Sungkono, Kelly Rossa (2016) Process Discovery Untuk Streaming Event Log Menggunakan Model Markov Tersembunyi. Undergraduate thesis, Institut Teknology Sepuluh Nopember.
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Abstract
Process discovery adalah teknik penggalian model proses
dari rangkaian aktivitas yang tercatat dalam event log. Saat ini,
sistem informasi menghasilkan streaming event log dimana event
log dicatat sesuai waktu proses yang terjadi. Online Heuristic
Miner adalah algoritma process discovery yang mampu
menghasilkan model proses dari streaming event log. Kelemahan
dari algoritma Online Heuristic Miner adalah ketidakmampuan
mengatasi incomplete trace pada streaming event log. Incomplete
trace adalah rangkaian aktivitas pada event log yang terpotong
di bagian awal ataupun di bagian akhir. Incomplete trace
mengakibatkan proses secara utuh tidak dapat ditampilkan dalam
model proses sehingga incomplete trace disebut sebagai noise.
Algoritma yang memanfaatkan Model Markov
Tersembunyi digunakan untuk membentuk model proses yang
dapat menangani incomplete trace pada streaming event log.
Model Markov Tersembunyi dipilih karena model ini banyak
digunakan untuk meramalkan data sehingga berguna dalam
memperbaiki incomplete trace. Algoritma yang memanfaatkan
Model Markov Tersembunyi tersebut terdiri atas gabungan dari
metode pembentukan model proses serta metode yang
dimodifikasi. Metode yang dimodifikasi adalah metode Baum-
Welch, Backward serta Viterbi, dimana pemodifikasian
diperuntukkan untuk mengelompokkan observer pada Model
Markov Tersembunyi dan menanggulangi aktivitas yang belum
tercatat dalam Model Markov Tersembunyi.
Uji coba dilakukan dengan menggunakan tiga periode
process discovery, yaitu setiap 5 detik, setiap 10 detik dan setiap
15 detik. Hasil uji coba menunjukkan bahwa algoritma yang
memanfaatkan Model Markov Tersembunyi mampu memperbaiki
incomplete trace sehingga tidak ada aktivitas yang hilang dalam
model proses. Hasil uji coba juga menunjukkan kualitas model
proses dari algoritma yang memanfaatkan Model Markov
Tersembunyi lebih baik dibandingkan kualitas model proses dari
algoritma Online Heuristic Miner. ========== Process discovery is a technique for obtaining process
model from sequences of activities recorded in the event log.
Nowadays, information systems produce a streaming event log
wherein it is recorded according to the execution time. Online
Heuristic Miner is an algorithm of process discovery which
capable of obtaining process models from the streaming event
log. Disadvantage using an Online Heuristic Miner algorithm is
the inability to overcome the incomplete trace on the streaming
event log. The incomplete trace is a truncated trace, either
clipped off at the beginning or clipped off at the end. The
incomplete trace make a whole process cannot be displayed in the
process model, so incomplete trace is noise in process discovery.
An algorithm utilizing Hidden Markov Model is used to
obtain a model process that can handle incomplete traces on a
streaming event log. Hidden Markov Model is chosen because
this model is widely used to predict the data which is useful in
recovering incomplete trace. The algorithm utilizing Hidden
Markov Model consists of a combination of methods of model
process and modified methods. The modified methods are Baum-
Welch, Backward and Viterbi methods, where the modification is
intended to classify observer on Hidden Markov Model and
combat activities that have not been recorded in the Hidden
Markov Model. The experiments are using three period of discovery
process, i.e. every 5 seconds, every 10 seconds and every 15
seconds. Experimental results show that the algorithm utilizing
Hidden Markov Model is capable of recovering incomplete trace,
so all activities are displayed in the process model. The
experiment results also showed the quality of the process models
obtaining by algorithm utilizing Hidden Markov Model is better
than those obtaining algorithms Online Heuristic Miner.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSIf 519.233 Sun p 3100016066340 |
| Uncontrolled Keywords: | Incomplete Trace, Kualitas Model Proses, Metode Baum-Welch, Metode Viterbi, Metode Backward, Model Markov Tersembunyi, Process discovery, Streaming event log, Backward Method, Baum-Welch Method, Hidden Markov Model, Incomplete Trace, Invisible Task, Process discovery, Streaming event log, Quality of Process Model, Viterbi Method. |
| Subjects: | Q Science > QA Mathematics > QA274.7 Markov processes--Mathematical models. |
| Depositing User: | - Davi Wah |
| Date Deposited: | 29 Nov 2019 04:24 |
| Last Modified: | 29 Nov 2019 04:24 |
| URI: | http://repository.its.ac.id/id/eprint/71891 |
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