Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Mantik International Journal Of Computer, Network Security and Information System (IJCONSIST)
Afina Lina Nurlaili
UPN "Veteran" Jawa Timur
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Performance Evaluation of Inductive Miner for Internship Program Workflow Afina Lina Nurlaili; Muhsin Muhsin; Dhian Satria Yudha Kartika
Jurnal Mantik Vol. 5 No. 3 (2021): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Modern organizations involve a high number of activities in their operations, which can be very complex. Process model is used to simplify these complex activities. Various algorithms for discovering process models have been developed in process mining. Process mining can extract important information from each activity in obtained cases. There are three process mining algorithms commonly used: Alpha, Heuristic Miner, and Inductive Miner. Each of the algorithms has its own characteristics. This paper compares these algorithms for the Internship Program. Based on the obtained evaluation, Alpha algorithm can't describe the process based on PKL event log well. It is because loop processes exist. It is also shown that the PKL process is not implementing the SOP well yet. On the other hand, Heuristic Miner neglects minor processes which do not frequently happen and does not describe it in the process model. Inductive miner combines the working principle of Alpha Miner and Heuristic Miner. Overall, the process model that is formed uses the Alpha algorithm which is closest to reality because it has a fitness of 0.96.
Traffic Sign Detection Using Region And Corner Feature Extraction Method Hendra Maulana; Dhian Satria Yudha Kartika; Agung Mustika Riski; Afina Lina Nurlaili
IJCONSIST JOURNALS Vol 3 No 1 (2021): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3139.835 KB) | DOI: 10.33005/ijconsist.v3i1.54

Abstract

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.
Co-Authors Agung Mustika Riski Dhian Satria Yudha Kartika Maulana, Hendra Muhsin