Tutorial Lengkap Label Studio: Data Labeling untuk Machine Learning

Label Studio adalah platform data labeling open-source untuk machine learning. Platform ini mendukung labeling berbagai tipe data termasuk teks, gambar, audio, video, dan time series, menjadikannya essential untuk membangun dataset training ML berkualitas tinggi.

Mengapa Label Studio?

Keunggulan Label Studio:

Multi-modal: Label teks, gambar, audio, video, HTML
Fleksibel: Interface labeling yang customizable
Kolaboratif: Workflow anotasi berbasis tim
Integrasi: ML backends, cloud storage, webhooks
Open source: Self-hosted dengan opsi enterprise

Use Cases:

Anotasi NLP (NER, sentiment, classification)
Computer vision labeling (bounding boxes, segmentation)
Transkripsi dan klasifikasi audio
Anotasi data multi-modal
Workflow active learning

Instalasi

# Install dengan pip pip install label-studio Start Label Studio label-studio start Atau dengan Docker docker run -it -p 8080:8080 \ -v $(pwd)/mydata:/label-studio/data \ heartexlabs/label-studio:latest Akses di http://localhost:8080

Quick Start

1. Buat Project

from labelstudiosdk import Client

Koneksi ke Label Studio
ls = Client(url='http://localhost:8080', apikey='your-api-key')


Buat project
project = ls.startproject(
    title='Analisis Sentimen',
    labelconfig='''

    
      
      
        
        
        
      
    
    '''
)

print(f"Project dibuat: {project.id}")

2. Import Data

# Import dari list
tasks = [
    {"text": "Saya suka produk ini!"},
    {"text": "Ini sangat buruk."},
    {"text": "Biasa saja, tidak istimewa."}
]

project.importtasks(tasks)

Import dari file
project.importtasks('data.json')


Import dari URL
project.importtasks([
    {"image": "https://example.com/image1.jpg"},
    {"image": "https://example.com/image2.jpg"}
])

3. Export Anotasi

# Export semua anotasi
annotations = project.exporttasks()


Export dalam format spesifik
annotations = project.exporttasks(exporttype='JSON')

annotations = project.exporttasks(exporttype='CSV')

annotations = project.exporttasks(exporttype='COCO')

annotations = project.exporttasks(exporttype='YOLO')


Simpan ke file
import json
with open('annotations.json', 'w') as f:
    json.dump(annotations, f)

