Pm4py: COLLAB: source sederhana data csv

Langkah 1: Copy-Paste ke Google Colab

# Install PM4Py
!pip install -q pm4py

# Upload file CSV
from google.colab import files
uploaded = files.upload()

# Load CSV
import pandas as pd
df = pd.read_csv(list(uploaded.keys())[0])

# Tampilkan 5 baris pertama
print("Contoh isi CSV:")
print(df.head())

Langkah 2: Format Ulang & Konversi ke Event Log PM4Py

from pm4py.objects.log.importer.pandas import importer as pandas_importer
from pm4py.objects.log.util import dataframe_utils
from pm4py.objects.conversion.log import converter as log_converter

# Ubah nama kolom agar sesuai dengan standar PM4Py
df.columns = ['case:concept:name', 'concept:name', 'time:timestamp']
df['time:timestamp'] = pd.to_datetime(df['time:timestamp'])

# Konversi ke event log
df = dataframe_utils.convert_timestamp_columns_in_df(df)
log_df = pandas_importer.apply(df)
event_log = log_converter.apply(log_df, variant=log_converter.Variants.TO_EVENT_LOG)

Langkah 3: Tampilkan Visualisasi Proses Sederhana (DFG)

from pm4py.algo.discovery.dfg import algorithm as dfg_discovery
from pm4py.visualization.dfg import visualizer as dfg_visualization

# Buat DFG (Directly-Follows Graph)
dfg = dfg_discovery.apply(event_log)

# Tampilkan visualisasi DFG
dfg_vis = dfg_visualization.apply(dfg, log=event_log, variant=dfg_visualization.Variants.FREQUENCY)
dfg_visualization.view(dfg_vis)

Catatan Format CSV

CSV kamu minimal harus punya kolom berikut:

case_id,activity,timestamp

Contoh:

1,Start,2023-01-01 08:00:00
1,Process A,2023-01-01 08:30:00
1,Process B,2023-01-01 09:30:00
1,End,2023-01-01 10:00:00

Pranala Menarik

• Process Mining