https://lms.onnocenter.or.id/wiki/index.php?action=history&feed=atom&title=Pm4py%3A_COLLAB%3A_source_sederhana_data_csv 2026-04-25T14:59:45Z Revision history for this page on the wiki MediaWiki 1.45.1 https://lms.onnocenter.or.id/wiki/index.php?title=Pm4py:_COLLAB:_source_sederhana_data_csv&diff=72195&oldid=prev 2025-03-29T00:27:49Z

<p>Created page with &quot;==&#039;&#039;&#039;Langkah 1: Copy-Paste ke Google Colab&#039;&#039;&#039;== # Install PM4Py !pip install -q pm4py # Upload file CSV from google.colab import files uploaded = files.upload() # L...&quot;</p> <p><b>New page</b></p><div>==&#039;&#039;&#039;Langkah 1: Copy-Paste ke Google Colab&#039;&#039;&#039;==<br /> <br /> # Install PM4Py<br /> !pip install -q pm4py<br /> <br /> # Upload file CSV<br /> from google.colab import files<br /> uploaded = files.upload()<br /> <br /> # Load CSV<br /> import pandas as pd<br /> df = pd.read_csv(list(uploaded.keys())[0])<br /> <br /> # Tampilkan 5 baris pertama<br /> print(&quot;Contoh isi CSV:&quot;)<br /> print(df.head())<br /> <br /> ==&#039;&#039;&#039;Langkah 2: Format Ulang &amp; Konversi ke Event Log PM4Py&#039;&#039;&#039;==<br /> <br /> from pm4py.objects.log.importer.pandas import importer as pandas_importer<br /> from pm4py.objects.log.util import dataframe_utils<br /> from pm4py.objects.conversion.log import converter as log_converter<br /> <br /> # Ubah nama kolom agar sesuai dengan standar PM4Py<br /> df.columns = [&#039;case:concept:name&#039;, &#039;concept:name&#039;, &#039;time:timestamp&#039;]<br /> df[&#039;time:timestamp&#039;] = pd.to_datetime(df[&#039;time:timestamp&#039;])<br /> <br /> # Konversi ke event log<br /> df = dataframe_utils.convert_timestamp_columns_in_df(df)<br /> log_df = pandas_importer.apply(df)<br /> event_log = log_converter.apply(log_df, variant=log_converter.Variants.TO_EVENT_LOG)<br /> <br /> ==&#039;&#039;&#039;Langkah 3: Tampilkan Visualisasi Proses Sederhana (DFG)&#039;&#039;&#039;==<br /> <br /> from pm4py.algo.discovery.dfg import algorithm as dfg_discovery<br /> from pm4py.visualization.dfg import visualizer as dfg_visualization<br /> <br /> # Buat DFG (Directly-Follows Graph)<br /> dfg = dfg_discovery.apply(event_log)<br /> <br /> # Tampilkan visualisasi DFG<br /> dfg_vis = dfg_visualization.apply(dfg, log=event_log, variant=dfg_visualization.Variants.FREQUENCY)<br /> dfg_visualization.view(dfg_vis)<br /> <br /> ==&#039;&#039;&#039;Catatan Format CSV&#039;&#039;&#039;==<br /> CSV kamu minimal harus punya kolom berikut:<br /> <br /> case_id,activity,timestamp<br /> <br /> Contoh:<br /> <br /> 1,Start,2023-01-01 08:00:00<br /> 1,Process A,2023-01-01 08:30:00<br /> 1,Process B,2023-01-01 09:30:00<br /> 1,End,2023-01-01 10:00:00<br /> <br /> <br /> <br /> ==Pranala Menarik==<br /> <br /> * [[Process Mining]]</div>

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