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All Journal Jurnal Teknik Industri Terintegrasi (JUTIN) Journal of Mathematics and Applied Statistics
Jaya, Arsan Kumala
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Decision Sciences, Operations Research & Management Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering

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Designing a Web Application for Recognizing Past Learning Using the Laravel Framework Jaya, Arsan Kumala; Hanif, Abdullah; Triadi, Fara; Biabdillah, Fajerin
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.239

Abstract

This study aims to provide information on the application design process using the Laravel framework. This study aims to design a web application that can help higher education institutions manage students who take prior learning recognition (RPL) classes effectively and efficiently. The problem often faced by universities is the difficulty in recording the formal/non-formal education history of RPL students. This application is expected to provide a solution by providing features such as recording education history, training history, conference history, award history, organizational history, and employment history. The system development method used in the design is the System Development Life Cycle (SDLC) by utilizing the Laravel framework as a framework for the system development process. The expected results of this study are a web application that is user-friendly, reliable, and able to increase the efficiency of student data collection in universities.
Impulsive Purchase with Vision Transformer Prediction of Vehicular Perception System for Fast-Food Outlets in Urban Traffic Congestion Biabdillah, Fajerin; Ismayanti, Rika; Hartanto, Subhan; Jaya, Arsan Kumala
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 4 (2025): October
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i4.53140

Abstract

Urban traffic congestion creates a unique environment where drivers are often captive audiences to roadside fast-food outlets and advertisements. This paper proposes a vision-driven impulsive purchase prediction system that simulates human-like vehicle vision using a Vision Transformer (ViT) model to detect fast-food outlet visibility, crowd levels, and promotional banner exposure in real-time. By integrating these visual cues, our system predicts the likelihood of impulsive stopping behavior (the “impulse score”) of drivers in heavy traffic. We collected and analyzed visual data from congested thoroughfares in major Indonesian cities (Jakarta, Surabaya, Bandung) known for severe traffic jams. The proposed ViT-based model was trained to identify key features such as recognizable outlet signage, drive-thru queue lengths, and promotional signage, mirroring the attention patterns of human drivers. Experimental results demonstrate that the model achieves high accuracy in detecting relevant cues and predicting impulsive purchase decisions, with a mean absolute percentage error (MAPE) of around 12% in forecasting impulse stop rates. This work is the first to leverage a transformer-driven computer vision approach for modeling consumer impulsivity in traffic, bridging automotive perception and marketing analytics. The findings suggest that smart vehicle systems and urban planners can benefit from such technology to anticipate consumer behavior in traffic, optimize roadside advertising, and manage congestion-related demand surges at fast-food outlets.
Co-Authors Biabdillah, Fajerin Hanif, Abdullah Hartanto, Subhan Ismayanti, Rika Triadi, Fara