from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder

def dsplit(data, labels=None, test_size=0.2, random_state=None):
    """
    Split the dataset into training and testing sets.
    
    Args:
    data (pandas.DataFrame): Input data.
    labels (list): Optional list of labels.
    test_size (float): Proportion of the dataset to include in the test split.
    random_state (int): Random state for reproducibility.
    
    Returns:
    tuple: X_train, X_test, y_train, y_test, encoded_labels
    """
    encoder = LabelEncoder()

    X = data.iloc[:, :-1]
    y = data.iloc[:, -1]
    
    if labels is not None:
        encoded_labels = encoder.fit_transform(labels)
    else:
        encoder.fit(y)
        encoded_labels = None
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
    
    scaler = StandardScaler()
    X_train = scaler.fit_transform(X_train)
    X_test = scaler.transform(X_test)
    
    y_train = encoder.transform(y_train.values.ravel())
    y_test = encoder.transform(y_test.values.ravel())
    
    return X_train, X_test, y_train, y_test, encoded_labels