Code Skripsi
This is full code for prediction of fuel adulteration
This commit is contained in:
22
Combine.py
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22
Combine.py
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import pandas as pd
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labels = [100, 90, 80, 70, 60, 50]
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df = pd.DataFrame()
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for label in labels:
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df_temp = pd.read_csv(f"Fuel_{label}_new.csv")
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df = pd.concat([df, df_temp], axis=0)
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df.to_csv("Fuel_All.csv", index=False)
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# import pandas as pd
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# labels = [100,90,80,70,60,50]
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# df = pd.DataFrame()
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# for label in labels:
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# df_temp = pd.read_excel(f"Fuel_{label}_new.xlsx")
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# df = pd.concat([df, df_temp], axis=0)
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# df.to_excel("Fuel_All_External.xlsx", index=False)
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62
Concat.py
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62
Concat.py
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import os
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import pandas as pd
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# Path folder yang berisi data txt
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label = 100
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path = "pertalite_murni/"
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# path = f"{label}/"
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# Simpan Data
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myData = []
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for dir, folder, files in os.walk(path):
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# Menggabungkan setiap data dalam folder tertentu
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for file in files:
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df_temp = pd.read_csv(f"{dir}/{file}", delimiter=';')
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data_values = list(df_temp.columns)
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myData.append(data_values)
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# 18 Kolom
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columns = ['410nm', '435nm', '460nm','485nm',
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'510nm', '535nm', '560nm', '585nm',
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'610nm', '645nm', '680nm', '705nm',
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'730nm', '760nm', '810nm', '860nm',
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'900nm', '940nm']
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# Simpan ke dalam CSV
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df = pd.DataFrame(myData, columns=columns)
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df['Label'] = label
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df.to_excel(f"Fuel_Ron90{label}_new2.xlsx", index=False)
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# import os
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# import pandas as pd
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# # Path folder yang berisi data txt
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# label = 100
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# path = f"{label}/"
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# # Simpan Data
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# myData = []
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# for dir, folder, files in os.walk(path):
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# # Menggabungkan setiap data dalam folder tertentu
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# for file in files:
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# df_temp = pd.read_csv(f"{dir}/{file}", delimiter=';')
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# data_values = list(df_temp.columns)
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# myData.append(data_values)
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# # 18 Kolom
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# columns = ['410nm', '435nm', '460nm','485nm',
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# '510nm', '535nm', '560nm', '585nm',
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# '610nm', '645nm', '680nm', '705nm',
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# '730nm', '760nm', '810nm', '860nm',
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# '900nm', '940nm']
