Tutorial Naive Bayes Classifier Dari Konsep hingga Implementasi

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Learn to implement a Naive Bayes Classifier from concept to implementation using Python and understand the probabilistic approach and Bayes' theorem

intermediate Published 6 May 2026

Action Steps

Understand the probabilistic approach and eager learning in Naive Bayes Classifier
Learn Bayes' theorem and its components: prior, likelihood, evidence, and posterior
Apply the conditional independence assumption in Naive Bayes Classifier
Implement a Naive Bayes Classifier using Python and evaluate its performance
Use the classifier to predict the class of new, unseen data

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this tutorial to improve their classification skills and understand the underlying concepts of Naive Bayes Classifier

Key Insight

💡 Naive Bayes Classifier uses a probabilistic approach and Bayes' theorem to classify data, with the assumption of conditional independence between features

Key Takeaways

Learn to implement a Naive Bayes Classifier from concept to implementation using Python and understand the probabilistic approach and Bayes' theorem

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Title: Tutorial Naive Bayes Classifier Dari Konsep hingga Implementasi

URL Source: https://medium.com/@ibnu.yakinn/tutorial-naive-bayes-classifier-dari-konsep-hingga-implementasi-85ba886ff460?source=rss------python-5

Published Time: 2026-05-06T15:55:05Z

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# Tutorial Naive Bayes Classifier Dari Konsep hingga Implementasi | by Ibnu Yakinn | May, 2026 | Medium

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# Tutorial Naive Bayes Classifier Dari Konsep hingga Implementasi

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**Pendekatan Probabilistik**

Klasifikasi data memiliki dua pendekatan utama yaitu _probabilistik_ dan _instance based._ Naive Bayes menggunakan pendekatan _probabilistik_. Algoritma ini menghitung peluang sebuah data masuk ke kelas tertentu menggunakan teori probabilitas. Sistem langsung membangun model saat proses pelatihan data berjalan. Pendekatan pembuatan model ini bernama eager learning.

**Teorema Bayes dan Asumsi Dasar**

Konsep utama algoritma ini berasal dari Teorema Bayes.

![Image 4](https://miro.medium.com/v2/resize:fit:469/1*vOG-Z6D9ntAtFt0WMbMXrg.png)

Rumus tersebut memiliki empat komponen utama yang saling terhubung.

1. _Prior_ adalah probabilitas awal sebuah kelas sebelum sistem melihat data.
2. _Likelihood_ adalah probabilitas fitur data pada kelas tertentu.
3. _Evidence_ adalah faktor pembagi atau normalisasi.
4. _Posterior_ adalah hasil akhir probabilitas data tersebut masuk ke kelas tertentu.

Sistem menggunakan asumsi utama bernama _conditional independence_.

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Tutorial Naive Bayes Classifier Dari Konsep hingga Implementasi

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