PENERAPAN FAST CORNER DETECTION UNTUK OPTIMALISASI PLANE FINDER VUFORIA PADA SISTEM PEMBELAJARAN PENGENALAN OBJEK ASD

Muhammad Dihya Al Qalby, . (2026) PENERAPAN FAST CORNER DETECTION UNTUK OPTIMALISASI PLANE FINDER VUFORIA PADA SISTEM PEMBELAJARAN PENGENALAN OBJEK ASD. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

[img] Text
ABSTRAK.pdf
Download (586kB)
[img] Text
AWAL.pdf
Download (3MB)
[img] Text
BAB 1.pdf
Restricted to Repository UPNVJ Only
Download (269kB)
[img] Text
BAB 2.pdf
Restricted to Repository UPNVJ Only
Download (928kB)
[img] Text
BAB 3.pdf
Restricted to Repository UPNVJ Only
Download (807kB)
[img] Text
BAB 4.pdf
Restricted to Repository UPNVJ Only
Download (2MB)
[img] Text
BAB 5.pdf
Download (254kB)
[img] Text
DAFTAR PUSTAKA.pdf
Download (231kB)
[img] Text
RIWAYAT HIDUP.pdf
Restricted to Repository staff only
Download (122kB)
[img] Text
LAMPIRAN.pdf
Restricted to Repository UPNVJ Only
Download (1MB)
[img] Text
HASIL PLAGIARISME.pdf
Restricted to Repository staff only
Download (40MB)
[img] Text
ARTIKEL JURNAL KI.pdf
Restricted to Repository staff only
Download (291kB)

Abstract

Conventional learning media are considered insufficient in sustaining the attention and engagement of children with Autism Spectrum Disorder (ASD), while visual instability in digital media (drift, jitter, floating) may trigger frustration or tantrums. This study applies the FAST Corner Detection algorithm as an independent measurement module running alongside Vuforia's Plane Finder within a Unity-based markerless Augmented Reality learning application, to evaluate the relationship between visual feature density (corner count) and virtual object placement stability. Development was conducted using the Multimedia Development Life Cycle (MDLC) method, with testing across 24 sessions covering daytime, afternoon, and nighttime lighting conditions. Mann-Whitney U test results show a significant drift reduction of 72.57–100% and floating reduction of 89.96–100% in FAST mode compared to Baseline, while jitter showed no significant difference, being more influenced by device sensors than visual feature density. User Acceptance Testing (UAT) conducted with an ASD therapist yielded a score of 4.94 out of 5.00 (98.75%), categorized as Highly Feasible. This research provides empirical grounding that visual feature density is associated with markerless AR tracking stability, despite the system architecture not involving direct feedback between the two modules. Keywords: Augmented Reality, Autism Spectrum Disorder, FAST Corner Detection, Plane Finder, Tracking Stability.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 2210511035] [Pembimbing 1: Ridwan Raafi’udin] [Pembimbing 2: Muhammad Panji Muslim] [Penguji 1: Muhammad Adrezo] [Penguji 2: Ichsan Mardani]
Uncontrolled Keywords: Augmented Reality, Autism Spectrum Disorder, FAST Corner Detection, Plane Finder, Tracking Stability.
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics > QA76 Computer software
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: MUHAMMAD DIHYA AL QALBY
Date Deposited: 28 Aug 2026 09:02
Last Modified: 28 Aug 2026 09:02
URI: http://repository.upnvj.ac.id/id/eprint/51970

Actions (login required)

View Item View Item