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# # Simpan ke dalam Excel
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# df = pd.DataFrame(myData, columns=columns)
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# df['Label'] = label
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# df.head()
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# df.to_excel(f"Fuel_{label}_new.xlsx", index=False)
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BIN
Fuel_All_External.xlsx
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BIN
Fuel_All_External.xlsx
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Binary file not shown.
121
Fuel_All_new.csv
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121
Fuel_All_new.csv
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410nm,435nm,460nm,485nm,510nm,535nm,560nm,585nm,610nm,645nm,680nm,705nm,730nm,760nm,810nm,860nm,900nm,940nm,Label
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5.781670570373535,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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5.781670570373535,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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5.781670570373535,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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5.781670570373535,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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5.781670570373535,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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5.781670570373535,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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5.781670570373535,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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4.95571756362915,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
|
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4.95571756362915,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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4.95571756362915,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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4.95571756362915,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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4.95571756362915,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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4.95571756362915,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.90084648132324,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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4.95571756362915,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.90084648132324,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
|
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4.95571756362915,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.90084648132324,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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4.129764556884766,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,26.90084648132324,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
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4.95571756362915,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
|
||||
4.129764556884766,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
|
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4.129764556884766,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
|
||||
4.129764556884766,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,0.7796502709388733,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,100
|
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3.30381178855896,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,0.8245857357978821,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
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3.30381178855896,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,0.8245857357978821,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,25.406354904174805,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,0.8245857357978821,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,25.406354904174805,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,0.8245857357978821,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,25.406354904174805,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,0.8245857357978821,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,25.406354904174805,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,0.8245857357978821,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,25.406354904174805,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,2.0441787242889404,0.3965269923210144,1.5593005418777466,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,0.8245857357978821,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,25.406354904174805,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,26.153600692749023,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,0.8245857357978821,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
