irkhamnht (irkhamnht)
Statistics graduate from Diponegoro University and Bangkit 2023 cohort (Machine Learning path), with a strong passion for data analytics, statistical modeling, and transforming data into impactful insights. Enthusiastic learner with hands-on experience in Python, SQL, Tableau, and Microsoft Excel. Eager to grow, collaborate, and contribute to data-driven decisions in real-world projects.
Risk Analysis Python MySQL Microsoft Excel Data Analysis SPSS Microsoft Power BI Machine Learning Data Science
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| User Name: | irkhamnht |
| Account Type: | Personal Account |
| Date Registered: | 02/07/2025 03:57:59 WIB |
| Last Seen: | 10/05/2026 15:08:31 WIB |
| Provinsi: | Jawa Tengah |
| Kabupaten: | Kab. Banyumas |
| Website: | https://github.com/Irkhamaxxel |
| Online Hours: | 11.45 |
| Projects Won: | 0 |
| Projects Completed: | 0 |
| Completion Rate | - |
| Projects Arbitrated: | 0 |
| Arbitration Rate | - |
| Current Projects: | 0 |
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2023: 1. Project Scope
- Build a machine learning model to sorting the CVs of job applicants into fewer ones according to the company's needs
- The application can detect competent job applicant CVs according to the qualifications required by each company
- The application can only detect job applicants CVs based on “keywords” entered by HR of each company in the application
2. Deliverables
- Users as job applicants can use this application to apply for jobs and be able to more efficiently save time
- Users as job applicants can see the qualifications required by each company before sending their CV
2024: This is a local population information system developed for managing administrative data in Repaking Village, built using PHP and MySQL.
Key Features
Admin login
Population data management (births, deaths, migration in/out)
Document and report uploads
Family data management
Technologies Used
PHP
MySQL
HTML/CSS/JavaScript
XAMPP (localhost environment)
2025:
Credit Risk Prediction using Machine Learning
A machine learning project that predicts loan default risk using applicant features such as age, income, credit history, loan characteristics, and past defaults. Models used include Logistic Regression, XGBoost, and Random Forest.
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