cek kondisi hasil model berikut
Training and evaluating model for fold 1
Epoch 1/200
103/103 [==============================] - 49s 462ms/step - loss: 9.5263 - accuracy: 0.4801 - val_loss: 8.7521 - val_accuracy: 0.5162
Epoch 2/200
103/103 [==============================] - 47s 458ms/step - loss: 8.1008 - accuracy: 0.5295 - val_loss: 7.4447 - val_accuracy: 0.5272
Epoch 3/200
103/103 [==============================] - 47s 460ms/step - loss: 6.9076 - accuracy: 0.5441 - val_loss: 6.3573 - val_accuracy: 0.5803
Epoch 4/200
103/103 [==============================] - 48s 464ms/step - loss: 5.9102 - accuracy: 0.5626 - val_loss: 5.4400 - val_accuracy: 0.5935
Epoch 5/200
103/103 [==============================] - 47s 462ms/step - loss: 5.0857 - accuracy: 0.5801 - val_loss: 4.7155 - val_accuracy: 0.5819
Epoch 6/200
103/103 [==============================] - 47s 460ms/step - loss: 4.4231 - accuracy: 0.5941 - val_loss: 4.1114 - val_accuracy: 0.6164
Epoch 7/200
103/103 [==============================] - 48s 462ms/step - loss: 3.8981 - accuracy: 0.6049 - val_loss: 3.6443 - val_accuracy: 0.6246
Epoch 8/200
103/103 [==============================] - 47s 460ms/step - loss: 3.4839 - accuracy: 0.6084 - val_loss: 3.2750 - val_accuracy: 0.6237
Epoch 9/200
103/103 [==============================] - 48s 463ms/step - loss: 3.1494 - accuracy: 0.6098 - val_loss: 2.9883 - val_accuracy: 0.6216
Epoch 10/200
103/103 [==============================] - 48s 470ms/step - loss: 2.8683 - accuracy: 0.6168 - val_loss: 2.7280 - val_accuracy: 0.6216
Epoch 11/200
103/103 [==============================] - 48s 462ms/step - loss: 2.6410 - accuracy: 0.6255 - val_loss: 2.5303 - val_accuracy: 0.6289
Epoch 12/200
103/103 [==============================] - 48s 463ms/step - loss: 2.4584 - accuracy: 0.6213 - val_loss: 2.3647 - val_accuracy: 0.6103
Epoch 13/200
103/103 [==============================] - 48s 461ms/step - loss: 2.3012 - accuracy: 0.6243 - val_loss: 2.2098 - val_accuracy: 0.6304
Epoch 14/200
103/103 [==============================] - 48s 466ms/step - loss: 2.1674 - accuracy: 0.6300 - val_loss: 2.0908 - val_accuracy: 0.6277
Epoch 15/200
103/103 [==============================] - 47s 459ms/step - loss: 2.0515 - accuracy: 0.6288 - val_loss: 2.0464 - val_accuracy: 0.5819
Epoch 16/200
103/103 [==============================] - 48s 462ms/step - loss: 1.9525 - accuracy: 0.6395 - val_loss: 1.9200 - val_accuracy: 0.6081
Epoch 17/200
103/103 [==============================] - 47s 459ms/step - loss: 1.8863 - accuracy: 0.6352 - val_loss: 1.9194 - val_accuracy: 0.5562
Epoch 18/200
103/103 [==============================] - 48s 464ms/step - loss: 1.8104 - accuracy: 0.6372 - val_loss: 1.7614 - val_accuracy: 0.6378
Epoch 19/200
103/103 [==============================] - 48s 462ms/step - loss: 1.7435 - accuracy: 0.6459 - val_loss: 1.7217 - val_accuracy: 0.6188
Epoch 20/200
103/103 [==============================] - 48s 463ms/step - loss: 1.6941 - accuracy: 0.6540 - val_loss: 1.6723 - val_accuracy: 0.6292
Epoch 21/200
103/103 [==============================] - 48s 464ms/step - loss: 1.6571 - accuracy: 0.6447 - val_loss: 1.6396 - val_accuracy: 0.6255
Epoch 22/200
103/103 [==============================] - 48s 461ms/step - loss: 1.6058 - accuracy: 0.6609 - val_loss: 1.5941 - val_accuracy: 0.6387
Epoch 23/200
103/103 [==============================] - 47s 461ms/step - loss: 1.5709 - accuracy: 0.6636 - val_loss: 1.5524 - val_accuracy: 0.6515
Epoch 24/200
103/103 [==============================] - 47s 460ms/step - loss: 1.5449 - accuracy: 0.6629 - val_loss: 1.5274 - val_accuracy: 0.6491
Epoch 25/200
103/103 [==============================] - 47s 462ms/step - loss: 1.5000 - accuracy: 0.6729 - val_loss: 1.5219 - val_accuracy: 0.6460
Epoch 26/200
103/103 [==============================] - 47s 457ms/step - loss: 1.4776 - accuracy: 0.6770 - val_loss: 1.4939 - val_accuracy: 0.6536
Epoch 27/200
