Edrick Ernest Sinaga Primary Author mixed material bibliography BEKASI Universitas Satya Negara Indonesia 2025 id Indonesia

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Spam is one of the common issues in email systems that can disrupt user comfort and productivity. This study aims to classify emails into two categories, namely Spam and Non-Spam, using the Decision Tree method based on the Iterative Dichotomiser 3 (ID3) algorithm. The classification process is carried out by designing a dataset consisting of a number of labeled emails and five key attributes, namely Free, Click, Act, Attached, and Details. The main stages in this method involve calculating the entropy, average entropy, and information gain of each attribute to determine which attribute is the most effective in splitting the data. The attribute with the highest information gain is selected as the root node in the construction of the decision tree. The entire process is conducted manually to gain a deeper understanding of how the Iterative Dichotomiser 3 (ID3) algorithm works in classifying data. The final results show that the constructed decision tree model can accurately group emails based on keyword patterns, and provides a strong conceptual understanding of decision-making processes in basic data mining applications. Edrick Ernest Sinaga Teknik Informatika Decision Tree Iterative Dichotomiser 3 (ID3) Entropy Information Gain Email Classification Spam Detection. TI 2025 Institutional Repository USNI Universitas Satya Negara Indonesia TI 2025 8250237 Perpustakaan USNI Kampus B (SKRIPSI) TI 2025 IMPLEMENTASI ALGORITMA DECISION TREE UNTUK MENGKLASIFIKASI EMAIL SPAM DAN NON-SPAM 4290 2025-09-17 13:57:27 2025-09-18 14:26:59 machine generated