Konfigurasi Label

1. Klasifikasi Teks

2. Named Entity Recognition

3. Klasifikasi Gambar

4. Object Detection (Bounding Boxes)

5. Image Segmentation

6. Transkripsi Audio

<p> rows="4" editable="true" maxSubmissions="1"/></p> <p></View></p> <p></code></pre></p> <h3 id="7-klasifikasi-audio-dengan-regions">7. Klasifikasi Audio dengan Regions</h3> <pre><code><View> <p> <Labels name="label" toName="audio"></p> <p> <Label value="Ucapan"/></p> <p> <Label value="Musik"/></p> <p> <Label value="Noise"/></p> <p> </Labels></p> <p> <Audio name="audio" value="$audio"/></p> <p></View></p> <p></code></pre></p> <h3 id="8-anotasi-video">8. Anotasi Video</h3> <pre><code><View> <p> <Video name="video" value="$video"/></p> <p> <VideoRectangle name="box" toName="video"/></p> <p> <Labels name="label" toName="video"></p> <p> <Label value="Orang"/></p> <p> <Label value="Kendaraan"/></p> <p> </Labels></p> <p></View></p> <p></code></pre></p> <h2 id="operasi-sdk">Operasi SDK</h2> <h3 id="1-manajemen-project">1. Manajemen Project</h3> <pre><code>from label</em>studio<em>sdk import Client <p>ls = Client(url='http://localhost:8080', api</em>key='your-api-key')</p> <h1>List semua projects</h1> <p>projects = ls.get<em>projects()</p> <p>for p in projects:</p> <p> print(f"{p.id}: {p.title}")</p> <h1>Get project spesifik</h1> <p>project = ls.get</em>project(project<em>id=1)</p> <h1>Update project</h1> <p>project.set</em>params(</p> <p> title="Judul Baru",</p> <p> description="Deskripsi baru"</p> <p>)</p> <h1>Hapus project</h1> <p>project.delete()</p> <p></code></pre></p> <h3 id="2-manajemen-task">2. Manajemen Task</h3> <pre><code># Get semua tasks <p>tasks = project.get<em>tasks()</p> <h1>Get task spesifik</h1> <p>task = project.get</em>task(task<em>id=1)</p> <h1>Update task</h1> <p>project.update</em>task(</p> <p> task<em>id=1,</p> <p> data={"text": "Teks diupdate"}</p> <p>)</p> <h1>Hapus task</h1> <p>project.delete</em>task(task<em>id=1)</p> <h1>Get tasks dengan filter</h1> <p>from label</em>studio<em>sdk.data</em>manager import Filters, Column, Operator, Type</p> <p>filters = Filters.create(Filters.AND, [</p> <p> Filters.item(</p> <p> Column.completed<em>at,</p> <p> Operator.EMPTY,</p> <p> Type.Datetime,</p> <p> Filters.value(True)</p> <p> )</p> <p>])</p> <p>unlabeled</em>tasks = project.get<em>tasks(filters=filters)</p> <p></code></pre></p> <h3 id="3-manajemen-anotasi">3. Manajemen Anotasi</h3> <pre><code># Buat anotasi <p>annotation = project.create</em>annotation(</p> <p> task<em>id=1,</p> <p> result=[</p> <p> {</p> <p> "from</em>name": "sentiment",</p> <p> "to<em>name": "text",</p> <p> "type": "choices",</p> <p> "value": {"choices": ["Positif"]}</p> <p> }</p> <p> ]</p> <p>)</p> <h1>Get anotasi untuk task</h1> <p>annotations = project.get</em>annotations(task<em>id=1)</p> <h1>Hapus anotasi</h1> <p>project.delete</em>annotation(annotation<em>id=1)</p> <p></code></pre></p> <h3 id="4-upload-prediksi">4. Upload Prediksi</h3> <pre><code># Upload prediksi untuk pre-labeling <p>predictions = [</p> <p> {</p> <p> "task": 1,</p> <p> "result": [</p> <p> {</p> <p> "from</em>name": "sentiment",</p> <p> "to<em>name": "text",</p> <p> "type": "choices",</p> <p> "value": {"choices": ["Positif"]}</p> <p> }</p> <p> ],</p> <p> "score": 0.95</p> <p> }</p> <p>]</p> <p>project.create</em>predictions(predictions)</p> <h1>Bulk prediction upload</h1> <p>model<em>predictions = []</p> <p>for task in project.get</em>tasks():</p> <p> pred = model.predict(task['data']['text'])</p> <p> model<em>predictions.append({</p> <p> "task": task['id'],</p> <p> "result": [{</p> <p> "from</em>name": "sentiment",</p> <p> "to<em>name": "text",</p> <p> "type": "choices",</p> <p> "value": {"choices": [pred['label']]}</p> <p> }],</p> <p> "score": pred['confidence']</p> <p> })</p> <p>project.create</em>predictions(model<em>predictions)</p> <p></code></pre></p> <h2 id="integrasi-cloud-storage">Integrasi Cloud Storage</h2> <h3 id="1-amazon-s3">1. Amazon S3</h3> <pre><code># Koneksikan S3 storage <p>storage = project.connect</em>s3<em>import</em>storage(</p> <p> bucket='my-bucket',</p> <p> prefix='images/',</p> <p> aws<em>access</em>key<em>id='AKIAXXXXXXXX',</p> <p> aws</em>secret<em>access</em>key='xxxxxxxxxx',</p> <p> region<em>name='us-east-1',</p> <p> recursive</em>scan=True</p> <p>)</p> <h1>Sync storage</h1> <p>storage.sync()</p> <h1>Export ke S3</h1> <p>export<em>storage = project.connect</em>s3<em>export</em>storage(</p> <p> bucket='my-bucket',</p> <p> prefix='annotations/',</p> <p> aws<em>access</em>key<em>id='AKIAXXXXXXXX',</p> <p> aws</em>secret<em>access</em>key='xxxxxxxxxx'</p> <p>)</p> <p></code></pre></p> <h3 id="2-google-cloud-storage">2. Google Cloud Storage</h3> <pre><code># Koneksikan GCS storage <p>storage = project.connect<em>google</em>import<em>storage(</p> <p> bucket='my-bucket',</p> <p> prefix='data/',</p> <p> google</em>application<em>credentials='path/to/credentials.json'</p> <p>)</p> <p>storage.sync()</p> <p></code></pre></p> <h3 id="3-azure-blob-storage">3. Azure Blob Storage</h3> <pre><code># Koneksikan Azure storage <p>storage = project.connect</em>azure<em>import</em>storage(</p> <p> container='my-container',</p> <p> prefix='images/',</p> <p> account<em>name='myaccount',</p> <p> account</em>key='xxxxxxxxxx'</p> <p>)</p> <p>storage.sync()</p> <p></code></pre></p> <h2 id="integrasi-ml-backend">Integrasi ML Backend</h2> <h3 id="1-buat-ml-backend">1. Buat ML Backend</h3> <pre><code># ml<em>backend.py <p>from label</em>studio<em>ml import LabelStudioMLBase</p> <p>class SentimentModel(LabelStudioMLBase):</p> <p> def <strong>init</strong>(self, <strong>kwargs):</p> <p> super(SentimentModel, self).<strong>init</strong>(</strong>kwargs)</p> <p> # Load model Anda</p> <p> from transformers import pipeline</p> <p> self.model = pipeline("sentiment-analysis")</p> <p> def predict(self, tasks, <strong>kwargs):</p> <p> predictions = []</p> <p> for task in tasks:</p> <p> text = task['data']['text']</p> <p> result = self.model(text)[0]</p> <p> label = "Positif" if result['label'] == 'POSITIVE' else "Negatif"</p> <p> predictions.append({</p> <p> 'result': [{</p> <p> 'from</em>name': 'sentiment',</p> <p> 'to<em>name': 'text',</p> <p> 'type': 'choices',</p> <p> 'value': {'choices': [label]}</p> <p> }],</p> <p> 'score': result['score']</p> <p> })</p> <p> return predictions</p> <p> def fit(self, annotations, </strong>kwargs):</p> <p> # Train model dengan anotasi baru</p> <p> pass</p> <p></code></pre></p> <h3 id="2-jalankan-ml-backend">2. Jalankan ML Backend</h3> <pre><code># Install ML backend <p>pip install label-studio-ml</p> <h1>Initialize backend</h1> <p>label-studio-ml init my</em>backend --script ml<em>backend.py</p> <h1>Start backend</h1> <p>label-studio-ml start my</em>backend -p 