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International Journal of Recent Technology and Applied Science (IJORTAS)
Published by Lamintang Education & Training Centre
ISSN : 27212017     EISSN : 27217280     DOI : https://doi.org/10.36079/lamintang.ijortas
Core Subject : Science, Engineering,
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology
The aim of this journal is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of Technology and Applied Science.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "vol 8 no 2: september 2026" : 2 Documents clear
A Low-Cost Arduino-Based Smart Parking Assistance System Using Distance Sensors for Real-Time Vehicle Guidance Harfian Rais; Normalisa
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 2: September 2026
Publisher : Lamintang Education and Training (LET) Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0802.996

Abstract

The growing number of vehicles has made parking increasingly challenging, particularly in areas with limited parking space. Conventional parking methods often rely on drivers' visual judgment, which may lead to inaccurate distance estimation and increase the risk of minor collisions. To address this issue, this study presents the design and implementation of a low-cost smart parking assistance system based on an Arduino Uno microcontroller integrated with an ultrasonic distance sensor and LED indicators that provide real-time visual guidance. The system was developed using the Arduino IDE and experimentally evaluated by comparing sensor measurements with actual distances ranging from 10 to 60 cm, while also examining the accuracy of distance detection and the reliability of LED responses. The experimental results showed stable and consistent performance, with a maximum measurement error of only ±1 cm across all test distances. In addition, the system achieved a 100% success rate in LED indicator activation, where the green, yellow, and red LEDs responded accurately according to the predefined safety thresholds without noticeable delay. These findings demonstrate that the proposed system can provide reliable real-time obstacle detection, helping drivers park more safely while reducing the risk of collisions. Owing to its simple design, low implementation cost, and dependable performance, the proposed system is well suited for residential garages, university campuses, and small commercial parking facilities. Future research should explore the integration of multi-sensor fusion, Internet of Things (IoT)-based remote monitoring, cloud-based data analytics, and artificial intelligence techniques to enable predictive parking occupancy analysis and intelligent parking management for large-scale smart city applications.
Interpretable Machine Learning Framework for Personalized Health Insurance Risk Prediction Mohammed Al-Mhadawi; Qahtan M. Yas
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 2: September 2026
Publisher : Lamintang Education and Training (LET) Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0802.1081

Abstract

In modern health insurance systems, accurate medical expenditure prediction is vital for financial solvency and equitable premium distribution. However, deployment is often hampered by the trade-off between predictive accuracy and model transparency. This study presents a unified, interpretable machine learning regression framework to evaluate personalized health insurance charges using structured tabular data. We benchmarked six predictive architectures (five tree ensembles and a multi-layer perceptron control) on a real-world dataset (n=1,338). Experimental results demonstrate that Gradient Boosting achieved superior performance with R2=0.8789, RMSE = $4,335.47, MAPE = 28.49%, and an operational model footprint of only 170 KB. In contrast, standard deep learning (MLP) failed catastrophically (R2 = −0.3947) due to severe right-skewness and nonlinear tabular feature interactions. Model interpretability via SHAP values identified smoker status (48.2% Gini importance) and BMI interactions as primary cost drivers. Future research will focus on evaluating hybrid TabNet-Boosting architectures and integrating longitudinal temporal claims to capture evolving risk profiles across multinational cohorts.

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