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International Journal of Industrial Engineering and Engineering Management
Published by Universitas Atma Jaya Yogyakarta
ISSN : -     EISSN : 26854090     DOI : https://doi.org/10.24002/ijieem
Core Subject : Engineering,
Industrial & Manufacturing Engineering
International Journal of Industrial Engineering and Engineering Management (IJIEEM) is an open access scientific journal that publishes theoretical and empirical peer-reviewed articles, which contribute to advance the understanding of phenomena related with all aspects of Industrial Engineering and Engineering Management
Arjuna Subject : Teknik (semua kategori) - Tenik Industri dan Manufaktur
Articles 3 Documents
 1 
Search results for , issue "Vol. 7 No. 2 (2025)" : 3 Documents clear
Coating Adherence Optimization for 67Ni18Cr5Si4B Alloy Powder by High-Velocity Oxygen Fuel Spray Based on the Grey Wolf Algorithm Method Adekola, Anthony Ozimu; Ogunmola, Bayo Yemisi; Onitiri, Modupe Adeoye; Alozie, Nehemiah Sabinus; Oluwo, Adeyinka; Rajan, John; Jose, Swaminathan; Oke, Sunday Ayoola
International Journal of Industrial Engineering and Engineering Management Vol. 7 No. 2 (2025)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v7i2.7874

Abstract

Adhesion engineers increasingly use coatings in industrial equipment on gas turbine blades and vanes because of the benefits of protection against thermal stresses, oxidation, and hot corrosion. However, the coating process has suffered sub-optimal value determination, posing a serious threat to the economics of coating. While the prevailing approach of introducing the Taguchi method appears effective in resolving this issue, it sacrifices convergence speed and multiple optimization solutions. Thus, the grey wolf algorithm is proposed to optimize the coating of 67Ni18Cr5Si4B alloy powder process parameters, including powder feed rate, spray velocity, and spray distance. The high-velocity oxygen fuel spray was used, and the objectives were good microhardness, adhesion strength, and porosity. The optimal value to obtain the best coating for each of the responses was given as 85MPa for the adhesion strength, 0.684909% porosity, and 583.04HV microhardness. The present study offers important insights into the optimization thresholds to help the components development process. The quantitative form of this work is new. Fast convergence solutions offered by metaheuristics such as the grey wolf optimization algorithm are rarely found in the literature.
Gap Identification between Bootcamp Programs and Industrial Needs Utilizing Text Mining Ardyansyah, Muhammad Arief; Purnama, Ignatius Luddy Indra
International Journal of Industrial Engineering and Engineering Management Vol. 7 No. 2 (2025)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v7i2.10099

Abstract

This paper aims to identify the gaps between the bootcamp program and industrial needs using text mining techniques. The case studies focus on bootcamp programs for full-stack laravel vue.js developers, and uiux designers. The data utilized includes curriculum or syllabi from the boot camps and industrial requirements gathered from online job portals, particularly job descriptions from Indeed.com. The analysis revealed a gap of 30% between the bootcamp curriculum and industrial needs for full-stack Laravel vue.js developer, and 20% between the bootcamp curriculum and industrial needs for uiux designer. Based on this identification, alternative solutions were proposed to enhance the curriculum or syllabus. The results of the usability test are OK level. It is indicated that satisfactory outcomes, suggesting that the proposed improvements could help make bootcamp programs more responsive to the evolving needs of the related industry.
Spatial-temporal Pattern and Influencing Factors of Listed Enterprises in China’s Strategic Emerging Industries Dai, Peichao
International Journal of Industrial Engineering and Engineering Management Vol. 7 No. 2 (2025)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v7i2.10561

Abstract

This study analyzes the structure and spatial distribution of listed companies in China's strategic emerging industries (SEIs) from 2010 to 2021, using a quantitative approach. An industrial diversity index is created to assess provincial structures, and spatial agglomeration is examined through a spatial autocorrelation model. The distribution is visualized with kernel density estimation (KDE), and migration patterns of the gravity center are tracked. The key findings are as follows: (1) Significant regional disparities in SEI development exist, with greater diversity in the Yangtze River Delta (YRD), Beijing-Tianjin-Hebei (BTH), and the Pearl River Delta (PRD) compared to other regions; (2) The distribution shows strong positive spatial autocorrelation, indicating a pronounced agglomeration effect; (3) The spatial center of gravity primarily shifts within Central China; (4) The distribution follows a pattern of decreasing concentration from the eastern coastal areas to the western inland regions, with scattered presence in the central and northeastern regions; (5) Key factors such as economic development (DN values), policy support, R&D investments, passenger turnover, and technology market activity play a significant role in shaping the number of listed companies in each region. This analysis offers valuable insights for policymakers aiming to guide regional industrial development.

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