Stakeholders of Kampus Mengajar require continuous updates on the participants’ progress across all program batches. Previously, data sharing was managed through numerous spreadsheets categorized by province and city, leading to inefficiency and time consumption. As a data analyst on the team, I developed a centralized dashboard to serve as an information hub for all stakeholders. This interactive platform consolidates data into a single, accessible resource, streamlining communication and enhancing efficiency.
Kampus Mengajar is an initiative that deploys college students to remote schools facing challenges in literacy and numeracy. To ensure these students can make meaningful contributions and foster innovation in their assigned schools, it is imperative they possess a thorough understanding of teaching methodologies and subject matter. To this end, Kampus Mengajar offers an intensive month-long series of Pre-Assignment Classes, designed to prepare participants comprehensively and uphold the highest standards of quality. In my role as a data analyst, I assess the impact and success rate of these Pre-Assignment Classes, providing critical insights to optimize the program for future cohorts.
| View Data Studio | View Presentation |
Kampus Mengajar assigns college students to schools in remote areas with literacy and numeracy challenges. To officially begin their assignments, these students must register with local education authorities as representatives of The Ministry of Education. As a data analyst, I developed an active and dynamic dashboard tracker, integrated with various Google Forms to monitor the report submission progress of each participant. This ensures that all relevant authorities are promptly informed and the process remains efficient and transparent.
Kampus Mengajar is a large-scale initiative encompassing tens of thousands of college students across Indonesia. To sustain the program’s growth annually, it’s imperative to ensure its continued relevance and responsiveness to region-specific challenges and issues. As a data analyst, I conduct a thorough analysis of yearly program registrants to provide insightful recommendations for the board. This analysis aids in crafting updated policies and refining program concepts to drive continuous improvement and development.
Kampus Mengajar is a comprehensive program involving over 20,000 college students, 2,000 lecturers from more than 700 universities, and various regional educational institutions under the Ministry of Education, Culture, Research, and Technology. Timely updates on the program’s progress, particularly regarding fund disbursement, are critical for all stakeholders and participants. As a data analyst for Kampus Mengajar, I developed a dynamic data tracker using Google Data Studio. This platform, updated automatically on a daily basis, consolidates information from multiple division worksheets, significantly reducing the workload of the Stakeholder Relations Division by 70%.
The objective of this project is to develop a strategic business recommendation to enhance the company’s customer acquisition strategy for its travel insurance product. We conducted an exploratory data analysis (EDA) on a dataset of over 20,000 customers using Python. Our approach included feature engineering to derive insights into customer behavior based on their demographics and backgrounds. We employed Seaborn and Pandas for data visualization, implemented a K-means clustering algorithm to create a predictive model, and formulated actionable business recommendations based on our findings.
| View dataset | View code | View presentation |
The objective of this project is to develop a strategic business recommendation for Home Credit Indonesia to enhance the accuracy of their loan acceptance and rejection processes, thereby reducing the number of customers experiencing difficulties with loan repayments. Utilizing a dataset of over 350,000 customers, I performed an exploratory data analysis using Python and SQL, visualized the findings with Matplotlib, and developed a predictive model using Logistic Regression. The outcome is a comprehensive business recommendation aimed at improving decision-making and minimizing loan repayment issues.
| View code | View presentation |