https://lms.onnocenter.or.id/wiki/index.php?action=history&feed=atom&title=Pm4py%3A_COLLAB%3A_analisa_bottleneck_dari_csv 2026-04-20T09:59:31Z Revision history for this page on the wiki MediaWiki 1.45.1 https://lms.onnocenter.or.id/wiki/index.php?title=Pm4py:_COLLAB:_analisa_bottleneck_dari_csv&diff=72193&oldid=prev 2025-03-29T00:21:55Z

<p>Created page with &quot;==&#039;&#039;&#039;Langkah 1: Buka Google Colab&#039;&#039;&#039;== Kunjungi: [https://colab.research.google.com](https://colab.research.google.com) ==&#039;&#039;&#039;Langkah 2: Copy-Paste Script Ini ke Colab&#039;&#039;&#039;==...&quot;</p> <p><b>New page</b></p><div>==&#039;&#039;&#039;Langkah 1: Buka Google Colab&#039;&#039;&#039;==<br /> <br /> Kunjungi: [https://colab.research.google.com](https://colab.research.google.com)<br /> <br /> ==&#039;&#039;&#039;Langkah 2: Copy-Paste Script Ini ke Colab&#039;&#039;&#039;==<br /> <br /> # Instalasi PM4Py<br /> !pip install -q pm4py<br /> <br /> # Import library<br /> import pandas as pd<br /> import matplotlib.pyplot as plt<br /> from pm4py.objects.log.util import dataframe_utils<br /> from pm4py.objects.conversion.log import converter as log_converter<br /> from pm4py.objects.log.importer.pandas import importer as pandas_importer<br /> from pm4py.algo.discovery.dfg import algorithm as dfg_discovery<br /> from pm4py.visualization.dfg import visualizer as dfg_visualization<br /> from pm4py.algo.analysis.performance_spectrum import algorithm as performance_spectrum<br /> <br /> # Upload CSV<br /> from google.colab import files<br /> uploaded = files.upload()<br /> <br /> # Load CSV<br /> filename = list(uploaded.keys())[0]<br /> df = pd.read_csv(filename)<br /> <br /> # Ubah nama kolom agar sesuai dengan 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 = pandas_importer.apply(df)<br /> event_log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG)<br /> <br /> # === ANALISIS DAN VISUALISASI === #<br /> <br /> # 1. Visualisasi DFG berdasarkan frekuensi<br /> dfg_freq = dfg_discovery.apply(event_log, variant=dfg_discovery.Variants.FREQUENCY)<br /> dfg_vis = dfg_visualization.apply(dfg_freq, log=event_log, variant=dfg_visualization.Variants.FREQUENCY)<br /> dfg_visualization.view(dfg_vis)<br /> <br /> # 2. Visualisasi bottleneck: Performance Spectrum<br /> ps = performance_spectrum.apply(event_log)<br /> performance_spectrum.visualize(ps)<br /> <br /> ==&#039;&#039;&#039;Langkah 3: Siapkan File CSV&#039;&#039;&#039;==<br /> <br /> Format minimal yang dibutuhkan:<br /> <br /> case_id,activity,timestamp<br /> <br /> Contoh:<br /> <br /> 1,A,2023-01-01 10:00:00<br /> 1,B,2023-01-01 12:00:00<br /> 1,C,2023-01-01 13:30:00<br /> 2,A,2023-01-01 09:00:00<br /> 2,B,2023-01-01 09:45:00<br /> 2,C,2023-01-01 11:00:00<br /> <br /> <br /> ==Pranala Menarik==<br /> <br /> * [[Process Mining]]</div>

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