---
library_name: transformers
tags:
- deeplearning
- indobert
- SMOTE
license: apache-2.0
language:
- id
base_model:
- indobenchmark/indobert-base-p1
---
# Hasil Train Loop
| Epoch | Train Loss | Train ACC | Train F1 | Train REC | Train PRE | Valid Loss | Valid ACC | Valid F1 | Valid REC | Valid PRE | Catatan                                         |
| ----- | ---------- | --------- | -------- | --------- | --------- | ---------- | --------- | -------- | --------- | --------- | ----------------------------------------------- |
| 1     | 0.8074     | 0.6357    | 0.6347   | 0.6357    | 0.6343    | 0.6218     | 0.7230    | 0.6995   | 0.7050    | 0.7110    | Model terbaik disimpan                          |
| 2     | 0.5472     | 0.7728    | 0.7732   | 0.7728    | 0.7739    | 0.5335     | 0.7824    | 0.7515   | 0.7531    | 0.7563    | Model terbaik disimpan                          |
| 3     | 0.4165     | 0.8307    | 0.8313   | 0.8307    | 0.8321    | 0.4123     | 0.8380    | 0.8113   | 0.8127    | 0.8104    | Model terbaik disimpan                          |
| 4     | 0.3166     | 0.8751    | 0.8755   | 0.8751    | 0.8762    | 0.4554     | 0.8248    | 0.7951   | 0.7973    | 0.7971    | VALID LOSS tidak membaik (1/2)                  |
| 5     | 0.2593     | 0.8970    | 0.8973   | 0.8970    | 0.8979    | 0.4023     | 0.8441    | 0.8230   | 0.8300    | 0.8219    | Model terbaik disimpan                          |
| 6     | 0.2175     | 0.9160    | 0.9161   | 0.9160    | 0.9163    | 0.3470     | 0.8850    | 0.8633   | 0.8609    | 0.8665    | Model terbaik disimpan                          |
| 7     | 0.1940     | 0.9268    | 0.9269   | 0.9268    | 0.9271    | 0.3848     | 0.8704    | 0.8480   | 0.8484    | 0.8478    | VALID LOSS tidak membaik (1/2)                  |
| 8     | 0.1616     | 0.9411    | 0.9411   | 0.9411    | 0.9411    | 0.4156     | 0.8596    | 0.8377   | 0.8414    | 0.8354    | VALID LOSS tidak membaik (2/2) — Early stopping |

# Accuracy per Epoch
![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F67cd97b215882735a47c1390%2FnkYMNnkIsIpxQYjwQbYmK.png)

# Loss per Epoch
![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F67cd97b215882735a47c1390%2FxcnAYbvnAroGz_49S-I0P.png)

# Classification Report pada Data Testing
![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F67cd97b215882735a47c1390%2FCjouBQw9N_3SVqcNMlC3R.png)

# Confusion Matrix pada Data Testing
![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F67cd97b215882735a47c1390%2Foz08M-5JwdBNWt083M3HF.png)

# Distribusi Sentimen Hasil Pred Pada Data Testing
![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F67cd97b215882735a47c1390%2FI-uz-YIvdK6WCqSf6fXZ3.png)

# WordCloud Hasil Prediksi Pada Data Testing
![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F67cd97b215882735a47c1390%2F_GsET5OlnD1W6u2Id6Nwl.png)

# Frekuensi Kata Hasil Prediksi Pada Data Testing
![image/png](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F67cd97b215882735a47c1390%2Fg97IcCc1u8UBzqC8hDUIP.png)