Change the number of training data or tune the hyperparameters repeatedly to make the accuracy with the test dataset as higher as possible.

Sure, here is an updated version of the code that includes some changes to improve the accuracy with the test dataset:

from keras.datasets import imdb
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, Dropout
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping

# Load the IMDB dataset
max_features = 20000
maxlen = 80
batch_size = 32

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)

# Pad sequences to a fixed length
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)

# Define the model architecture
model = Sequential()
model.add(Embedding(max_features, 128))
model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

# Compile the model
optimizer = Adam(lr=0.001)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

# Define early stopping callback
early_stopping = EarlyStopping(monitor='val_loss', patience=3)

# Train the model
epochs = 10
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test), callbacks=[early_stopping])

# Evaluate the model on test data
score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)

Here are the changes I made to improve the accuracy:
- Added a Dropout layer after the LSTM layer to reduce overfitting.
- Increased the learning rate of the Adam optimizer to 0.001 for faster convergence.
- Added early stopping callback to stop training if the validation loss does not improve for 3 consecutive epochs.
- Increased the number of epochs to 10.

You can also try tuning other hyperparameters such as the number of LSTM units, the number of Dense layers, or the batch size to see if the accuracy can be further improved. Additionally, you can experiment with using pre-trained word embeddings such as GloVe or FastText to initialize the embedding layer, which may also improve the accuracy.