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All Journal Journal of Applied Engineering and Technological Science (JAETS) Journal of Environmental Science and Sustainable Development
Karim, Mohammad Alfin
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Social Sciences

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IMPACT OF URBAN DEVELOPMENT ON UV EXPOSURE: A CLUSTERING AND MACHINE LEARNING ASSESSMENT Sahroni, Taufik Roni, Mr.; Yasin, Verdi; Alfaris, Lulut; Ariefka, Reza; Siagian, Ruben Cornelius; Karim, Mohammad Alfin; Rahdiana, Nana; Suhara, Ade
Journal of Environmental Science and Sustainable Development Vol. 7, No. 2
Publisher : UI Scholars Hub

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Abstract

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling high-dimensional data and strong non-linear interactions, outperforming Random Forest in predicting Ultraviolet A variables. Analysis of variance (ANOVA) showed significant inter-group differences, which were validated by Tukey HSD post-hoc tests. Results showed that Cluster 4 was the region with the highest UV exposure. In contrast, Cluster 5 recorded the lowest, with exposure levels ranging from 6.61 to 15.82, a considerable difference of 9.21. The findings underscore the role of geographic and environmental factors in shaping UV exposure patterns, with implications for public health. Areas with high UV exposure face higher risks, including skin cancer and premature ageing. The predictive accuracy of the XGBoost model highlights its usefulness in addressing UV-related health risks. The study advocates for improved UV protection strategies and informed health policies to mitigate climate change impacts and promote sustainable urban development. The findings suggest that the development of data-driven early warning systems for UV radiation exposure could be implemented to improve public health policy and safety.
Design of A Digitalization System for Machine Scheduling and Allocation in Flexible Job Shop Heavy Equipment Manufacturing Industry Karim, Mohammad Alfin; Sahroni, Taufik Roni
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.5089

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This study aims to develop a digitalized scheduling system based on the Flexible Job Shop (FJS) model to optimize production efficiency in the heavy equipment manufacturing industry. The heavy equipment manufacturing industry faces significant challenges in achieving production efficiency due to its high-mix, low-volume (HMLV) nature and the complexity of production processes. The research follows a structured approach, beginning with Focus Group Discussions (FGDs) to gather stakeholder requirements. These requirements are translated into a House of Quality (HoQ) matrix to prioritize features for the dashboard. A literature review identifies optimal scheduling methods, with a focus on FJS and heuristic scheduling rules. The dashboard is developed using JavaScript, PHP, Node.js, and PostgreSQL, and deployed on Amazon Web Services (AWS). The system undergoes black-box testing to ensure functionality and reliability before implementation. The study identifies the Earliest Due Date (EDD) method as the most effective scheduling approach, with an average delay of 3.2 days, utilization of 29%, and completion time of 14.33 days. The implementation of the digitalized scheduling system increased on-time production from 70.56% to 92.8% and improved production achievement from 92.78% to 97.4%. The dashboard application successfully integrates real-time data, adaptive scheduling, and operational features, such as a start-stop system and machine load recommendations. The findings highlight the importance of digital transformation in manufacturing, particularly in optimizing resource allocation, reducing delays, and improving production efficiency. This research contributes to the field of digitalized scheduling and real-time production management by providing a practical, data-driven solution tailored to the HMLV characteristics of heavy equipment manufacturing.
Co-Authors Ariefka, Reza Lulut Alfaris Rahdiana, Nana Sahroni, Taufik Roni Sahroni, Taufik Roni, Mr. Siagian, Ruben Cornelius Suhara, Ade Yasin, Verdi