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F.E.T.I.

F.E.T.I.

Faiz · Eleanor · Thalita · Irfan

Group 16 — two projects in search, optimization, and classification.

IF3070 Foundations of Artificial Intelligence · STEI ITB · 2024/2025-1

Projects

Magic Cube Solver

Six search algorithms racing a 5×5×5 diagonal magic cube — replayed move by move.

A 5×5×5 diagonal magic cube has 125 cells that must be a permutation of 1..125, with all 109 rows, columns, pillars and diagonals summing to 315. Six local-search and metaheuristic algorithms attack it server-side in Go, and the browser replays the resulting swap log on a scrubber against a live objective-value plot.

Algorithms

  • Steepest Ascent Hill Climbing
  • Hill Climbing with Sideways Move
  • Random Restart Hill Climbing
  • Stochastic Hill Climbing
  • Simulated Annealing
  • Genetic Algorithm

Built with

  • Go
  • Fiber
  • SvelteKit
  • Three.js
  • Chart.js
  • Tailwind
  • Vite
  • Docker

The problem

125
cells, a permutation of 1..125
109
sums that must all match
315
the magic constant

Objective functions are minimised, so zero means solved. A seed makes any run reproducible bit for bit.

Phishing URL Classifier

Two algorithms, each written twice — and the baseline that keeps them honest.

The UCI PhiUSIIL corpus runs 140,404 URLs deep and only 7.5% of it is hostile — which is exactly why accuracy flatters here and recall does the real work. KNN and Gaussian Naive Bayes are each built twice across its 49 features, once from scratch and once from scikit-learn, so every number has a reference to answer to.

Algorithms

  • K-Nearest Neighbors (from scratch)
  • K-Nearest Neighbors (scikit-learn)
  • Gaussian Naive Bayes (from scratch)
  • Gaussian Naive Bayes (scikit-learn)
  • EDA
  • Feature Engineering
  • SMOTE

Built with

  • Python
  • scikit-learn
  • NumPy
  • pandas
  • SciPy
  • Streamlit
  • Jupyter
  • Docker

Results (validation, 28,081 rows)

98.1%
accuracy KNN, scikit-learn
0.763
phishing recall the metric that moves
92.5%
baseline constant predictor

A constant “legitimate” predictor scores 92.5%. Coursework reimplementation — not a security product. Do not use it to decide whether a link is safe.

Team