DAFTAR PUSTAKA - Analisis Sentimen Berbasis Aspek Pada Pertandingan Sepak Bola Arab vs Indonesi

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LAMPIRAN

1. Meng Import Dataset import pandas as pd

pd.options.mode.chained_assignment = None # Nonaktifkan warning

gabungan_sentimen = pd.read_csv('gabungan_sentimen.csv') gabungan_sentimen =

gabungan_sentimen.drop(columns=['stopwords_removal','is_menyerang','is_kondi si_lapangan','is_bertahan'])

gabungan_sentimen

2. Preprocessing Teks import re

from nltk.tokenize import word_tokenize from nltk.corpus import stopwords

from Sastrawi.Stemmer.StemmerFactory import StemmerFactory

# URL raw GitHub

url = "https://raw.githubusercontent.com/panggi/pujangga/master/resource/

formalization/formalizationDict.txt"

# Membaca file dari URL

df_slang_word = pd.read_csv(url, sep="\t", header=None, names=["Informal",

"Formal"])

# Konversi DataFrame ke dictionary untuk lookup

slang_dict = dict(zip(df_slang_word["Informal"], df_slang_word["Formal"]))

# Mengubah teks menjadi lowercase def case_folding(text):

text = text.lower() return text

# membersihkan teks def cleaning(text):

# Ganti koma dengan koma yang diikuti spasi text = re.sub(r',', ', ', text)

# Ganti titik dengan titik yang diikuti spasi text = re.sub(r'\.', '. ', text)

# Hapus angka

text = re.sub(r'\d+', '', text)

# Hapus tanda baca dan simbol (kecuali huruf dan spasi) text = re.sub(r'[^\w\s]', '', text)

# Hapus karakter tambahan (opsional, jika ada spasi berlebihan)

text = re.sub(r'\s+', ' ', text).strip() return text

# mentokenisasi teks def tokenizing(text):

# Tokenisasi teks

tokens = word_tokenize(text) return tokens

# mengganti slang words def slang_words(tokens):

return [slang_dict.get(token, token) for token in tokens]

# mengubah kata menjadi kata dasar def stemming(tokens):

# Membuat stemmer Sastrawi factory = StemmerFactory()

stemmer = factory.create_stemmer()

# Stemming setiap token menggunakan Sastrawi stems = [stemmer.stem(token) for token in tokens]

return stems

# menggabungkan kata negasi dengan kata selanjutnya def convert_negation(tokens):

# Mengubah kata negasi menjadi bentuk aslinya

negation_words = ['tidak', 'bukan', 'tak', 'jangan', 'belum', 'tiada', 'takut', 'kurang']

for i in range(len(tokens)):

if tokens[i] in negation_words and i < len(tokens) - 1:

tokens[i + 1] = f"{tokens[i]}_{tokens[i + 1]}"

return tokens

# menghapus stopwords

def stopwords_removal(tokens):

# Menghapus stopwords

stop_words = set(stopwords.words('indonesian'))

# Menambahkan stopwords secara manual

additional_stopwords = ["dong", "nih", "sih", "aja", "deh" ,"hihi","nya"]

stop_words.update(additional_stopwords)

filtered_tokens = [token for token in tokens if token not in stop_words]

return filtered_tokens

3. Penerapan Preprocessing ke Dataset gabungan_sentimen['case_folding'] =

gabungan_sentimen['cleaning'] =

gabungan_sentimen['case_folding'].apply(cleaning)

gabungan_sentimen['tokenizing'] =

gabungan_sentimen['cleaning'].apply(tokenizing)

gabungan_sentimen['slang_words'] =

gabungan_sentimen['tokenizing'].apply(slang_words)

gabungan_sentimen['stemming'] =

gabungan_sentimen['slang_words'].apply(stemming)

gabungan_sentimen['convert_negation'] =

gabungan_sentimen['stemming'].apply(convert_negation)

gabungan_sentimen['stopwords_removal'] =

gabungan_sentimen['convert_negation'].apply(stopwords_removal)

gabungan_sentimen

4. Prediksi Sentimen untuk Indonesia (HOME dan AWAY)

id = gabungan_sentimen[['stopwords_removal','pertandingan', 'negara']]

id = id[id['negara'] == 'id']

id = id.drop(columns=['negara']).reset_index(drop=True)

a. Prediksi Menyerang HOME

id_menyerang_home = id[id['pertandingan'] == 'HOME']

id_menyerang_home

import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_id_menyerang.pkl', 'rb') as file:

loaded_model_id_menyerang = pickle.load(file)

