As: Software Developer
- Developed desktop software using C# and SQLite.
- Implemented modules for data storage, retrieval, and manipulation.
- Technology Used: Visual Studio, C#, Oracle Data Modeler, SQLite.
Portfolio | Zainul Muhaimin
A Collection of my favorites project I’ve worked. Feeling great while sharing here.
![Book Archive [Desktop]](https://zaindev.my.id/images/works/Book-Archive.png)
Application
Book Archive [Desktop]
Documentation
CrudCrud News API DocsDocumentation
CrudCrud News API Docs
As: Documentator
- Authored comprehensive API documentation for CRUD operations on news articles, including endpoints for creating, reading, updating, and deleting data.
- Designed and structured documentation using Swagger for Swagger 2 and OpenAPI 3, providing live and interactive API references.
- Drafted step-by-step instructions for API key setup, endpoint configuration, and example usage scenarios to streamline developer integration.
- Ensured clarity and accessibility by outlining essential details such as request methods, parameters, responses, and error codes.
- Tools Used: Git, VS Code, Swagger.
- Live URL: Live Documentation - OAS 3
Website
E-Library![Garden Guard [Backend]](https://zaindev.my.id/images/works/Garden-Guard-Backend.png)
Backend/API
Garden Guard [Backend]Backend/API
Garden Guard [Backend]
As: Backend Developer
- Implemented RESTful API endpoints using Hono for seamless integration with the application frontend.
- Integrated TensorFlow.js models to predict plant diseases from user-submitted data in real-time.
- Optimized backend performance with Bun to ensure fast server-side processing and minimal latency.
- Ensured code versioning, collaboration, and deployment workflows using Git.
- Tools Used: Git, Bun, TensorFlow.js, Hono.
![Garden Guard [ML Model]](https://zaindev.my.id/images/works/Garden-Guard.png)
AI/ML
Garden Guard [ML Model]
As: ML Engineer
- Built and trained CNN models in TensorFlow for classifying plant diseases with 94%–98% accuracy.
- Processed 1,600–3,200 training images and 400–800 validation images per model across 100 epochs.
- Used Adam optimizer (learning rate: 0.001) for efficient training and optimal performance.
- Conducted data preprocessing and visualization with NumPy, Matplotlib, and Seaborn.
- Managed datasets via Google Drive, using Jupyter Notebook and Google Colab for experimentation.
- Tools: Python, TensorFlow, Keras, NumPy, Matplotlib, Scikit-learn, Google Colab.
UI/UX
ProSelfUI/UX
ProSelf
As: UX Researcher
- Collected and analyzed user feedback through surveys, interviews, and focus groups to help guide design decisions.
- Planned with the team to develop user personas, storyboards, and prototypes to help communicate design ideas using the design thinking method.
- Created and administered user research studies that led to design improvements in task flows and user interface.
- Technology Used: Trello, Figma, Microsoft Visio, Maze.
![QRIS Convert [Mobile]](https://zaindev.my.id/images/works/Qris-Convert.png)
Application
QRIS Convert [Mobile]Application
QRIS Convert [Mobile]
As: Fullstack Developer (Application and API Backend)
- Designed and implemented API for converting static QRIS to dynamic QRIS, using Node.JS.
- Create Simple UI Design Using Figma.
- Implemented an Android app using Flutter, incorporating a design from Figma, and seamlessly integrating it with the built API.
- Technology Used: Node.JS, Figma, Flutter SDK.
Website
SIM LPPMWebsite
SIM LPPM
As: QA Engineer & Software Tester
- Created a test plan document, and executed tests to validate software functionality.
- Utilized manual testing using the Black Box method to identify and report bugs to locate and track issues and reduced software bugs by 80%.
- Generated test automation, executed tests, and increased efficiency by 60%.
- Worked closely with developers to diagnose, prioritize, and resolve defects.
- Technology Used: Katalon Studio, Git, Google Docs, Chrome Devtools.