3.30381178855896,0.9738456606864928,8.808282852172852,1.8655965328216555,2.307474136352539,25.406354904174805,0.5287637114524841,0.4831080138683319,1.14484703540802,0.4147160351276397,1.0220893621444702,0.3965269923210144,1.5593005418777466,0.8432264924049377,0.8245857357978821,1.1582142114639282,0.6376231908798218,0.939268171787262,90
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
4.129764556884766,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,23.911863327026367,0.5287637114524841,0.4831080138683319,2.28969407081604,0.4147160351276397,2.0441787242889404,0.3965269923210144,1.5593005418777466,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,50
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||||
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||||
4.129764556884766,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,23.911863327026367,0.5287637114524841,0.4831080138683319,2.28969407081604,0.4147160351276397,1.0220893621444702,0.3965269923210144,2.3389508724212646,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,50
|
||||
4.129764556884766,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,23.911863327026367,0.5287637114524841,0.4831080138683319,2.28969407081604,0.4147160351276397,1.0220893621444702,0.3965269923210144,2.3389508724212646,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,50
|
||||
4.129764556884766,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,23.911863327026367,0.5287637114524841,0.4831080138683319,2.28969407081604,0.4147160351276397,2.0441787242889404,0.3965269923210144,2.3389508724212646,0.8432264924049377,1.6491714715957642,2.3164284229278564,0.6376231908798218,0.939268171787262,50
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||||
4.129764556884766,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,23.911863327026367,0.5287637114524841,0.4831080138683319,2.28969407081604,0.4147160351276397,2.0441787242889404,0.3965269923210144,2.3389508724212646,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,50
|
||||
4.129764556884766,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,23.911863327026367,0.5287637114524841,0.4831080138683319,2.28969407081604,0.4147160351276397,2.0441787242889404,0.3965269923210144,2.3389508724212646,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,50
|
||||
4.129764556884766,0.9738456606864928,9.786980628967283,1.8655965328216555,2.307474136352539,23.911863327026367,0.5287637114524841,0.4831080138683319,2.28969407081604,0.4147160351276397,2.0441787242889404,0.3965269923210144,2.3389508724212646,0.8432264924049377,1.6491714715957642,1.1582142114639282,0.6376231908798218,0.939268171787262,50
|
||||
|
BIN
Fuel_All_new.xlsx
Normal file
BIN
Fuel_All_new.xlsx
Normal file
Binary file not shown.
3444
Fuel_Prediction_New_(1).ipynb
Normal file
3444
Fuel_Prediction_New_(1).ipynb
Normal file
File diff suppressed because one or more lines are too long
418
Uji_model_skripsi (1).ipynb
Normal file
418
Uji_model_skripsi (1).ipynb
Normal file
@ -0,0 +1,418 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"id": "pA16t5i1KdzR"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import tensorflow as tf\n",
|
||||
"from tensorflow.keras.models import load_model\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler\n",
|
||||
"import joblib"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "xIsmloYVK0LT",
|
||||
"outputId": "4e105a41-c4ba-4bd8-9ae8-a5c88ca50f53"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
|
||||
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 1. Muat Model yang Telah Dilatih\n",
|
||||
"model = load_model('model_new.h5')\n",
|
||||
"model_tuning = load_model('model_tuning_spektroskopi_new.h5')\n",
|
||||
"\n",
|
||||
"scaler_X = joblib.load('scaler_X.pkl')\n",
|
||||
"scaler_y = joblib.load('scaler_y.pkl')\n",
|
||||
"\n",
|
||||
"df = pd.read_csv('Fuel_All_External.csv')\n",
|
||||
"X = df.iloc[:, :-1].values\n",
|
||||
"y = df.iloc[:, -1].values\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 237,
|
||||
"metadata": {
|
||||
"id": "ZyT7wj4SmqZA"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def inverse_transform_y(predictions):\n",
|
||||
" return scaler_y.inverse_transform(predictions.reshape(-1, 1)).flatten()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 244,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "OSKzZRPp4d00",
|
||||
"outputId": "ba434853-5829-4c32-d109-df931e3a1b80"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Shape X sebelum transform: (50, 18)\n",
|
||||
"Scaler di-fit dengan jumlah fitur: 18\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(\"Shape X sebelum transform:\", X.shape)\n",
|
||||
"print(\"Scaler di-fit dengan jumlah fitur:\", scaler_X.n_features_in_)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "M6m-JLHv1Srz",
|
||||
"outputId": "d2bbefff-314a-4f7a-de7b-c30dd2c51946"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
|
||||
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X = df.iloc[:, :-1].values\n",
|
||||
"y = df.iloc[:, -1].values\n",
