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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah KOMPUTASI
I Made Wiryana
Gunadarma University
Computer Science & IT Engineering

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Specification and Visualization of Policy Model in RBAC Rafif Favian; I Made Wiryana; Cahyawati Diah Kusumarini
Jurnal Ilmiah KOMPUTASI Vol. 21 No. 2 (2022): Jurnal Ilmiah Komputasi Volume: 21 No. 2, Juni 2022
Publisher : Lembaga Penelitian STMIK Jakarta STI&K

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

Abstract

The application of the State Civil Apparatus Information System is an application that manages all stages of civil service management of the state civil apparatus, in line with technological developments and the needs of the state civil apparatus for digital data, the State Civil Apparatus Information System needs to be developed. There are 18 application developments, one of which is the user management function. The problem with this function is that the user has quite a lot of roles, where 1 user can have access to several applications. Therefore, a clear user management is needed to manage this, namely using access control with an RBAC (Role Based Access Control) approach. The purpose of this study is to produce a role model on user management features using XACML and produce an RBAC policy design on user management access control in the state Civil Apparatus Information System application. The research method consists of: (1) Literature Review, (2) Role Modeling Using XACML, (3) Result Visualization, and (4) Merge Operation Process. This research has produced 6 role models, where the six models have been visualized in the form of a graph in the form of images, visualization is carried out to detect if there is an error in defining roles in the State Civil Apparatus Information System service. and secondly, this research has produced an RBAC policy written using the XACML scheme as its specification.
Store product classification using convolutional neural network I Made Wiryana; Suryadi Harmanto; Alfharizky Fauzi; Imam Bil Qisthi; Zalita Nadya Utami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1439-1447

Abstract

Stores sells consumer goods, mainly food products and other household products at retail. The products sold in stores vary greatly, in order to be time efficient in the fast-paced era and the current technological era requires artificial intelligence technology. In the artificial intelligence branch, there is a specific or detailed learning process known as deep learning. One of the branches of deep learning is the convolutional neural network (CNN). This research intends to employ a CNN architecture to facilitate and streamline the time and cost of the store’s product sorting process. The test is conducted with 1,050 product images divided into 35 labels and divided into three data, namely 80% data training 10% data validation and 10% data test. The image used is preprocessed with a size of 256×256 pixels. The data was trained with six convolution layers and an epoch of 50 with an execution time of 33 minutes so as to achieve an accuracy of 91.37%.
Co-Authors Alfharizky Fauzi Cahyawati Diah Kusumarini Imam Bil Qisthi Rafif Favian Suryadi Harmanto Zalita Nadya Utami