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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Journal of Urban Health Research
Alvina, Jesslyn
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Computer Science & IT Medicine & Pharmacology

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Self-Protection against UV Exposure: Behavioral Patterns and Phototype Correlations among Medical Students in North Jakarta, Indonesia Alvina, Jesslyn; Arieselia, Zita; Regina, Regina
Journal of Urban Health Research Vol. 3 No. 3 (2025): Journal of Urban Health Research
Publisher : School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/juhr.v3i3.6631

Abstract

Introduction: Exposure to ultraviolet (UV) rays can cause significant skin damage, including cancer. This study examines sun protection behaviors among medical students in North Jakarta, despite their knowledge of UV risks, and correlates these behaviors with Fitzpatrick skin types. Understanding these behaviors helps inform targeted interventions to promote sun safety among future healthcare professionals. Methods: An observational study with a cross-sectional approached on 230 respondents consisting of medical students, conducted through online questionnaires within 3-month period on a systematic random sampling method. Data on demographic data, Fitzpatrick skin type scale, and 5 questions on self-protection behavior obtained was analyzed using Spearman’s rank correlation and chi-square analysis, p < 0.05 indicating a significant relationship. Results: Type III, IV, and V are the most common Fitzpatrick’s phototypes found on subjects where 69.6% of students had low sun protection behavior. Male exhibit lower sun protection behavior than female, and there was no relationship between students’ Fitzpatrick's skin type and sun protection behavior, (p = 0.112). Conclusions: Sun protection behavior among medical students at FKIK UAJ is low. There is a correlation between gender and sun protection behavior, but no correlation between Fitzpatrick's skin type and sun protection behavior among the students. The study reveals that medical students, especially males, exhibit low sun protection behaviors despite their knowledge of UV risks, emphasizing the need for targeted educational interventions. These findings are crucial for medical education and public health, making them relevant for journals focused on preventive medicine, dermatology, and medical education.  
Implementation of Grid Search Optimization Algorithm and Adaptive Response Rate Exponential Smoothing for Hyperparameter Tuning in Production Activity Determination Sanjaya, Federico; Alvina, Jesslyn; Putra, Muhammad Amsar; Sitanggang, Delima
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 1 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i1.6593

Abstract

This research aims to improve the accuracy of production planning at PT Bilah Baja Makmur Abadi by combining the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm and Grid Search optimization. The main problems faced are unpredictable demand fluctuations, dead stock risks, and high operational costs due to imbalances between production and demand. The ARRES algorithm is used for demand forecasting with adaptive exponential weighting, while Grid Search optimizes the alpha and initial year parameters to improve prediction accuracy. This study uses a 5-year sales dataset (2017-2021) with model evaluation using Mean Absolute Percentage Error (MAPE). The results showed that the combination of Grid Search and ARRES optimization algorithms proved effective in helping predict production needs. This can be seen from the significant decrease in the average MAPE value, which is 7.07% using this combination method, compared to 8.18% in the ARRSES method. The lower MAPE value indicates that the Grid Search method is effective in optimizing the ARRSES model parameters. With relatively high prediction accuracy (MAPE < 10%), this method is able to cope with unexpected demand fluctuations.
Co-Authors Delima Sitanggang, Delima Putra, Muhammad Amsar Regina Regina Sanjaya, Federico Zita Arieselia