|
||||
"\n",
|
||||
"X = scaler_X.transform(X)\n",
|
||||
"y = scaler_y.transform(y.reshape(-1, 1)).flatten()\n",
|
||||
"\n",
|
||||
"# print(X.shape)\n",
|
||||
"# print(y.shape)\n",
|
||||
"y_preds = []\n",
|
||||
"y_preds_tuning = []\n",
|
||||
"for i in range(len(df)):\n",
|
||||
" # print(f\"Data {i+1}: X = {X[i]}, y = {y[i]}\")\n",
|
||||
" X_pred = X[i].reshape(1, 18, 1)\n",
|
||||
" y_pred = model.predict(X_pred)\n",
|
||||
" y_pred = inverse_transform_y(y_pred)\n",
|
||||
" y_pred_tuning = model_tuning.predict(X_pred)\n",
|
||||
" y_pred_tuning = inverse_transform_y(y_pred_tuning)\n",
|
||||
"\n",
|
||||
" y_preds.append(y_pred)\n",
|
||||
" y_preds_tuning.append(y_pred_tuning)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "9QsKHVhsANkK"
|
||||
},
|
||||
"source": [
|
||||
"#Tanpa Tuning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 248,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "LaACsdQj74wD",
|
||||
"outputId": "bc88a503-f808-4348-f353-e67c09bdc97c"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Data 1: Prediksi = [84.23407]\n",
|
||||
"Data 2: Prediksi = [84.23407]\n",
|
||||
"Data 3: Prediksi = [84.23407]\n",
|
||||
"Data 4: Prediksi = [84.23407]\n",
|
||||
"Data 5: Prediksi = [84.23407]\n",
|
||||
"Data 6: Prediksi = [84.23407]\n",
|
||||
"Data 7: Prediksi = [84.23407]\n",
|
||||
"Data 8: Prediksi = [84.23407]\n",
|
||||
"Data 9: Prediksi = [90.308846]\n",
|
||||
"Data 10: Prediksi = [90.308846]\n",
|
||||
"Data 11: Prediksi = [80.63012]\n",
|
||||
"Data 12: Prediksi = [80.63012]\n",
|
||||
"Data 13: Prediksi = [80.63012]\n",
|
||||
"Data 14: Prediksi = [80.63012]\n",
|
||||
"Data 15: Prediksi = [80.63012]\n",
|
||||
"Data 16: Prediksi = [80.63012]\n",
|
||||
"Data 17: Prediksi = [80.63012]\n",
|
||||
"Data 18: Prediksi = [80.63012]\n",
|
||||
"Data 19: Prediksi = [80.63012]\n",
|
||||
"Data 20: Prediksi = [80.63012]\n",
|
||||
"Data 21: Prediksi = [71.96156]\n",
|
||||
"Data 22: Prediksi = [71.96156]\n",
|
||||
"Data 23: Prediksi = [70.68844]\n",
|
||||
"Data 24: Prediksi = [70.68844]\n",
|
||||
"Data 25: Prediksi = [70.68844]\n",
|
||||
"Data 26: Prediksi = [70.68844]\n",
|
||||
"Data 27: Prediksi = [70.68844]\n",
|
||||
"Data 28: Prediksi = [70.68844]\n",
|
||||
"Data 29: Prediksi = [70.68844]\n",
|
||||
"Data 30: Prediksi = [70.68844]\n",
|
||||
"Data 31: Prediksi = [69.318535]\n",
|
||||
"Data 32: Prediksi = [69.318535]\n",
|
||||
"Data 33: Prediksi = [69.318535]\n",
|
||||
"Data 34: Prediksi = [69.318535]\n",
|
||||
"Data 35: Prediksi = [69.318535]\n",
|
||||
"Data 36: Prediksi = [69.39558]\n",
|
||||
"Data 37: Prediksi = [69.39558]\n",
|
||||
"Data 38: Prediksi = [69.39558]\n",
|
||||
"Data 39: Prediksi = [69.318535]\n",
|
||||
"Data 40: Prediksi = [69.318535]\n",
|
||||
"Data 41: Prediksi = [54.984215]\n",
|
||||
"Data 42: Prediksi = [54.984215]\n",
|
||||
"Data 43: Prediksi = [54.984215]\n",
|
||||
"Data 44: Prediksi = [54.984215]\n",
|
||||
"Data 45: Prediksi = [54.984215]\n",
|
||||
"Data 46: Prediksi = [50.889156]\n",
|
||||
"Data 47: Prediksi = [50.889156]\n",
|
||||
"Data 48: Prediksi = [50.889156]\n",
|
||||
"Data 49: Prediksi = [54.984215]\n",
|
||||
"Data 50: Prediksi = [54.984215]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for i, pred in enumerate(y_preds):\n",
|
||||
" print(f\"Data {i+1}: Prediksi = {pred}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "eNA-rm20APn8"
|
||||
},
|
||||
"source": [
|
||||
"#Tuning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 250,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "pTknD6ehASj0",
|
||||
"outputId": "a230eccc-f474-48a5-9e13-775b7367cfef"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Data 1: Prediksi = [95.58472]\n",
|
||||
"Data 2: Prediksi = [95.58472]\n",
|
||||
"Data 3: Prediksi = [95.58472]\n",
|
||||
"Data 4: Prediksi = [95.58472]\n",
|
||||
"Data 5: Prediksi = [95.58472]\n",
|
||||
"Data 6: Prediksi = [95.58472]\n",
|
||||
"Data 7: Prediksi = [95.58472]\n",
|
||||
"Data 8: Prediksi = [95.58472]\n",
|
||||
"Data 9: Prediksi = [96.007904]\n",
|
||||
"Data 10: Prediksi = [96.007904]\n",
|
||||
"Data 11: Prediksi = [86.576454]\n",
|
||||
"Data 12: Prediksi = [86.576454]\n",
|
||||
"Data 13: Prediksi = [86.576454]\n",
|
||||
"Data 14: Prediksi = [86.576454]\n",
|
||||
"Data 15: Prediksi = [86.576454]\n",
|
||||
"Data 16: Prediksi = [86.576454]\n",
|
||||
"Data 17: Prediksi = [86.576454]\n",
|
||||
"Data 18: Prediksi = [86.576454]\n",
|
||||
"Data 19: Prediksi = [86.576454]\n",
|
||||
"Data 20: Prediksi = [86.576454]\n",
|
||||
"Data 21: Prediksi = [73.77576]\n",
|
||||
"Data 22: Prediksi = [73.77576]\n",
|
||||
"Data 23: Prediksi = [73.02384]\n",
|
||||
"Data 24: Prediksi = [73.02384]\n",
|
||||
"Data 25: Prediksi = [73.02384]\n",
|
||||
"Data 26: Prediksi = [73.02384]\n",
|
||||
"Data 27: Prediksi = [73.02384]\n",
|
||||
"Data 28: Prediksi = [73.02384]\n",
|
||||
"Data 29: Prediksi = [73.02384]\n",
|
||||
"Data 30: Prediksi = [73.02384]\n",
|
||||
"Data 31: Prediksi = [66.95867]\n",
|
||||
"Data 32: Prediksi = [66.95867]\n",
|
||||
