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biobii/naive-bayes-text-classifier
==================================

Text classification using Naive Bayes approach

141785[1 issues](https://github.com/biobii/naive-bayes-text-classifier/issues)[1 PRs](https://github.com/biobii/naive-bayes-text-classifier/pulls)PHP

Since Sep 3Pushed 6y ago1 watchersCompare

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READMEChangelogDependenciesVersions (1)Used By (0)

Naive Bayes Text Classifier
===========================

[](#naive-bayes-text-classifier)

Library untuk klasifikasi teks Bahasa Indonesia menggunakan algoritma Naive Bayes Classifier (NBC). Proses stemming pada package ini menggunakan library [Sastrawi](https://github.com/sastrawi/sastrawi).

Cara Penggunaan
---------------

[](#cara-penggunaan)

Install menggunakan perintah `composer require biobii/naive-bayes-text-classifier`.

Menyiapkan data training. Bentuk data harus mengikuti seperti contoh berikut. Nilai pada key `class` dapat disesuaikan sesuai kebutuhan.

```
$data = [
    [
        'text' => 'Filmnya bagus, saya suka',
        'class' => 'positif'
    ],
    [
        'text' => 'Film jelek, aktingnya payah.',
        'class' => 'negatif'
    ],
];
```

Berikut contoh lengkap penggunaan.

```
require __DIR__ . '/vendor/autoload.php';

use Biobii\NaiveBayes;

$data = [
    [
        'text' => 'Filmnya bagus, saya suka',
        'class' => 'positif'
    ],
    [
        'text' => 'Filmnya menarik, aktingnya bagus',
        'class' => 'positif'
    ],
    [
        'text' => 'Saya suka film ini sangat keren',
        'class' => 'positif'
    ],
    [
        'text' => 'Film jelek, aktingnya payah.',
        'class' => 'negatif'
    ],
    [
        'text' => 'Kecewa, ini adalah film terburuk yang pernah saya tonton',
        'class' => 'negatif'
    ],
];

$nb = new NaiveBayes();

// mendefinisikan class target sesuai dengan yang ada pada data training.
$nb->setClass(['positif', 'negatif']);

// proses training
$nb->training($data);

// pengujian
echo $nb->predict('alur ceritanya jelek dan aktingnya payah'); // output "negatif"
```

###  Health Score

22

—

LowBetter than 21% of packages

Maintenance19

Infrequent updates — may be unmaintained

Popularity20

Limited adoption so far

Community6

Small or concentrated contributor base

Maturity36

Early-stage or recently created project

How is this calculated?**Maintenance (25%)** — Last commit recency, latest release date, and issue-to-star ratio. Uses a 2-year decay window.

**Popularity (30%)** — Total and monthly downloads, GitHub stars, and forks. Logarithmic scaling prevents top-heavy scores.

**Community (15%)** — Contributors, dependents, forks, watchers, and maintainers. Measures real ecosystem engagement.

**Maturity (30%)** — Project age, version count, PHP version support, and release stability.

### Community

Maintainers

![](https://www.gravatar.com/avatar/997ad0171dc49913d2a17e68db838b1ca09cdb6fd0e7081bf82ba67771ec348f?d=identicon)[biobii](/maintainers/biobii)

---

Tags

machine-learningnaive-bayesnaive-bayes-algorithmnaive-bayes-classifiertext-classification

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