103/103 [==============================] - 47s 460ms/step - loss: 1.4448 - accuracy: 0.6857 - val_loss: 1.4817 - val_accuracy: 0.6353
Epoch 28/200
103/103 [==============================] - 48s 464ms/step - loss: 1.4433 - accuracy: 0.6724 - val_loss: 1.4671 - val_accuracy: 0.6457
Epoch 29/200
103/103 [==============================] - 47s 461ms/step - loss: 1.4114 - accuracy: 0.6808 - val_loss: 1.4333 - val_accuracy: 0.6558
Epoch 30/200
103/103 [==============================] - 48s 465ms/step - loss: 1.3817 - accuracy: 0.6898 - val_loss: 1.4177 - val_accuracy: 0.6487
Epoch 31/200
103/103 [==============================] - 47s 459ms/step - loss: 1.3826 - accuracy: 0.6819 - val_loss: 1.4121 - val_accuracy: 0.6591
Epoch 32/200
103/103 [==============================] - 48s 464ms/step - loss: 1.3557 - accuracy: 0.6909 - val_loss: 1.3967 - val_accuracy: 0.6582
Epoch 33/200
103/103 [==============================] - 47s 460ms/step - loss: 1.3336 - accuracy: 0.6958 - val_loss: 1.3898 - val_accuracy: 0.6518
Epoch 34/200
103/103 [==============================] - 47s 461ms/step - loss: 1.3110 - accuracy: 0.7086 - val_loss: 1.6486 - val_accuracy: 0.4725
Epoch 35/200
103/103 [==============================] - 48s 462ms/step - loss: 1.3139 - accuracy: 0.7017 - val_loss: 1.3970 - val_accuracy: 0.6286
Epoch 36/200
103/103 [==============================] - 47s 457ms/step - loss: 1.3130 - accuracy: 0.6960 - val_loss: 1.3472 - val_accuracy: 0.6637
Epoch 37/200
103/103 [==============================] - 48s 464ms/step - loss: 1.2673 - accuracy: 0.7199 - val_loss: 1.3828 - val_accuracy: 0.6469
Epoch 38/200
103/103 [==============================] - 47s 460ms/step - loss: 1.2467 - accuracy: 0.7293 - val_loss: 1.3443 - val_accuracy: 0.6539
Epoch 39/200
103/103 [==============================] - 47s 460ms/step - loss: 1.2643 - accuracy: 0.7102 - val_loss: 1.3301 - val_accuracy: 0.6576
Epoch 40/200
103/103 [==============================] - 47s 457ms/step - loss: 1.2578 - accuracy: 0.7174 - val_loss: 1.3495 - val_accuracy: 0.6374
Epoch 41/200
103/103 [==============================] - 47s 456ms/step - loss: 1.2196 - accuracy: 0.7319 - val_loss: 1.3295 - val_accuracy: 0.6646
Epoch 42/200
103/103 [==============================] - 47s 461ms/step - loss: 1.2046 - accuracy: 0.7360 - val_loss: 1.3690 - val_accuracy: 0.6536
Epoch 43/200
103/103 [==============================] - 47s 458ms/step - loss: 1.2233 - accuracy: 0.7319 - val_loss: 1.3370 - val_accuracy: 0.6222
Epoch 44/200
103/103 [==============================] - 47s 461ms/step - loss: 1.2036 - accuracy: 0.7390 - val_loss: 1.3705 - val_accuracy: 0.6527
Epoch 45/200
103/103 [==============================] - 47s 459ms/step - loss: 1.1838 - accuracy: 0.7453 - val_loss: 1.3250 - val_accuracy: 0.6451
Epoch 46/200
103/103 [==============================] - 47s 461ms/step - loss: 1.2143 - accuracy: 0.7222 - val_loss: 1.2936 - val_accuracy: 0.6591
Epoch 47/200
103/103 [==============================] - 47s 459ms/step - loss: 1.1782 - accuracy: 0.7433 - val_loss: 1.2909 - val_accuracy: 0.6714
Epoch 48/200
103/103 [==============================] - 47s 456ms/step - loss: 1.1515 - accuracy: 0.7581 - val_loss: 1.3471 - val_accuracy: 0.6573
Epoch 49/200
103/103 [==============================] - 48s 463ms/step - loss: 1.1321 - accuracy: 0.7685 - val_loss: 1.3691 - val_accuracy: 0.6466
Epoch 50/200
103/103 [==============================] - 48s 463ms/step - loss: 1.1567 - accuracy: 0.7524 - val_loss: 1.3178 - val_accuracy: 0.6274
Epoch 51/200
103/103 [==============================] - 47s 460ms/step - loss: 1.1554 - accuracy: 0.7481 - val_loss: 1.2916 - val_accuracy: 0.6390
Epoch 52/200
103/103 [==============================] - ETA: 0s - loss: 1.1822 - accuracy: 0.7342Restoring model weights from the end of the best epoch: 47.
103/103 [==============================] - 47s 460ms/step - loss: 1.1822 - accuracy: 0.7342 - val_loss: 1.3947 - val_accuracy: 0.6381
Epoch 52: early stopping