"Data 33: Prediksi = [66.95867]\n",
|
||||
"Data 34: Prediksi = [66.95867]\n",
|
||||
"Data 35: Prediksi = [66.95867]\n",
|
||||
"Data 36: Prediksi = [66.1415]\n",
|
||||
"Data 37: Prediksi = [66.1415]\n",
|
||||
"Data 38: Prediksi = [66.1415]\n",
|
||||
"Data 39: Prediksi = [66.95867]\n",
|
||||
"Data 40: Prediksi = [66.95867]\n",
|
||||
"Data 41: Prediksi = [51.689125]\n",
|
||||
"Data 42: Prediksi = [51.689125]\n",
|
||||
"Data 43: Prediksi = [51.689125]\n",
|
||||
"Data 44: Prediksi = [51.689125]\n",
|
||||
"Data 45: Prediksi = [51.689125]\n",
|
||||
"Data 46: Prediksi = [51.19272]\n",
|
||||
"Data 47: Prediksi = [51.19272]\n",
|
||||
"Data 48: Prediksi = [51.19272]\n",
|
||||
"Data 49: Prediksi = [51.689125]\n",
|
||||
"Data 50: Prediksi = [51.689125]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for i, pred in enumerate(y_preds_tuning):\n",
|
||||
" print(f\"Data {i+1}: Prediksi = {pred}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "78_9QUE4wZGK"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
96
detect2.py
Normal file
96
detect2.py
Normal file
@ -0,0 +1,96 @@
|
||||
import tflite_runtime.interpreter as tflite
|
||||
import numpy as np
|
||||
import as7265x
|
||||
import smbus
|
||||
import joblib
|
||||
import RPi.GPIO as GPIO
|
||||
from luma.core.interface.serial import i2c
|
||||
from luma.core.render import canvas
|
||||
from luma.oled.device import ssd1306
|
||||
from time import sleep
|
||||
|
||||
# ======================== Load TF Model ========================
|
||||
interpreter = tflite.Interpreter(model_path="model.tflite")
|
||||
interpreter.allocate_tensors()
|
||||
input_details = interpreter.get_input_details()
|
||||
output_details = interpreter.get_output_details()
|
||||
|
||||
print("Input details:", input_details)
|
||||
print("Output details:", output_details)
|
||||
|
||||
# ======================== Load Scalers ========================
|
||||
scaler_X = joblib.load('scaler_X.pkl')
|
||||
scaler_y = joblib.load('scaler_y.pkl')
|
||||
|
||||
# ======================== GPIO Button Setup ========================
|
||||
BUTTON_PIN = 22 # Gunakan GPIO 22
|
||||
GPIO.setmode(GPIO.BCM)
|
||||
GPIO.setup(BUTTON_PIN, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)
|
||||
|
||||
# ======================== Create Sensor Instance ========================
|
||||
i2c_bus = smbus.SMBus(1)
|
||||
sensor = as7265x.AS7265X(i2c_bus)
|
||||
|
||||
# ======================== OLED Display Setup ========================
|
||||
serial = i2c(port=1, address=0x3C)
|
||||
device = ssd1306(serial, rotate=0)
|
||||
|
||||
# ======================== Scan Function ========================
|
||||
def scan():
|
||||
sensor.begin()
|
||||
sensor.enableBulb(as7265x.LED_WHITE)
|
||||
sensor.enableBulb(as7265x.LED_IR)
|
||||
sensor.enableBulb(as7265x.LED_UV)
|
||||
sensor.setIntegrationCycles(1)
|
||||
sensor.takeMeasurements()
|
||||
|
||||
data = [
|
||||
sensor.getCalibratedA(), sensor.getCalibratedB(), sensor.getCalibratedC(),
|
||||
sensor.getCalibratedD(), sensor.getCalibratedE(), sensor.getCalibratedF(),
|
||||
sensor.getCalibratedG(), sensor.getCalibratedH(), sensor.getCalibratedR(),
|
||||
sensor.getCalibratedI(), sensor.getCalibratedS(), sensor.getCalibratedJ(),
|
||||
sensor.getCalibratedT(), sensor.getCalibratedU(), sensor.getCalibratedV(),
|
||||
sensor.getCalibratedW(), sensor.getCalibratedK(), sensor.getCalibratedL()
|
||||
]
|
||||
|
||||
sensor.disableBulb(as7265x.LED_WHITE)
|
||||
sensor.disableBulb(as7265x.LED_IR)
|
||||
sensor.disableBulb(as7265x.LED_UV)
|
||||
|
||||
data = np.array(data).reshape(1, -1)
|
||||
return data
|
||||
|
||||
# ======================== Predict Function ========================
|
||||
def predict(data):
|
||||
scaled_data = scaler_X.transform(data)
|
||||
scaled_data = scaled_data.reshape((1, 18, 1))
|
||||
|
||||
interpreter.set_tensor(input_details[0]['index'], scaled_data)
|
||||
interpreter.invoke()
|
||||
output_data = interpreter.get_tensor(output_details[0]['index'])
|
||||
output_original = scaler_y.inverse_transform(output_data)
|
||||
|
||||
print("Model output (scaled):", output_data)
|
||||
print("Model output (original):", output_original)
|
||||
|
||||
with canvas(device) as draw:
|
||||
draw.rectangle(device.bounding_box, outline="white", fill="black")
|
||||
draw.text((5, 20), "Prediction Result:", fill="white")
|
||||
draw.text((5, 40), str(np.round(output_original[0], 2)), fill="white")
|
||||
|
||||
# ======================== Main Loop ========================
|
||||
print("Model berhasil diload")
|
||||
print("Tekan tombol untuk memulai prediksi...")
|
||||
|
||||
try:
|
||||
while True:
|
||||
if GPIO.input(BUTTON_PIN) == GPIO.HIGH:
|
||||
print("Tombol ditekan. Memulai scan dan prediksi...")
|
||||
data = scan()
|
||||
predict(data)
|
||||
sleep(1.5) # Debounce delay
|
||||
except KeyboardInterrupt:
|
||||
print("Program dihentikan oleh pengguna.")
|
||||
finally:
|
||||
GPIO.cleanup()
|
||||
print("GPIO dibersihkan.")
|
||||
BIN
model_new.h5
Normal file
BIN
model_new.h5
Normal file
Binary file not shown.
