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Implementation of KNN and Gaussian Naive-Bayes algorithms to classify phishing URLs. Built from scratch and compared with scikit-learn versions.

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IF3070_TugasBesar2: Implementasi Algoritma Pembelajaran Mesin

Tentang Proyek

Tugas Besar 2 IF3070 Dasar Inteligensi Buatan bertujuan untuk memberikan pengalaman langsung dalam menerapkan algoritma pembelajaran mesin pada permasalahan nyata dengan mengimplementasikan algoritma K-Nearest Neighbour (KNN) dan Gaussian Naive Bayes. Implementasi ini dilakukan dengan menggunakan Python dan Jupyter Notebook.

Implementasi Proyek

  • Split Data
  • Data Cleaning & Preprocessing
  • Modeling & Validation (KNN & Gaussian Naive Bayes)

Cara Kompilasi Program

  1. Clone Repository Github dan cd ke Folder src
git clone https://github.com/aninditaws/IF3070_TugasBesar2.git
cd src
  1. Pastikan Anda sudah mengunduh Python, Jupyter, Notebook, dan pip
pip install jupyter
pip install notebook
python -m ensurepip --upgrade
  1. Setup Virtual Environment
python -m venv env
env\Scripts\activate
  1. Run File Jupyter Notebook IF3070_DAI_TugasBesar2_finalboss.ipynb

Struktur Program

│ README.md
│
├── data
│   ├── sample_submission.csv   
│   ├── test.csv               
│   └── train.csv              
│
├── docs
│   └── 
│
├── src
│   └── IF3070_DAI_TugasBesar2_finalb.ipynb   
│
└───

Pembagian Tugas

NIM Nama Lengkap Tugas
18222113 Angelica Aliwinata Modelling Naive Bayes
18222116 Jason Jahja Modelling KNN
18222123 Melissa Trenggono Modelling Naive Bayes
18222128 Anindita Widya Santoso Data Cleaning & Preprocessing, Compile Pipeline

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Implementation of KNN and Gaussian Naive-Bayes algorithms to classify phishing URLs. Built from scratch and compared with scikit-learn versions.

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