Syafniya Zilfah

Developed and maintained RFM and Revenue dashboards using Looker Studio, analyzing B2C and B2B data to enhance marketing and sales strategies. Designed and implemented a Business Unit return dashboard, tracking product returns to improve customer satisfaction and optimize performance.

Integrated marketplace data into jurnal.id and automated the process using Python, significantly improving data entry efficiency. Conducted comprehensive sales analysis, including GMV, Revenue, and AOV calculations, and performed customer clustering and survey visualization to gain insights into customer behavior.

Processed and analyzed data using Metabase, creating dashboards and providing actionable insights to improve performance. Validated data from various sources, ensuring accuracy and supporting informed decision-making for clients.

Created materials and assignments for students focusing on data mining concepts. Reviewed lessons and lectures with students, providing support and marking assignments to facilitate their learning.

Utilized SQL to compare databases, identify missing data, and troubleshoot related issues, ensuring data integrity. Created visually appealing and interactive dashboards in Tableau, performing forecasting using historical data to identify trends and patterns.

Grade: 3.44/4.00

Activities and societies: Undergraduate Thesis: Classification of Rice Plant Growth Phase Based on Landsat-8 Satellite Multitemporal Image Data with the Method Convolutional Neural Network (Survey Sample Case Study KSA Poso Regency, Central Sulawesi Province)

Completed a Bachelor's Degree in Statistics with a GPA of 3.44/4.00. Undertook an undergraduate thesis on the classification of rice plant growth phases using Landsat-8 satellite multitemporal image data and Convolutional Neural Network method.

Grade: 93.36/100

Activities and societies: Final Project: Prevent Stunting by Calories Detection using Object Detection (YOLOv3)

Participated in the Google Bangkit Academy 2021, specializing in Machine Learning and achieving a score of 93.36/100. Developed a final project focused on preventing stunting through calorie detection using Object Detection (YOLOv3).