BIN
model_new.tflite
Normal file
BIN
model_new.tflite
Normal file
Binary file not shown.
BIN
model_tuning_spektroskopi_new.h5
Normal file
BIN
model_tuning_spektroskopi_new.h5
Normal file
Binary file not shown.
BIN
model_tuning_spektroskopi_new.tflite
Normal file
BIN
model_tuning_spektroskopi_new.tflite
Normal file
Binary file not shown.
BIN
scaler_X.pkl
Normal file
BIN
scaler_X.pkl
Normal file
Binary file not shown.
BIN
scaler_y.pkl
Normal file
BIN
scaler_y.pkl
Normal file
Binary file not shown.
63
scan_with_button.py
Normal file
63
scan_with_button.py
Normal file
@ -0,0 +1,63 @@
|
||||
import RPi.GPIO as GPIO
|
||||
import time
|
||||
import subprocess
|
||||
import as7265x
|
||||
import smbus
|
||||
import numpy as np
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
# Konfigurasi sensor
|
||||
i2c = smbus.SMBus(1)
|
||||
sensor = as7265x.AS7265X(i2c)
|
||||
sensor.begin()
|
||||
sensor.setIntegrationCycles(1)
|
||||
|
||||
# Konfigurasi tombol
|
||||
BUTTON_PIN = 22 # Gunakan GPIO 22
|
||||
GPIO.setmode(GPIO.BCM)
|
||||
GPIO.setup(BUTTON_PIN, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)
|
||||
|
||||
# Meminta input nama folder
|
||||
folder = input("Masukkan nama folder untuk menyimpan hasil: ")
|
||||
if not os.path.exists(folder):
|
||||
os.makedirs(folder)
|
||||
|
||||
# Fungsi untuk melakukan scanning
|
||||
def scan_and_save():
|
||||
print("Scanning...")
|
||||
sensor.enableBulb(as7265x.LED_WHITE)
|
||||
sensor.enableBulb(as7265x.LED_IR)
|
||||
sensor.enableBulb(as7265x.LED_UV)
|
||||
|
||||
sensor.takeMeasurements()
|
||||
data = [
|
||||
sensor.getCalibratedA(), sensor.getCalibratedB(), sensor.getCalibratedC(),
|
||||
sensor.getCalibratedD(), sensor.getCalibratedE(), sensor.getCalibratedF(),
|
||||
sensor.getCalibratedG(), sensor.getCalibratedH(), sensor.getCalibratedR(),
|
||||
sensor.getCalibratedI(), sensor.getCalibratedS(), sensor.getCalibratedJ(),
|
||||
sensor.getCalibratedT(), sensor.getCalibratedU(), sensor.getCalibratedV(),
|
||||
sensor.getCalibratedW(), sensor.getCalibratedK(), sensor.getCalibratedL()
|
||||
]
|
||||
|
||||
sensor.disableBulb(as7265x.LED_WHITE)
|
||||
sensor.disableBulb(as7265x.LED_IR)
|
||||
sensor.disableBulb(as7265x.LED_UV)
|
||||
|
||||
# Simpan hasil ke dalam file
|
||||
filename = datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + ".txt"
|
||||
file_path = os.path.join(folder, filename)
|
||||
with open(file_path, "w") as file:
|
||||
file.write(";".join(map(str, data)))
|
||||
|
||||
print(f"Data telah disimpan di {file_path}")
|
||||
|
||||
try:
|
||||
print("Tekan tombol untuk memulai scanning...")
|
||||
while True:
|
||||
if GPIO.input(BUTTON_PIN) == GPIO.HIGH:
|
||||
scan_and_save()
|
||||
time.sleep(1) # Hindari pemicuan berulang cepat
|
||||
except KeyboardInterrupt:
|
||||
print("Keluar...")
|
||||
GPIO.cleanup()
|
||||
Reference in New Issue
Block a user