• Conducted data exploration and preprocessing, including resizing, contrast enhancement, and data augmentation.

• Fine-tuned a pre-trained ResNet50 deep learning model for multi-class image classification.

• Evaluated performance using metrics such as accuracy, F1-score, and recall to ensure robust and reliable outputs.

Achievements:

• Reached 86.71% validation accuracy and 81% test accuracy for disease classification.

• Demonstrated high precision in identifying diabetic retinopathy, providing insights for model improvement in other disease categories.

• Delivered a solution with potential applications in clinical diagnostics and public health screenings, particularly in underserved regions.

Mentoring Engagements:

• Participated in mentoring sessions with industry professionals, gaining practical insights into machine learning applications and best practices.

• Enhanced technical skills and refined approaches to solve data-driven challenges in a professional setting.

Key Responsibilities and Achievements:

• Designed and implemented data-driven lab assignments for 19 students, covering foundational topics like Arrays, Data Structures, Sorting & Searching, Pointers, Linked Lists, Queues, Stacks, and Trees using C++. Achieved a 25% increase in student lab performance as tracked through assignment scores.

• Leveraged analytics to monitor and analyze student performance trends across 11 lab sessions, identifying areas needing reinforcement, which resulted in a 15% improvement in the class’s overall comprehension of core data structures concepts.

• Applied data science and business intelligence principles to collect feedback, helping refine lab content and ensuring 90% of students reported improved confidence in applying data structures principles to real-world problems.

• Demonstrated strong communication skills by leading lab sessions, explaining complex concepts clearly, and providing relatable examples, resulting in a 95% student satisfaction rate in end-of-term feedback.

• Collaborated with the professor to optimize curriculum alignment with student learning needs, contributing feedback that enhanced lab relevance and application, and directly improved student outcomes by 20% on key assessments.

• Conducted performance analysis to ensure fair and consistent grading, providing constructive, data-informed feedback that supported student progress and motivation.
  
  

Course Highlights:

• Designed 11 lab sessions that effectively bridged theory and practice in C++, using targeted problem-solving exercises, resulting in a 30% increase in lab engagement and hands-on application proficiency among students.

Project-Based Virtual Intern : Business Intelligent Analyst Bank Muamalat x Rakamin Academy
As a Qualified Student, I gained valuable experience in data analysis and business intelligence.

Key Skills and Knowledge Acquired:

• Proficient in Microsoft Excel, including advanced formulas and pivot tables for efficient data manipulation.

• Familiar with database management systems, RDBMS concepts, and SQL functions using Google Big Query.

• Skilled in data analysis techniques and statistical methods to understand data patterns and forecast trends.

• Capable of implementing data information into interactive dashboards for effective visualization and reporting.

Project-Based Virtual Intern : Big Data Analyst Kimia Farma x Rakamin Academy
As a Qualified Student, I gained valuable hands-on experience in data analysis and big data technologies.

Key Skills and Knowledge Acquired:

• Proficient in understanding and utilizing various database systems, including Data Lake, Data Warehouse, and Data Mart.

• Grasped the fundamental concepts of big data and its practical applications in the industry.

• Familiar with cloud computing fundamentals and their role in big data solutions.

• Skilled in RDBMS operations, SQL queries, and statistical analysis.

• Experienced in data analysis methods, data visualization techniques, and forecasting models.

• Capable of implementing data information into interactive dashboards for effective insights.

Key Responsibilities and Measurable Achievements:

• Leveraged data analysis skills to process over 500 financial data points related to invoices, achieving a 30% reduction in payment delays through timely and data-driven payment reminders, ensuring consistent financial compliance across Operation Area 5.

• Utilized data management techniques to organize and maintain a structured archive of more than 700 records, improving data accessibility and retrieval times by 40%, which directly supported audit processes and enhanced operational efficiency.

• Analyzed 20+ cooperation contracts to ensure adherence to contract values and identify potential financial risks, reducing non-compliance incidents by 15% and contributing to improved risk management protocols.

• Streamlined the verification process for contracts and invoices by implementing database-driven solutions and PPF printing, enhancing overall efficiency by 25% and reducing manual processing errors by 20%.

• Applied business intelligence insights to enhance contract and payment workflow efficiencies, identifying bottlenecks that, once addressed, resulted in a 20% increase in the accuracy and timeliness of financial reporting.
  
  

Impact and Value Added:

• Strengthened financial data integrity and accuracy, supporting strategic decision-making with reliable data insights.

• Contributed to a more data-driven approach within the finance department, aligning financial operations with key compliance and risk management objectives.

• Collaborated with mentors to streamline Training Class operations, using data insights to optimize session scheduling and resource allocation, enhancing the efficiency and satisfaction of participants.

• Managed all facets of the Training Class, including tracking participant engagement and performance metrics to identify and address learning needs, improving overall training outcomes.

• Analyzed feedback data from participants to provide actionable insights on class structure and content, contributing to a 15% improvement in participant satisfaction.

• Monitored infrastructure and facilities, using data to anticipate and address logistical needs, ensuring an uninterrupted and high-quality learning environment.

• Established and maintained positive relationships with mentors and participants, leveraging data to foster a collaborative environment tailored to individual learning preferences and attributes.