Implementasi Algoritma Pembelajaran Mesin

Tugas Besar 2 IF3170 Inteligensi Artifisial

Implementasi Algoritma Pembelajaran Mesin KNN, Naive Bayes, dan ID3

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Made By:

Kelompok 25:

AK48

NIM	Nama
13522020	Aurelius Justin Philo Fanjaya
13522071	Bagas Sambega Rosyada
13522090	Fedrianz Dharma
13522091	Raden Francisco Trianto B.

External Links

• Spesifikasi
• QNA
• Laporan

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About The Project

This program created to implement 3 Supervised Learning algorithms, K-Nearest Neighbor (KNN), Naive Bayes, and Iterative Dichhotomizer 3 (ID3) from scratch.

Dataset used is dataset UNSW-NB15, contains lists of cyber attack and normal condition of network traffic.

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Getting Started

Prerequisites

Project dependencies

• Python

  # in Linux
  sudo apt install python3
  
  # Or other OS
  https://www.python.org/

• Jupyter notebook

  pip install jupyter
  pip install notebook

• Other Python libraries (install using pip): sklearn, scipy, matplotlib, numpy, pandas, joblib, pickle

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Installation

How to install and use your project

1. Clone the repo

   git clone https://github.com/FedrianzD/Tubes-2-AI.git

2. Change directory

   cd Tubes-2-AI

3. Run the Notebook Program

   python3 src/Notebook.py

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Instruction

To run the program you can simply run all in the src/Notebook.py file.

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Features

1. Exploratory Data Analysis

2. Data Validation and Preprocessing

3. Model Prediction

• KNN
• Naive Bayes ID3

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Task Distribution

NIM	Tugas
13522020	Implementasi ID3, preprocessing, modelling dan pipeline
13522071	Implementasi Naive Bayes, pipelining
13522090	Implementasi KNN, data validation dan modelling
13522091	Inisialisasi notebook, EDA, preprocessing, data validation dan pipelining

Contributing

If you want to contribute or further develop the program, please fork this repository using the branch feature.
Pull Request is permited and warmly welcomed

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Licensing

The code in this project is licensed under MIT license.

THANK YOU!