# Memuat vectorizer dari file pickle

with open('model/tfidf_vectorizer_model_id_menyerang.pkl', 'rb') as file:

loaded_vectorizer_id_menyerang = pickle.load(file)

print("Model dan vectorizer berhasil dimuat dari file.")

test_text_vectorized =

loaded_vectorizer_id_menyerang.transform(id_menyerang_home['stopwords_rem oval'].apply(lambda x: ' '.join(x)))

predictions = loaded_model_id_menyerang.predict(test_text_vectorized)

id_menyerang_home.loc[:, 'menyerang'] = predictions

id_menyerang_home['menyerang'].value_counts()

import matplotlib.pyplot as plt import seaborn as sns

# Data

counts = id_menyerang_home['menyerang'].value_counts()

label_mapping = {-1: "Negatif", 0: "Netral", 1: "Positif"} # Mapping label counts.index = counts.index.map(label_mapping) # Ubah label

# Plot

plt.figure(figsize=(10,6))

ax = sns.barplot(x=counts.index, y=counts.values, palette=['gray', 'green', 'red'])

# Tambahkan nilai di atas batang

for i, value in enumerate(counts.values):

ax.text(i, value + 40, str(value), ha='center', va='bottom', fontsize=12, fontweight='bold', color='black')

# Label

plt.xlabel("Kategori Sentimen") plt.ylabel("Jumlah")

plt.title("Distribusi Sentimen Indonesia Menyerang Home") plt.xticks(rotation=0)

# Tampilkan plt.show()

b. Prediksi Menyerang AWAY

id_menyerang_away = id[id['pertandingan'] == 'AWAY']

id_menyerang_away import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_id_menyerang.pkl', 'rb') as file:

loaded_model_id_menyerang = pickle.load(file)

# Memuat vectorizer dari file pickle

with open('model/tfidf_vectorizer_model_id_menyerang.pkl', 'rb') as file:

loaded_vectorizer_id_menyerang = pickle.load(file)

print("Model dan vectorizer berhasil dimuat dari file.")

test_text_vectorized =

loaded_vectorizer_id_menyerang.transform(id_menyerang_away['stopwords_rem oval'].apply(lambda x: ' '.join(x)))

predictions = loaded_model_id_menyerang.predict(test_text_vectorized)

id_menyerang_away.loc[:, 'menyerang'] = predictions

id_menyerang_away['menyerang'].value_counts()

import matplotlib.pyplot as plt import seaborn as sns

# Data

counts = id_menyerang_away['menyerang'].value_counts()

label_mapping = {-1: "Negatif", 0: "Netral", 1: "Positif"} # Mapping label counts.index = counts.index.map(label_mapping) # Ubah label

# Plot

plt.figure(figsize=(10,6))

ax = sns.barplot(x=counts.index, y=counts.values, palette=['gray', 'green', 'red'])

# Tambahkan nilai di atas batang

for i, value in enumerate(counts.values):

ax.text(i, value + 45, str(value), ha='center', va='bottom', fontsize=12, fontweight='bold', color='black')

# Label

plt.xlabel("Kategori Sentimen") plt.ylabel("Jumlah")

plt.title("Distribusi Sentimen Indonesia Menyerang Away") plt.xticks(rotation=0)

# Tampilkan plt.show()

c. Prediksi Bertahan HOME

id_bertahan_home = id[id['pertandingan'] == 'HOME']

id_bertahan_home

import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_id_bertahan.pkl', 'rb') as file:

loaded_model_id_bertahan = pickle.load(file)

# Memuat vectorizer dari file pickle

with open('model/tfidf_vectorizer_model_id_bertahan.pkl', 'rb') as file:

loaded_vectorizer_id_bertahan = pickle.load(file)

print("Model dan vectorizer berhasil dimuat dari file.")