9090</p> <p></code></pre></p> <h3 id="3-koneksi-ke-label-studio">3. Koneksi ke Label Studio</h3> <pre><code># Tambahkan ML backend ke project <p>project.connect<em>ml</em>backend(</p> <p> url='http://localhost:9090',</p> <p> title='Sentiment Model'</p> <p>)</p> <h1>Trigger prediksi</h1> <p>project.start<em>predictions()</p> <p></code></pre></p> <h2 id="active-learning">Active Learning</h2> <h3 id="1-setup-active-learning">1. Setup Active Learning</h3> <pre><code>from label</em>studio<em>ml import LabelStudioMLBase <p>import numpy as np</p> <p>class ActiveLearningModel(LabelStudioMLBase):</p> <p> def predict(self, tasks, <strong>kwargs):</p> <p> predictions = []</p> <p> for task in tasks:</p> <p> # Get prediksi model dengan uncertainty</p> <p> pred, confidence = self.model.predict</em>with<em>uncertainty(task['data'])</p> <p> predictions.append({</p> <p> 'result': [{</p> <p> 'from</em>name': 'label',</p> <p> 'to<em>name': 'text',</p> <p> 'type': 'choices',</p> <p> 'value': {'choices': [pred]}</p> <p> }],</p> <p> 'score': confidence</p> <p> })</p> <p> return predictions</p> <p> def get</em>tasks<em>to</em>label(self, n=10):</p> <p> # Get tasks diurutkan berdasarkan uncertainty</p> <p> tasks = self.project.get<em>tasks()</p> <p> predictions = self.predict(tasks)</p> <p> # Sort berdasarkan uncertainty (confidence terendah duluan)</p> <p> sorted</em>tasks = sorted(</p> <p> zip(tasks, predictions),</p> <p> key=lambda x: x[1]['score']</p> <p> )</p> <p> return [t[0] for t in sorted<em>tasks[:n]]</p> <p></code></pre></p> <h3 id="2-uncertainty-sampling">2. Uncertainty Sampling</h3> <pre><code>from label</em>studio<em>sdk import Client <p>ls = Client(url='http://localhost:8080', api</em>key='your-api-key')</p> <p>project = ls.get<em>project(project</em>id=1)</p> <h1>Get tasks dengan prediction scores</h1> <p>tasks = project.get<em>tasks()</p> <h1>Sort berdasarkan prediction score (uncertainty)</h1> <p>uncertain</em>tasks = sorted(</p> <p> [t for t in tasks if t.get('predictions')],</p> <p> key=lambda t: min(p['score'] for p in t['predictions'])</p> <p>)</p> <h1>Prioritaskan uncertain tasks</h1> <p>for task in uncertain<em>tasks[:10]:</p> <p> print(f"Task {task['id']}: Score {task['predictions'][0]['score']}")</p> <p></code></pre></p> <h2 id="webhooks">Webhooks</h2> <h3 id="1-setup-webhooks">1. Setup Webhooks</h3> <pre><code># Buat webhook <p>webhook = project.create</em>webhook(</p> <p> url='https://my-server.com/webhook',</p> <p> send<em>payload=True,</p> <p> send</em>for<em>all</em>actions=False,</p> <p> actions=['ANNOTATION<em>CREATED', 'ANNOTATION</em>UPDATED']</p> <p>)</p> <h1>Actions yang tersedia:</h1> <h1>- ANNOTATION<em>CREATED</h1> <h1>- ANNOTATION</em>UPDATED</h1> <h1>- ANNOTATION<em>DELETED</h1> <h1>- TASK</em>CREATED</h1> <h1>- PROJECT<em>UPDATED</h1> <p></code></pre></p> <h3 id="2-handle-webhook">2. Handle Webhook</h3> <pre><code>from flask import Flask, request <p>app = Flask(<strong>name</strong>)</p> <p>@app.route('/webhook', methods=['POST'])</p> <p>def handle</em>webhook():</p> <p> data = request.json</p> <p> action = data['action']</p> <p> annotation = data.get('annotation')</p> <p> task = data.get('task')</p> <p> if action == 'ANNOTATION<em>CREATED':</p> <p> # Trigger model retraining</p> <p> print(f"Anotasi baru untuk task {task['id']}")</p> <p> # retrain</em>model(annotation)</p> <p> return {'status': 'ok'}</p> <p>if <strong>name</strong> == '<strong>main</strong>':</p> <p> app.run(port=5000)</p> <p></code></pre></p> <h2 id="quality-control">Quality Control</h2> <h3 id="1-agreement-metrics">1. Agreement Metrics</h3> <pre><code># Get agreement scores <p>agreements = project.get<em>agreement()</p> <p>for task</em>id, agreement in agreements.items():</p> <p> print(f"Task {task<em>id}: {agreement['agreement']:.2%}")</p> <p></code></pre></p> <h3 id="2-review-workflow">2. Review Workflow</h3> <pre><code><View> <p> <Text name="text" value="$text"/></p> <p> <Choices name="sentiment" toName="text"></p> <p> <Choice value="Positif"/></p> <p> <Choice value="Negatif"/></p> <p> </Choices></p> <p> </p> <p> <Header value="Review"/></p> <p> <Choices name="review" toName="text" choice="single"></p> <p> <Choice value="Disetujui"/></p> <p> <Choice value="Ditolak"/></p> <p> <Choice value="Perlu Review"/></p> <p> </Choices></p> <p> <TextArea name="review</em>comment" toName="text" placeholder="Komentar review"/></p> <p></View></p> <p></code></pre></p> <h2 id="best-practices">Best Practices</h2> <h3 id="1-import-data-efisien">1. Import Data Efisien</h3> <pre><code>import json <p>from label<em>studio</em>sdk import Client</p> <p>ls = Client(url='http://localhost:8080', api<em>key='your-api-key')</p> <p>project = ls.get</em>project(project<em>id=1)</p> <h1>Batch import untuk dataset besar</h1> <p>def batch</em>import(data, batch<em>size=1000):</p> <p> for i in range(0, len(data), batch</em>size):</p> <p> batch = data[i:i+batch<em>size]</p> <p> project.import</em>tasks(batch)</p> <p> print(f"Imported {min(i+batch<em>size, len(data))}/{len(data)}")</p> <h1>Load dan import</h1> <p>with open('large</em>dataset.json') as f:</p> <p> data = json.load(f)</p> <p>batch<em>import(data)</p> <p></code></pre></p> <h3 id="2-processing-export">2. Processing Export</h3> <pre><code>import pandas as pd <h1>Export anotasi</h1> <p>annotations = project.export</em>tasks()</p> <h1>Proses untuk ML training</h1> <p>training<em>data = []</p> <p>for task in annotations:</p> <p> if task['annotations']:</p> <p> annotation = task['annotations'][0]</p> <p> for result in annotation['result']:</p> <p> if result['type'] == 'choices':</p> <p> training</em>data.append({</p> <p> 'text': task['data']['text'],</p> <p> 'label': result['value']['choices'][0]</p> <p> })</p> <p>df = pd.DataFrame(training<em>data)</p> <p>df.to</em>csv('training_data.csv', index=False)</p> <p></code></pre></p> <h2 id="kesimpulan">Kesimpulan</h2> <p>Label Studio adalah essential untuk data labeling ML dengan:</p> <li></strong>Support multi-modal<strong>: Teks, gambar, audio, video</li> <li></strong>Interface fleksibel<strong>: Konfigurasi labeling customizable</li> <li></strong>Kolaborasi<strong>: Workflow anotasi tim</li> <li></strong>Integrasi ML<strong>: Backend predictions, active learning</li> <li></strong>Cloud storage**: Integrasi S3, GCS, Azure</li> <p>Key takeaways:</p> <ul><li>Rancang interface labeling yang jelas</li> <li>Gunakan prediksi untuk efisiensi pre-labeling</li> <li>Implementasikan workflow quality control</li> <li>Manfaatkan webhooks untuk automation</li> <li>Batch operations untuk dataset besar</li> <p></ul></p> </div> <footer style="margin-top:2rem;padding-top:1rem;border-top:1px solid #2a2c35;font-size:0.875rem;color:#9ca0ab;"> <p>Ditulis oleh Ruby Abdullah di <a