Analisis dan Pemeringkatan Faktor Kritis yang Mempengaruhi Mutu Pengelasan Struktur Baja Menggunakan Relative Importance Index (RII)

Muhammad Firdaus, Mardiaman Mardiaman

Abstract


Welding quality plays a crucial role in determining the strength, safety, and long-term performance of structural steel construction. Welding defects may lead to rework, project delays, additional costs, and reduced structural reliability. Therefore, identifying and prioritizing the factors influencing welding quality is essential for improving construction performance and quality management practices. This study aims to identify and rank the critical factors affecting structural steel welding quality using the Relative Importance Index (RII) method. A quantitative survey approach was employed in this study. Data were collected from 100 construction professionals, including engineers, welders, quality control personnel, welding inspectors, supervisors, and project practitioners involved in structural steel works. The questionnaire was developed based on the AWS D1.1 standard and relevant literature and distributed through Google Forms via WhatsApp between March and April 2026. The research instrument consisted of 24 causal-factor indicators grouped into six dimensions, namely technical aspects, documentation, human resources, supervision, management, and work environment, as well as five welding-quality outcome indicators. Reliability testing showed that the overall instrument achieved a Cronbach’s Alpha value of 0.984, indicating excellent internal consistency. The results of the Relative Importance Index analysis revealed that the five most influential factors affecting welding quality were quality control supervision before, during, and after welding activities, cleanliness of the welding area from contaminants, compliance with AWS D1.1 standards, availability of competent welding inspectors, and the use of calibrated and well-maintained welding equipment. These findings indicate that welding quality is influenced not only by technical factors but also by effective supervision systems, personnel competency, and adherence to quality management procedures. The study provides practical guidance for contractors, consultants, owners, and quality management teams in prioritizing quality improvement strategies for structural steel welding projects.

Keywords


welding quality; structural steel; Relative Importance Index; quality control; welding inspector.

Full Text:

PDF

References


American Welding Society. (2020). AWS D1.1/D1.1M:2020 An American National Standard. In American Welding Society.

Deng, H., Cheng, Y., Feng, Y., & Xiang, J. (2021). SS symmetry Industrial Laser Welding Defect Detection and Image Defect. Symmetry, 13(9), 1731.

Elhendawy, G. A., & El-Taybany, Y. (2025). Machine Vision-Assisted Welding Defect Detection System with Convolutional Neural Networks. International Journal of Precision Engineering and Manufacturing, 26(12), 3185–3194. https://doi.org/10.1007/s12541-025-01281-y

Fan, K., Peng, P., Zhou, H., Wang, L., & Guo, Z. (2021). Real‐time high‐performance laser welding defect detection by combining acgan‐based data enhancement and multi‐model fusion. Sensors, 21(21). https://doi.org/10.3390/s21217304

Hou, W., Zhang, D., Wei, Y., Guo, J., & Zhang, X. (2020). Review on computer aided weld defect detection from radiography images. Applied Sciences (Switzerland), 10(5), 1–16. https://doi.org/10.3390/app10051878

Hu, T., Huang, X., Yang, Z., Liu, Z., Zhao, J., & Xu, Z. (2025). Vision-based welding quality detection of steel bridge components in complex construction environments. Urban Lifeline, 3(1). https://doi.org/10.1007/s44285-025-00038-3

Liang, Y., Yu, B., Ding, M., Hu, W., Jin, Y., & Yuan, Y. (2025). WELD-DETR: A Real-Time Welding Defect Detection Framework with Multi-Scale Feature Fusion and Multi-Kernel Perception Optimization. Sensors, 25(22), 1–30. https://doi.org/10.3390/s25227024

Mursid, M., Nurwidyaningrum, D., & Setiawan, Y. (2023). Asesmen Kelayakan Material Praktek Konstruksi Baja di Teknik Sipil PNJ untuk Menghasilkan Produk Layak Jual. Journal of Research and Inovation in Civil Engineering as Applied Science (RIGID), 1(1), 1–5. https://doi.org/10.58466/rigid.v1i1.1178

Mvola, B., Kah, P., Martikainen, J., & Suoranta, R. (2016). Dissimilar high-strength steels: Fusion welded joints, mismatches, and challenges. Reviews on Advanced Materials Science, 44(2), 146–159.

