Model klasifikasi sentimen untuk teks berbahasa Indonesia menggunakan fine-tuning IndoBERT. Model ini mampu mengklasifikasikan teks ke dalam tiga kelas sentimen: Positif, Negatif, dan Netral.
1from transformers import pipeline
2
3# Load model langsung dari Hugging Face Hub
4sentiment_analyzer = pipeline(
5 "text-classification",
6 model="Hadisawara/indonesian-sentiment-analysis"
7)
8
9# Contoh penggunaan
10texts = [
11 "Produk ini sangat bagus dan berkualitas tinggi!",
12 "Pelayanan sangat buruk, saya kecewa.",
13 "Barang sudah diterima dengan kondisi baik."
14]
15
16for text in texts:
17 result = sentiment_analyzer(text)
18 print(f"Teks: {text}")
19 print(f"Sentimen: {result[0]['label']} (score: {result[0]['score']:.4f})")
20 print()
1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3
4model_name = "Hadisawara/indonesian-sentiment-analysis"
5tokenizer = AutoTokenizer.from_pretrained(model_name)
6model = AutoModelForSequenceClassification.from_pretrained(model_name)
7
8def predict_sentiment(text):
9 inputs = tokenizer(
10 text,
11 return_tensors="pt",
12 truncation=True,
13 max_length=512,
14 padding=True
15 )
16
17 with torch.no_grad():
18 outputs = model(**inputs)
19 predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
20
21 labels = ["NEGATIVE", "NEUTRAL", "POSITIVE"]
22 predicted_label = labels[predictions.argmax().item()]
23 confidence = predictions.max().item()
24
25 return {"label": predicted_label, "confidence": confidence}
26
27# Test
28result = predict_sentiment("Aplikasi ini sangat membantu pekerjaan saya!")
29print(result) # {"label": "POSITIVE", "confidence": 0.98}
1@misc{hadisawara2026indonesian,
2 title={Indonesian Sentiment Analysis: Fine-tuned IndoBERT for Indonesian Text Classification},
3 author={Hadisawara},
4 year={2026},
5 publisher={Hugging Face},
6 howpublished={\url{https://huggingface.co/Hadisawara/indonesian-sentiment-analysis}}
7}
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