Septiarakhman, Rizal (2018) Pola Pencocokan Workflow untuk Memulihkan Anomali Pada Proses Bisnis. Undergraduate thesis, Institut Teknolog iSepuluh Nopember.
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Abstract
Belakangan ini, banyak perusahaan yang membutuhkan program untuk memodelkan log data mereka. Dikarenakan ini dapat memiliki banyak keuntungan salah satunya untuk mengecek error dari tiap aktivitas yang ada pada log data. Masalahnya log data biasanya sangatlah besar dan tidak dapat dimodelkan manual karena akan sangat membuang waktu dan energi. Maka dari itu, tugas akhir ini ditujukan untuk memunculkan model bisnis proses dari Linear Temporal Logic dan membandingkannya dengan model yang diciptakan langsung dari event log menggunakan metode Pattern Matching. Tugas akhir kali ini menunjukkan kelanjutan dari algoritma declarative miner, untuk membuat pola dalam Linear Temporal Logic (LTL) model dari log data yang dimiliki perusahaan, memiliki performa yang sangat bagus. Itu dibuktikan dari besarnya akurasi perbandingan antara LTL dan Event Log dengan menggunakan Neo4j.
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Nowadays, many companies need a program to model their data logs. As this can have many benefits such as to check the errors of every activity in the data log. The problem is log data is usually very large that it can’t be modelled manually because it will be such a waste of time and energy. Therefore, this research generate a business process model from Linear Temporal Logic and compare it to model generated directly from event log using Pattern Matching. This research shows that an extended algorithm of declarative miner, to create pattern in Linear Temporal Logic (LTL) rule model from Log Data owned by the company, has a very good performance. It is proven by the high accuracy of the matching of LTL and event log using graph in Neo4j.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSIf 005.276 2 Sep p-1 3100018074494 |
| Uncontrolled Keywords: | Control Flow Pattern, Neo4j, Linear Temporal Logic, Graph Database, Pattern Matching |
| Subjects: | Q Science > QA Mathematics > QA76.9.I58 Recommender systems (Information filtering) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Faculty of Information and Communication Technology > Informatics > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Rizal Septiarakhman |
| Date Deposited: | 19 Mar 2018 01:23 |
| Last Modified: | 22 Jul 2020 23:53 |
| URI: | http://repository.its.ac.id/id/eprint/50027 |
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