Pm4py: contoh minimal dari csv - Revision history

Onnowpurbo: /* Persiapan */

2025-03-30T04:57:29Z

Persiapan

Onnowpurbo: /* Minimal Source Code (PM4Py with CSV) */

2025-03-29T23:21:46Z

Minimal Source Code (PM4Py with CSV)

Onnowpurbo at 22:45, 29 March 2025

2025-03-29T22:45:30Z

Onnowpurbo at 22:44, 29 March 2025

2025-03-29T22:44:47Z

Onnowpurbo at 22:44, 29 March 2025

2025-03-29T22:44:10Z

Onnowpurbo: /* Contoh Format File Input (`event_log.csv`): */

2025-03-28T23:44:01Z

Contoh Format File Input (`event_log.csv`):

Onnowpurbo: Created page with "Berikut adalah '''minimal source code''' untuk menggunakan ['''PM4Py'''](https://pm4py.fit.fraunhofer.de/) — library Python yang digunakan untuk '''Process Mining''', biasan..."

2025-03-28T23:43:47Z

Created page with "Berikut adalah '''minimal source code''' untuk menggunakan ['''PM4Py'''](https://pm4py.fit.fraunhofer.de/) — library Python yang digunakan untuk '''Process Mining''', biasan..."

New page

Berikut adalah '''minimal source code''' untuk menggunakan ['''PM4Py'''](https://pm4py.fit.fraunhofer.de/) — library Python yang digunakan untuk '''Process Mining''', biasanya dengan input berupa '''event logs'''. PM4Py umumnya menggunakan file input dalam format '''XES''', '''CSV''', atau '''Parquet'''.

=='''Contoh Minimal: Menggunakan PM4Py dengan File CSV'''==

==Contoh Format File Input (`event_log.csv`):==

case_id,activity,timestamp
1,Start,2024-01-01 08:00:00
1,Check,2024-01-01 08:15:00
1,Approve,2024-01-01 08:45:00
2,Start,2024-01-01 09:00:00
2,Check,2024-01-01 09:10:00
2,Reject,2024-01-01 09:30:00

Kolom wajib:
* `case_id`: ID proses unik (setiap satu proses memiliki banyak aktivitas)
* `activity`: nama aktivitas
* `timestamp`: waktu aktivitas dilakukan

=='''Minimal Source Code (PM4Py with CSV)'''==

import pandas as pd
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
from pm4py.visualization.petrinet import visualizer as pn_visualizer
from pm4py.objects.log.util import dataframe_utils

# 1. Load CSV event log
df = pd.read_csv("event_log.csv")

# 2. Format timestamps properly
df['timestamp'] = pd.to_datetime(df['timestamp'])

# 3. Ensure column names match PM4Py expectations
df = dataframe_utils.convert_timestamp_columns_in_df(df)

# 4. Convert to event log object
event_log = log_converter.apply(df, parameters={
log_converter.Variants.TO_EVENT_LOG.value.Parameters.CASE_ID_KEY: 'case_id'
})

# 5. Discover process model using Alpha Miner
net, initial_marking, final_marking = alpha_miner.apply(event_log)

# 6. Visualize Petri Net
gviz = pn_visualizer.apply(net, initial_marking, final_marking)
pn_visualizer.view(gviz)

==Cara Install PM4Py==

pip install pm4py

> Jika kamu pakai Jupyter atau Google Colab, tambahkan `!pip install pm4py` di atas cell.

Kalau kamu mau pakai format lain seperti '''XES''', tinggal ubah bagian `pd.read_csv(...)` menjadi:

from pm4py.objects.log.importer.xes import factory as xes_importer
log = xes_importer.apply("your_log.xes")

==Pranala Menarik==

* [[Process Mining]]

@@ Line 20: / Line 20: @@
 =='''Persiapan'''==
-  pip install pandas
+  pip install pandas pm4py graphviz
 =='''Minimal Source Code (PM4Py with CSV)'''==

@@ Line 31: / Line 31: @@
   from pm4py.visualization.petrinet import visualizer as pn_visualizer
   from pm4py.objects.log.util import dataframe_utils
   # 1. Load CSV event log
-  df = pd.read_csv("event_log.csv")
+  df = pd.read_csv(csv_filename)
   # 2. Format timestamps properly

@@ Line 17: / Line 17: @@
 * `activity`: nama aktivitas
 * `timestamp`: waktu aktivitas dilakukan
 =='''Minimal Source Code (PM4Py with CSV)'''==

@@ Line 5: / Line 5: @@
 ==Contoh Format File Input (`event_log.csv`):==
-case_id,activity,timestamp
+ case_id,activity,timestamp
 ,Start,2024-01-01 08:00:00
 ,Check,2024-01-01 08:15:00
 ,Approve,2024-01-01 08:45:00
 ,Start,2024-01-01 09:00:00
 ,Check,2024-01-01 09:10:00
 ,Reject,2024-01-01 09:30:00
 Kolom wajib: