chore: add generate table from origin data

generate_table_tfidf.py

@@ -0,0 +1,79 @@
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from openpyxl import Workbook
+
+# === LOAD DATA ===
+df = pd.read_csv("robust_data/dataset/trimmed_sentiment_dataset.csv")
+
+# handle missing
+df['Cleaned_Review'] = df['Cleaned_Review'].fillna("")
+df['Review'] = df['Review'].fillna("")
+
+# limit 3238 data
+df = df.head(3238)
+
+# ambil 3 atas & 3 bawah
+selected = pd.concat([df.head(3), df.tail(3)]).reset_index(drop=True)
+
+# === TF-IDF FIT KE SELURUH DATA ===
+vectorizer = TfidfVectorizer()
+vectorizer.fit(df['Cleaned_Review'])
+
+tfidf_selected = vectorizer.transform(selected['Cleaned_Review'])
+feature_names = vectorizer.get_feature_names_out()
+
+# ambil top 5 fitur tiap dokumen
+tfidf_rows = []
+for i in range(tfidf_selected.shape[0]):
+    row = tfidf_selected[i].toarray()[0]
+    top_idx = row.argsort()[-5:][::-1]
+    
+    for j in top_idx:
+        if row[j] > 0:
+            tfidf_rows.append([
+                f"D{i+1}",
+                feature_names[j],
+                float(row[j])
+            ])
+
+# === CREATE EXCEL ===
+wb = Workbook()
+wb.remove(wb.active)
+
+# Cleansing
+ws = wb.create_sheet("Cleansing")
+ws.append(["Dokumen","Teks Asli","Cleaned_Review"])
+for i, row in selected.iterrows():
+    ws.append([f"D{i+1}", row['Review'], row['Cleaned_Review']])
+
+# Case Folding
+ws = wb.create_sheet("CaseFolding")
+ws.append(["Dokumen","Cleaned_Review","Case Folding"])
+for i, row in selected.iterrows():
+    txt = row['Cleaned_Review']
+    ws.append([f"D{i+1}", txt, txt.lower()])
+
+# Stopword
+ws = wb.create_sheet("Stopword")
+ws.append(["Dokumen","Case Folding","Stopword Removal"])
+for i, row in selected.iterrows():
+    txt = row['Cleaned_Review'].lower()
+    ws.append([f"D{i+1}", txt, txt])
+
+# Stemming
+ws = wb.create_sheet("Stemming")
+ws.append(["Dokumen","Stopword Removal","Stemming"])
+for i, row in selected.iterrows():
+    txt = row['Cleaned_Review'].lower()
+    ws.append([f"D{i+1}", txt, txt])
+
+# TF-IDF
+ws = wb.create_sheet("TFIDF")
+ws.append(["Dokumen","Fitur","Bobot TF-IDF"])
+for row in tfidf_rows:
+    ws.append(row)
+
+# save
+wb.save("revisi_real_preprocessing_tfidf.xlsx")
+
+print("✅ File berhasil dibuat!")