Palma-Ramírez, D., Ross-Veitía, B. D., Font-Ariosa, P., Espinel-Hernández, A., Sanchez-Roca, A., Carvajal-Fals, H., Nuñez-Alvarez, J. R., & Hernández-Herrera, H. (2024). Deep convolutional neural network for weld defect classification in radiographic images. Heliyon, 10(9). https://doi.org/10.1016/j.heliyon.2024.e30590

Peli, M., Ariani, V., Khadavi, K., & Roza, F. (2024). Kerangka Pengembangan dan Kepemimpinan Budaya Mutu dalam Konstruksi di Indonesia: Tinjauan Studi Literatur. Jurnal Talenta Sipil, 7(2), 725. https://doi.org/10.33087/talentasipil.v7i2.511

Pereira, A. B., & De Melo, F. J. M. Q. (2020). Quality assessment and process management of welded joints in metal construction—A review. Metals, 10(1). https://doi.org/10.3390/met10010115

Purnomo, T. W., Danitasari, F., & Handoko, D. (2023). Weld Defect Detection and Classification based on Deep Learning Method: A Review. Jurnal Ilmu Komputer Dan Informasi (Journal of Computer Science and Information), 16(1), 77–87.

Putri, M. D. A., Devia, Y. P., & Anwar, M. R. (2024). Importance Factor Analysis of Quality Assurance for Contractor Competitiveness. Rekayasa Sipil, 18(3), 281–288. https://doi.org/10.21776/ub.rekayasasipil.2024.018.03.14

Silitonga, D., Mardiaman, M., & Indriasari, I. (2025). Evaluasi Produktivitas Angkat Baja pada Bangunan Pabrik Kelapa Sawit. Jurnal Talenta Sipil, 8(2), 681. https://doi.org/10.33087/talentasipil.v8i2.957

Sipala, L. O. M. A., Mardiaman, & Sukahar, A. G. (2026). Analisis Faktor Dominan Keterlambatan Waktu Pelaksanaan Proyek Rumah Susun Di Papua Barat Daya. Menara: Jurnal Teknik Sipil, 21(2), 59–66. https://doi.org/10.21009/jmenara.v21i2.65421

Sun, H., Ramuhalli, P., & Jacob, R. E. (2023). Machine learning for ultrasonic nondestructive examination of welding defects: A systematic review. Ultrasonics, 127, 106854. https://doi.org/10.1016/j.ultras.2022.106854

Suryawan, M. A. (2026). Implementasi Total Quality Management (TQM) dalam Pengendalian Kualitas pada Proyek Pembangunan Gedung Bertingkat Tinggi di Jakarta. Jurnal Talenta Sipil, 9(1), 730-735. https://doi.org/10.33087/talentasipil.v9i1.1106

Torres-Torres, M., Soto-Diaz, R., Escorcia-Gutierrez, J., & Valls, A. (2026). Comparative evaluation of visual inspection and two-dimensional machine vision for the detection of discontinuities in GMAW welds of AISI 304 stainless steel. MRS Advances, March(1), 1–11. https://doi.org/10.1557/s43580-026-01566-y

Virkkunen, I., Fridolf, P., Rosell, A., & Barsoum, Z. (2022). Automated defect detection in digital radiography of aerospace. 643–671.

Zhang, W., Liu, W., Yu, X., Kang, D., Xiong, Z., Lv, X., Huang, S., & Li, Y. (2025). Deep Learning-Based Automated Detection of Welding Defects in Pressure Pipeline Radiograph. Coatings, 15(7), 1–14. https://doi.org/10.3390/coatings15070808




DOI: http://dx.doi.org/10.33087/talentasipil.v9i2.1422

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Jurnal Talenta Sipil, Faculty of Engineering, Batanghari University
Adress: Fakultas Teknik, Jl.Slamet Ryadi, Broni-Jambi, Kec.Telanaipura, Kodepos: 36122, email: talentasipil.unbari@gmail.com

Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.