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Yuliah Qotimah
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Journal of ICT Research and Applications
Published by Institut Teknologi Bandung
ISSN : 23375787     EISSN : 23385499     DOI : https://doi.org/10.5614/itbj.ict.res.appl.
Core Subject : Science,
Computer Science & IT
Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 2 Documents
 1 
Search results for , issue "Vol. 18 No. 2 (2024): (In Progress)" : 2 Documents clear
Virtual Reality (VR) Method to Improve Sense of Place for Interior Design Studio Students Akhmadi Akhmadi; Athifa Sri Ismiranti; Ahmad Nur Sheha
Journal of ICT Research and Applications Vol. 18 No. 2 (2024): (In Progress)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.18.2.1

Abstract

Virtual reality (VR) technology has emerged in response to recent developments in the 3-dimensional (3D) world. VR enables people to engage in various metaverse world experiences in a more immersive way. Immersive learning is a learning method that uses 3D digital technology to facilitate the learning process by visualization in the classroom. This research used a case study of the Interior Design II studio course taken by level-2 students of the Department of Interior Design, School of Creative Industries, Telkom University, Indonesia. The Interior Design II course requires students to design the interior of a residence with a minimum area of 100 m2. The method of paired sample test analysis was used to assess student’s preferences for pre-test and post-test statements from the VR intervention method in assessing student’s sense of place in the final design of the course. The results showed significant differences in student preferences during the pre-test (481.3% and 790.6%), which increased during the post-test (641.7% and 801%). The paired sample t-test analysis results also showed a Sig (2-tailed) number of 0.000 < 0.05, so there is a significant relationship between the pre-test and post-test intervention.
A Multivariate Fuzzy Weighted K-Modes Algorithm with Probabilistic Distance for Categorical Data Ren-Jieh Kuo; Maya Cendana; Thi Phuong Quyen Nguyen; Ferani E. Zulvia
Journal of ICT Research and Applications Vol. 18 No. 2 (2024): (In Progress)
Publisher : DRPM - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2023.18.2.2

Abstract

Data clustering is a data mining approach that assigns similar data to the same group. Traditionally, cluster similarity considers all attributes equally, but in real-world applications, some attributes may be more important than others. Therefore, this study proposes an algorithm that utilizes multivariate fuzzy weighting to demonstrate the varying importance of each attribute, using a Gini impurity measure for weight assignment. Additionally, the proposed algorithm implements probabilistic distance to reduce sensitivity to noise. Probabilistic distance offers more detailed information and better interpretation than Hamming distance, which ignores attribute positions. Probabilistic distance utilizes information about the attribute’s position within and between clusters. This enhances clustering performance by creating clusters with more similar attributes. Therefore, the proposed Multivariate Fuzzy Weighted K-Modes with Probabilistic Distance for Categorical Data (MFWKM-PD) algorithm, based on the multivariate fuzzy K-modes algorithm, not only considers detailed membership calculations but also considers the varying contributions of attributes and their positions in distance calculation. This study evaluated the proposed MFWKM-PD using several benchmark datasets. The experiments validated that the proposed MFWKM-PD shows promising results compared to other algorithms in terms of accuracy, NMI, and ARI.

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