href="https://rubythalib.ai">rubythalib.ai</a></p> </footer> </article> <section style="max-width:800px;margin:3rem auto 0;padding:2rem 1rem 0;background:#0b0b0f;color:#e6e7eb;border-top:1px solid #2a2c35;"> <h2 style="font-size:1.5rem;font-weight:600;margin-bottom:1rem;">Artikel Terkait</h2> <div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:1rem;"> <a href="/id/articles/tutorial-semantic-router-routing-cerdas-untuk-aplikasi-llm" style="text-decoration:none;color:inherit;display:block;"> <div style="border:1px solid #2a2c35;border-radius:8px;padding:1rem;height:100%;background:#15161c;"> <h3 style="font-size:1rem;font-weight:600;margin:0 0 0.5rem 0;">Tutorial Semantic Router: Routing Cerdas untuk Aplikasi LLM</h3> <p style="font-size:0.875rem;color:#9ca0ab;margin:0;">Tutorial Semantic Router: Routing Cerdas untuk Aplikasi LLM Semantic Router adalah library Python yang memungkinkan Anda...</p> </div> </a> <a href="/id/articles/tutorial-presidio-deteksi-dan-anonimisasi-data-pribadi-pii-dengan-python" style="text-decoration:none;color:inherit;display:block;"> <div style="border:1px solid #2a2c35;border-radius:8px;padding:1rem;height:100%;background:#15161c;"> <h3 style="font-size:1rem;font-weight:600;margin:0 0 0.5rem 0;">Tutorial Presidio: Deteksi dan Anonimisasi Data Pribadi (PII) dengan Python</h3> <p style="font-size:0.875rem;color:#9ca0ab;margin:0;">Tutorial Presidio: Deteksi dan Anonimisasi Data Pribadi (PII) dengan Python Dalam era AI dan big data, perlindungan data...</p> </div> </a> <a href="/id/articles/tutorial-lengkap-comet-ml-platform-mlops-untuk-experiment-tracking-dan-model-management" style="text-decoration:none;color:inherit;display:block;"> <div style="border:1px solid #2a2c35;border-radius:8px;padding:1rem;height:100%;background:#15161c;"> <h3 style="font-size:1rem;font-weight:600;margin:0 0 0.5rem 0;">Tutorial Lengkap Comet ML: Platform MLOps untuk Experiment Tracking dan Model Management</h3> <p style="font-size:0.875rem;color:#9ca0ab;margin:0;">Tutorial Lengkap Comet ML: Platform MLOps untuk Experiment Tracking dan Model Management Dalam dunia machine learning mo...</p> </div> </a> <a href="/id/articles/tutorial-surya-ocr-document-ai-untuk-text-detection-ocr-dan-layout-analysis" style="text-decoration:none;color:inherit;display:block;"> <div style="border:1px solid #2a2c35;border-radius:8px;padding:1rem;height:100%;background:#15161c;"> <h3 style="font-size:1rem;font-weight:600;margin:0 0 0.5rem 0;">Tutorial Surya OCR: Document AI untuk Text Detection, OCR, dan Layout Analysis</h3> <p style="font-size:0.875rem;color:#9ca0ab;margin:0;">Tutorial Surya OCR: Document AI untuk Text Detection, OCR, dan Layout Analysis Surya adalah library Document AI open-sou...</p> </div> </a> </div> </section> </div>  <noscript id="social-links-noscript"> <div style="display:none"> <a rel="me" href="https://www.facebook.com/rubythalibai">Facebook</a> <a rel="me" href="https://www.youtube.com/@rubyabdullah">YouTube</a> <a rel="me" href="https://www.instagram.com/rubythalib.ai/">Instagram</a> <a rel="me" href="https://www.linkedin.com/company/rubythalibai/">LinkedIn</a> <a rel="me" href="https://www.tiktok.com/@rubythalib.ai">TikTok</a> </div> </noscript>  <script> // Track all async operations window.domStability = { react: false, fonts: false, analytics: true, complete: false }; 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