https://onnocenter.or.id/wiki/index.php?action=history&feed=atom&title=Pm4py%3A_analisa_bottleneck_dari_csv 2026-05-04T19:11:44Z Revision history for this page on the wiki MediaWiki 1.35.4 https://onnocenter.or.id/wiki/index.php?title=Pm4py:_analisa_bottleneck_dari_csv&diff=72230&oldid=prev 2025-03-30T04:58:53Z

<p><span dir="auto"><span class="autocomment">1. Install PM4Py (jika belum)</span></span></p> <table class="diff diff-contentalign-left diff-editfont-monospace" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 04:58, 30 March 2025</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=='''1. Install PM4Py (jika belum)'''==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=='''1. Install PM4Py (jika belum)'''==</div></td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>  pip install pm4py</div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>  pip install pm4py <ins class="diffchange diffchange-inline">graphviz pandas</ins></div></td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=='''2. Contoh CSV Format'''==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=='''2. Contoh CSV Format'''==</div></td></tr> </table>

Onnowpurbo https://onnocenter.or.id/wiki/index.php?title=Pm4py:_analisa_bottleneck_dari_csv&diff=72191&oldid=prev 2025-03-29T00:17:51Z

<p>Created page with &quot;Berikut adalah contoh &#039;&#039;&#039;source code Python&#039;&#039;&#039; menggunakan pustaka &#039;&#039;&#039;PM4Py&#039;&#039;&#039; untuk menganalisis *bottleneck* dalam proses bisnis menggunakan file &#039;&#039;&#039;CSV&#039;&#039;&#039; dan menghasilkan...&quot;</p> <p><b>New page</b></p><div>Berikut adalah contoh '''source code Python''' menggunakan pustaka '''PM4Py''' untuk menganalisis *bottleneck* dalam proses bisnis menggunakan file '''CSV''' dan menghasilkan '''visualisasi bottleneck''' dalam bentuk grafik:<br /> <br /> =='''1. Install PM4Py (jika belum)'''==<br /> <br /> pip install pm4py<br /> <br /> =='''2. Contoh CSV Format'''==<br /> <br /> Pastikan CSV kamu punya format seperti ini:<br /> <br /> case_id,activity,timestamp<br /> 1,A,2023-01-01 10:00:00<br /> 1,B,2023-01-01 11:00:00<br /> 1,C,2023-01-01 13:00:00<br /> 2,A,2023-01-01 09:00:00<br /> 2,B,2023-01-01 09:30:00<br /> 2,C,2023-01-01 12:30:00<br /> <br /> <br /> =='''3. Source Code Analisis Bottleneck + Visualisasi'''==<br /> <br /> import pandas as pd<br /> from pm4py.objects.log.util import dataframe_utils<br /> from pm4py.algo.filtering.log.variants import variants_filter<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 /> import matplotlib.pyplot as plt<br /> <br /> # Step 1: Load CSV<br /> df = pd.read_csv(&quot;log.csv&quot;)<br /> <br /> # Step 2: Pastikan kolom sesuai PM4Py format<br /> df.columns = ['case:concept:name', 'concept:name', 'time:timestamp']<br /> df['time:timestamp'] = pd.to_datetime(df['time:timestamp'])<br /> <br /> # Step 3: Konversi ke Event Log<br /> df = dataframe_utils.convert_timestamp_columns_in_df(df)<br /> from pm4py.objects.log.importer.pandas import importer as pandas_importer<br /> log = pandas_importer.apply(df)<br /> <br /> # Step 4: Buat Directly-Follows Graph (DFG)<br /> dfg = dfg_discovery.apply(log)<br /> <br /> # Step 5: Visualisasi DFG<br /> dfg_vis = dfg_visualization.apply(dfg, log=log, variant=dfg_visualization.Variants.FREQUENCY)<br /> dfg_visualization.view(dfg_vis)<br /> <br /> # Step 6: Hitung Performance Spectrum (Durasi antar aktivitas)<br /> ps = performance_spectrum.apply(log) <br /> <br /> # Step 7: Visualisasi Bottleneck (Waktu antar aktivitas)<br /> performance_spectrum.visualize(ps)<br /> <br /> =='''Output:'''==<br /> <br /> * Gambar 1: '''DFG (Directly-Follows Graph)''' menunjukkan urutan aktivitas.<br /> * Gambar 2: '''Performance Spectrum Chart''' yang memperlihatkan durasi waktu antar aktivitas. Di sinilah kamu bisa melihat *bottleneck* (misal, waktu antar A ➝ B terlalu lama dibandingkan aktivitas lainnya).<br /> <br /> =='''Tips'''==<br /> <br /> * Jika ingin melihat bottleneck lebih detail:<br /> <br /> from pm4py.statistics.traces.log import case_statistics<br /> variants_count = case_statistics.get_variant_statistics(log)<br /> variants_count = sorted(variants_count, key=lambda x: x['count'], reverse=True)<br /> for variant in variants_count:<br /> print(variant)<br /> <br /> <br /> ==Pranala Menarik==<br /> <br /> * [[Process Mining]]</div>

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