Page 45 - 2023-Vol19-Issue2

41 |
Fig. 6. Distribution of assignment activity frequency
IV. CONCLUSION
Based on the testing result of the Rabobank Group ICT inci-
dent event log with process mining, it is shown in a spaghetti
process model at the discovery stage generated by Disco and
ProM. The use of the fuzzy miner and heuristic miner algo-
rithms with the simplification process produces a graph of
the relationship that can be observed. iDHM is used on the
heuristic miner with direct conformance capability and auto-
matic best dependency settings with parameter observation
frequency thresholds of 0.1, dependency measure thresholds
of 0.9, binding frequency thresholds of 0.1, and condition
quality thresholds of 0.5. In Fuzzy Miner, the preserve thresh-
old value is 0.05, the utility ratio is 0.85, the edge cutoff is
0.05, and the node cutoff is 0.05. In terms of incidents, it is
known that the division of the work area is not good, causing
a team to work on a lot of events while other teams only work
on one event. Therefore, it is necessary to divide the team with
clear and balanced areas and workloads in business processes
so that incidents do not recur.
CONFLICT OF INTEREST
The authors have no conflict of relevant interest to this article.
REFERENCES
[1] E. Bazhenova, S. Buelow, and M. Weske, “Discovering
decision models from event logs,” pp. 237–251, 2016.
[2] B. N. A. Hidayat, A. P. Kurniati, et al., “Process model
extension using heuristics miner:(case study: Incident
management of volvo it belgium),” pp. 73–78, 2016.
[3] R. Mangunsong, A. Kurniati, and M. Sabariah, “Analysis
and implementation of process mining with heuristic
miner algorithm case study: Event logs of rabobank
Andreswari, Millenia, Rizky, Haniyah & Mufti
group ict netherlands,” e-Proceeding of Engineering,
2015.
[4] A. R. C. Maita, L. C. Martins, C. R. Lopez Paz, L. Raf-
ferty, P. C. Hung, S. M. Peres, and M. Fantinato, “A
systematic mapping study of process mining,” Enter-
prise Information Systems, vol. 12, no. 5, pp. 505–549,
2018.
[5] A. A. Hermawan, “Analisis konteks proses bisnis anali-
sis konteks proses bisnis berdasarkan event log business
process context analysis based on event log,” J. Penelit.
dan Pengemb. Komun. dan Inform, vol. 3, no. 1, pp. 133–
149, 2014.
[6] S. Dadashnia, T. Niesen, P. Hake, P. Fettke, N. Mehdiyev,
and J. Evermann, “Identification of distinct usage pat-
terns and prediction of customer behavior,” BPI Chal-
lenge, 2016.
[7] A. Bautista, S. Akbar, A. Alvarez, T. Metzger, and
M. Reaves, “Process mining in information technology
incident management: A case study at volvo belgium.,”
2013.
[8] A. Jalali, “Exploring different aspects of users be-
haviours in the dutch autonomous administrative au-
thority through process cubes,” Business Process Intelli-
gence (BPI) Challenge, 2016.
[9] C. Moreira, E. Haven, S. Sozzo, and A. Wichert, “Pro-
cess mining with real world financial loan applications:
Improving inference on incomplete event logs,” PLoS
One, vol. 13, no. 12, p. e0207806, 2018.
[10] M. A. Agung, A. P. Kurniati, and A. A. Gozali, “Analisis
dan implementasi process mining menggunakan fuzzy
mining (studi kasus: Dataset bpi challenge 2014),” ePro-
ceedings of Engineering, vol. 2, no. 2, 2015.
[11] L. S. Putri and G. Ramantoko, “Implementasi process
mining dengan metode process discovery studi kasus
pada aplikasi integrated flexible learning experience
(iflex),” Jurnal Ekonomi: Journal of Economic, vol. 11,
no. 02, 2020.
[12] I. Singh, A. S. Sabitha, and A. Bansal, “Student perfor-
mance analysis using clustering algorithm,” pp. 294–299,
2016.
[13] A. Jalali, “Graph-based process mining,” pp. 273–285,
2021.
[14] I. H. Kwon, “Book review: Process mining: discovery,
conformance and enhancement of business processes,”