test_text_vectorized =

loaded_vectorizer_id_bertahan.transform(id_bertahan_home['stopwords_removal' ].apply(lambda x: ' '.join(x)))

predictions = loaded_model_id_bertahan.predict(test_text_vectorized)

id_bertahan_home.loc[:, 'bertahan'] = predictions

id_bertahan_home['bertahan'].value_counts()

import matplotlib.pyplot as plt import seaborn as sns

# Data

counts = id_bertahan_home['bertahan'].value_counts()

label_mapping = {-1: "Negatif", 0: "Netral", 1: "Positif"} # Mapping label counts.index = counts.index.map(label_mapping) # Ubah label

# Plot

plt.figure(figsize=(10,6))

ax = sns.barplot(x=counts.index, y=counts.values, palette=['gray', 'green', 'red'])

# Tambahkan nilai di atas batang

for i, value in enumerate(counts.values):

ax.text(i, value + 40, str(value), ha='center', va='bottom', fontsize=12, fontweight='bold', color='black')

# Label

plt.xlabel("Kategori Sentimen") plt.ylabel("Jumlah")

plt.title("Distribusi Sentimen Indonesia Bertahan Home") plt.xticks(rotation=0)

# Tampilkan plt.show()

d. Prediksi Bertahan AWAY

id_bertahan_away = id[id['pertandingan'] == 'AWAY']

id_bertahan_away

import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_id_bertahan.pkl', 'rb') as file:

loaded_model_id_bertahan = pickle.load(file)

# Memuat vectorizer dari file pickle

with open('model/tfidf_vectorizer_model_id_bertahan.pkl', 'rb') as file:

loaded_vectorizer_id_bertahan = pickle.load(file)

print("Model dan vectorizer berhasil dimuat dari file.")

test_text_vectorized =

loaded_vectorizer_id_bertahan.transform(id_bertahan_away['stopwords_removal' ].apply(lambda x: ' '.join(x)))

predictions = loaded_model_id_bertahan.predict(test_text_vectorized)

id_bertahan_away.loc[:, 'bertahan'] = predictions

id_bertahan_away['bertahan'].value_counts()

import matplotlib.pyplot as plt import seaborn as sns

# Data

counts = id_bertahan_away['bertahan'].value_counts()

label_mapping = {-1: "Negatif", 0: "Netral", 1: "Positif"} # Mapping label counts.index = counts.index.map(label_mapping) # Ubah label

# Plot

plt.figure(figsize=(10,6))

ax = sns.barplot(x=counts.index, y=counts.values, palette=['green', 'gray', 'red'])

# Tambahkan nilai di atas batang

for i, value in enumerate(counts.values):

ax.text(i, value + 40, str(value), ha='center', va='bottom', fontsize=12, fontweight='bold', color='black')

# Label

plt.xlabel("Kategori Sentimen") plt.ylabel("Jumlah")

plt.title("Distribusi Sentimen Indonesia Bertahan Away") plt.xticks(rotation=0)

# Tampilkan plt.show()

5. Prediksi Sentimen untuk Arab (HOME dan AWAY)

ar = gabungan_sentimen[['stopwords_removal','pertandingan', 'negara']]

ar = ar[ar['negara'] == 'ar']

ar = ar.drop(columns=['negara']).reset_index(drop=True)

a. Prediksi Menyerang HOME

ar_menyerang_home = ar[ar['pertandingan'] == 'HOME']

ar_menyerang_home

import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_ar_menyerang.pkl', 'rb') as file:

loaded_model_ar_menyerang = pickle.load(file)

# Memuat vectorizer dari file pickle

with open('model/tfidf_vectorizer_model_ar_menyerang.pkl', 'rb') as file:

loaded_vectorizer_ar_menyerang = pickle.load(file)

print("Model dan vectorizer berhasil dimuat dari file.")

test_text_vectorized =

loaded_vectorizer_ar_menyerang.transform(ar_menyerang_home['stopwords_rem oval'].apply(lambda x: ' '.join(x)))

predictions = loaded_model_ar_menyerang.predict(test_text_vectorized)

ar_menyerang_home.loc[:, 'menyerang'] = predictions

ar_menyerang_home['menyerang'].value_counts()

import matplotlib.pyplot as plt import seaborn as sns

# Data

counts = ar_menyerang_home['menyerang'].value_counts()

label_mapping = {-1: "Negatif", 0: "Netral", 1: "Positif"} # Mapping label counts.index = counts.index.map(label_mapping) # Ubah label

# Plot

plt.figure(figsize=(10,6))

ax = sns.barplot(x=counts.index, y=counts.values, palette=['red', 'gray', 'green'])

# Tambahkan nilai di atas batang

for i, value in enumerate(counts.values):

ax.text(i, value, str(value), ha='center', va='bottom', fontsize=12, fontweight='bold', color='black')

# Label

plt.xlabel("Kategori Sentimen") plt.ylabel("Jumlah")

plt.title("Distribusi Sentimen Arab Saudi Menyerang Home") plt.xticks(rotation=0)

# Tampilkan plt.show()

b. Prediksi Menyerang AWAY

ar_menyerang_away = ar[ar['pertandingan'] == 'AWAY']

ar_menyerang_away

import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_ar_menyerang.pkl', 'rb') as file:

loaded_model_ar_menyerang = pickle.load(file)

# Memuat vectorizer dari file pickle

with open('model/tfidf_vectorizer_model_ar_menyerang.pkl', 'rb') as file:

loaded_vectorizer_ar_menyerang = pickle.load(file)

print("Model dan vectorizer berhasil dimuat dari file.")

test_text_vectorized =

loaded_vectorizer_ar_menyerang.transform(ar_menyerang_away['stopwords_rem oval'].apply(lambda x: ' '.join(x)))

predictions = loaded_model_ar_menyerang.predict(test_text_vectorized)

ar_menyerang_away.loc[:, 'menyerang'] = predictions

ar_menyerang_away['menyerang'].value_counts()

import matplotlib.pyplot as plt import seaborn as sns

# Data

counts = ar_menyerang_away['menyerang'].value_counts()

label_mapping = {-1: "Negatif", 0: "Netral", 1: "Positif"} # Mapping label counts.index = counts.index.map(label_mapping) # Ubah label

# Plot

plt.figure(figsize=(10,6))

ax = sns.barplot(x=counts.index, y=counts.values, palette=['gray', 'red', 'green'])

# Tambahkan nilai di atas batang

for i, value in enumerate(counts.values):

ax.text(i, value, str(value), ha='center', va='bottom', fontsize=12, fontweight='bold', color='black')

# Label

plt.xlabel("Kategori Sentimen") plt.ylabel("Jumlah")

plt.title("Distribusi Sentimen Arab Saudi Menyerang Away") plt.xticks(rotation=0)

# Tampilkan

plt.show()

c. Prediksi Bertahan HOME

ar_bertahan_home = ar[ar['pertandingan'] == 'HOME']

ar_bertahan_home

import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_ar_bertahan.pkl', 'rb') as file:

loaded_model_ar_bertahan = pickle.load(file)

# Memuat vectorizer dari file pickle

with open('model/tfidf_vectorizer_model_ar_bertahan.pkl', 'rb') as file:

loaded_vectorizer_ar_bertahan = pickle.load(file)

print("Model dan vectorizer berhasil dimuat dari file.")

test_text_vectorized =

loaded_vectorizer_ar_bertahan.transform(ar_bertahan_home['stopwords_removal' ].apply(lambda x: ' '.join(x)))

predictions = loaded_model_ar_bertahan.predict(test_text_vectorized)

ar_bertahan_home.loc[:, 'bertahan'] = predictions

ar_bertahan_home['bertahan'].value_counts()

import matplotlib.pyplot as plt import seaborn as sns

# Data

counts = ar_bertahan_home['bertahan'].value_counts()

label_mapping = {-1: "Negatif", 0: "Netral", 1: "Positif"} # Mapping label counts.index = counts.index.map(label_mapping) # Ubah label

# Plot

plt.figure(figsize=(10,6))

ax = sns.barplot(x=counts.index, y=counts.values, palette=['gray', 'red', 'green'])

# Tambahkan nilai di atas batang

for i, value in enumerate(counts.values):

ax.text(i, value, str(value), ha='center', va='bottom', fontsize=12, fontweight='bold', color='black')

# Label

plt.xlabel("Kategori Sentimen") plt.ylabel("Jumlah")

plt.title("Distribusi Sentimen Arab Saudi Bertahan Home") plt.xticks(rotation=0)

