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JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Published by Universitas Lambung Mangkurat
ISSN : 25275399     EISSN : 25282514     DOI : http://dx.doi.org/10.20527
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) is intended as a media for scientific studies on the results of research, thinking and analytical-critical studies regarding research in Systems Engineering, Informatics / Information Technology, Information Management and Information Systems. As part of the spirit of disseminating knowledge from the results of research and thought for service to the wider community and as a reference source for academics in the field of Technology and Information.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 142 Documents
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Performance Evaluation of Random Forest for Hypertension Risk Prediction Arya Ardhi Baskara
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 2 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i2.483

Abstract

Hypertension is a major global health concern and a leading risk factor for cardiovascular disease, stroke, and kidney failure. Early prediction of hypertension is crucial because the condition is often asymptomatic in its initial stages and late detection increases the likelihood of severe complications. This study aims to develop and evaluate a predictive model for hypertension using the Random Forest algorithm, a robust ensemble learning method well-suited for medical data classification. The dataset used in this research was obtained from Kaggle and contains 1,985 records with 11 attributes representing demographic, lifestyle, and clinical risk factors. Preprocessing was performed to ensure data quality, followed by Random Forest classification with different parameter settings. The model was evaluated using 5-fold and 10-fold cross-validation with various numbers of trees ranging from 50 to 250. Performance metrics included accuracy, precision, recall, F1-score, and AUC. Experimental results demonstrated that the Random Forest algorithm achieved consistently high performance, with accuracy above 93%, precision above 95%, recall above 91%, F1-scores above 93%, and AUC values between 0.986 and 0.991. These findings confirm that Random Forest is highly effective and reliable for predicting hypertension risk. The study highlights the algorithm’s potential as a decision-support tool for early detection, enabling preventive measures and improving public health outcomes.
The Application of Augmented Reality in Furniture Purchasing and Evaluation Based on the System Usability Scale (SUS) Muhammad Fajrian Noor; Sofyar Sofyar; Dwipayana Ismulya; M. Utiya Raihan; Aqil Rahmatullah; M. Renald Abdi
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 2 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i2.489

Abstract

The furniture industry is undergoing a significant digital transformation, reshaping how consumers interact and make purchasing decisions. Augmented Reality (AR) technology enables users to visualise furniture products in their actual physical spaces before making a purchase, offering a more interactive, realistic, and personalised shopping experience. This study aims to evaluate the effectiveness of AR technology in supporting online furniture purchasing by applying the ADDIE development model and assessing usability through the System Usability Scale (SUS) method. A total of 35 respondents, representing millennial and Gen Z users aged between 18 untill 35, participated in testing an AR-based furniture shopping application. The research findings indicate that the application achieved an average SUS score of 81.5, which falls into the "Excellent" usability category, signifying a high level of user satisfaction and acceptance. The results also reveal that AR improves consumer confidence in product selection by allowing realistic visualisation of furniture items in users' own environments. Therefore, this study concludes that integrating AR technology in digital commerce not only enhances user experience but also provides an effective marketing strategy for furniture businesses to strengthen customer engagement, trust, and purchase decisions in the digital era.

Page 15 of 15 | Total Record : 142

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