Code Skripsi

This is full code for prediction of fuel adulteration

Concat.py

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+import os
+import pandas as pd
+
+# Path folder yang berisi data txt
+label = 100
+path = "pertalite_murni/"
+# path = f"{label}/"
+
+# Simpan Data
+myData = []
+for dir, folder, files in os.walk(path):
+    # Menggabungkan setiap data dalam folder tertentu
+    for file in files:
+        df_temp = pd.read_csv(f"{dir}/{file}", delimiter=';')
+        data_values = list(df_temp.columns)
+        myData.append(data_values)
+
+# 18 Kolom
+columns = ['410nm', '435nm', '460nm','485nm',
+           '510nm', '535nm', '560nm', '585nm',
+           '610nm', '645nm', '680nm', '705nm',
+           '730nm', '760nm', '810nm', '860nm',
+           '900nm', '940nm']
+
+# Simpan ke dalam CSV
+df = pd.DataFrame(myData, columns=columns)
+df['Label'] = label
+df.to_excel(f"Fuel_Ron90{label}_new2.xlsx", index=False)
+
+
+# import os
+# import pandas as pd
+
+# # Path folder yang berisi data txt
+# label = 100
+# path = f"{label}/"
+
+# # Simpan Data
+# myData = []
+# for dir, folder, files in os.walk(path):
+#     # Menggabungkan setiap data dalam folder tertentu
+#     for file in files:
+#         df_temp = pd.read_csv(f"{dir}/{file}", delimiter=';')
+#         data_values = list(df_temp.columns)
+#         myData.append(data_values)
+
+# # 18 Kolom
+# columns = ['410nm', '435nm', '460nm','485nm',
+#            '510nm', '535nm', '560nm', '585nm',
+#            '610nm', '645nm', '680nm', '705nm',
+#            '730nm', '760nm', '810nm', '860nm',
+#            '900nm', '940nm']
+
+# # Simpan ke dalam Excel
+# df = pd.DataFrame(myData, columns=columns)
+# df['Label'] = label
+# df.head()
+# df.to_excel(f"Fuel_{label}_new.xlsx", index=False)
+
+
+
+