# Tampilkan plt.show()

d. Prediksi Bertahan AWAY

ar_bertahan_away = ar[ar['pertandingan'] == 'AWAY']

ar_bertahan_away

import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_ar_bertahan.pkl', 'rb') as file:

loaded_model_ar_bertahan = pickle.load(file)

# Memuat vectorizer dari file pickle

with open('model/tfidf_vectorizer_model_ar_bertahan.pkl', 'rb') as file:

loaded_vectorizer_ar_bertahan = pickle.load(file)

print("Model dan vectorizer berhasil dimuat dari file.")

test_text_vectorized =

loaded_vectorizer_ar_bertahan.transform(ar_bertahan_away['stopwords_removal' ].apply(lambda x: ' '.join(x)))

predictions = loaded_model_ar_bertahan.predict(test_text_vectorized)

ar_bertahan_away.loc[:, 'bertahan'] = predictions

ar_bertahan_away['bertahan'].value_counts()

import matplotlib.pyplot as plt import seaborn as sns

# Data

counts = ar_bertahan_away['bertahan'].value_counts()

label_mapping = {-1: "Negatif", 0: "Netral", 1: "Positif"} # Mapping label counts.index = counts.index.map(label_mapping) # Ubah label

# Plot

plt.figure(figsize=(10,6))

ax = sns.barplot(x=counts.index, y=counts.values, palette=['gray', 'red', 'green'])

# Tambahkan nilai di atas batang

for i, value in enumerate(counts.values):

ax.text(i, value, str(value), ha='center', va='bottom', fontsize=12, fontweight='bold', color='black')

# Label

plt.xlabel("Kategori Sentimen") plt.ylabel("Jumlah")

plt.title("Distribusi Sentimen Arab Saudi Bertahan Away") plt.xticks(rotation=0)

# Tampilkan plt.show()

6. Prediksi Sentimen untuk Kondisi Lapangan (HOME dan AWAY) a. Prediksi Kondisi Lapangan HOME

home = gabungan_sentimen[['stopwords_removal','pertandingan']]

home = home[home['pertandingan'] == 'HOME']

home

import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_home.pkl', 'rb') as file:

loaded_model_home = pickle.load(file)

# Memuat vectorizer dari file pickle

with open('model/tfidf_vectorizer_model_home.pkl', 'rb') as file:

loaded_vectorizer_home = pickle.load(file)

print("Model dan vectorizer berhasil dimuat dari file.")

test_text_vectorized =

loaded_vectorizer_home.transform(home['stopwords_removal'].apply(lambda x: ' '.join(x)))

predictions = loaded_model_home.predict(test_text_vectorized)

home.loc[:, 'kondisi_lapangan'] = predictions

home['kondisi_lapangan'].value_counts()

import matplotlib.pyplot as plt import seaborn as sns

# Data

counts = home['kondisi_lapangan'].value_counts()

label_mapping = {-1: "Negatif", 0: "Netral", 1: "Positif"} # Mapping label counts.index = counts.index.map(label_mapping) # Ubah label

# Plot

plt.figure(figsize=(10,6))

ax = sns.barplot(x=counts.index, y=counts.values, palette=['gray', 'green', 'red'])

# Tambahkan nilai di atas batang

for i, value in enumerate(counts.values):

ax.text(i, value, str(value), ha='center', va='bottom', fontsize=12, fontweight='bold', color='black')

# Label

plt.xlabel("Kategori Sentimen") plt.ylabel("Jumlah")

plt.title("Distribusi Sentimen Kondisi Lapangan Home") plt.xticks(rotation=0)

# Tampilkan plt.show()

b. Prediksi Kondisi Lapangan AWAY

away = gabungan_sentimen[['stopwords_removal','pertandingan']]

away = away[away['pertandingan'] == 'AWAY']

away

import pickle

# Memuat model dari file pickle

with open('model/random_forest_model_away.pkl', 'rb') as file:

loaded_model_away = pickle.load(file)

# Memuat vectorizer dari file pickle

Dalam dokumen Analisis Sentimen Berbasis Aspek Pada Pertandingan Sepak Bola Arab vs Indonesia (Halaman 42-58)