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All Journal Jurnal Proyek Teknik Sipil Nusantara Science and Technology Proceedings Journal of Civil Engineering Science & Technology (CI-TECH)
Mik Wanul Khosiin
Civil Engineering, Universitas Pembangunan Nasional “Veteran” Surabaya, East Java, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Transportation

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Integrating CBS and WBS in Construction Projects by Using Data Analytics Mik Wanul Khosiin; Wei-Chih Wang; Indradi Wijatmiko
Nusantara Science and Technology Proceedings 1st International Conference Eco-Innovation in Science, Engineering, and Technology
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2020.0513

Abstract

In Indonesia, project databases are not well managed, covering hundreds or thousands of cost items and work items from construction projects. This condition becomes more complex when operators are required to integrate between CBS and WBS, which requires extra effort to perform detailed, rush, and accurate grouping. This study attempts to demonstrate the role of data science in managing CBS and WBS through the code system method in the pivot table. The analysis process requires four stages, step 1. data preparation, step 2. item relationship, step 3. code system analysis, and step 4. validation and evaluation. The results showed that the codes applied including A1001-A1003 have CBS and WBS derivatives in a systematic and well-structured manner so that automatically the calculation of the quantity and cost that accompanies each of these items obtains accurate results. This article is expected to provide options for field practitioners to perform data management to be efficient and productive for the continuity of construction projects going forward.
MULTIPLE IMPACT ANALYSIS OF PILING WORK ON HIGH WAY PROJECTS USING THE EVM METHOD Mik Wanul Khosiin; Tatang Ari Gumanti; Ali Muktiyanto; Widi Hidayat
Journal of Civil Engineering Science and Technology (CI-TECH) Vol. 3 No. 01 (2022): April 2022
Publisher : PROGRAM STUDI TEKNIK SIPIL - UPN "VETERAN" JAWA TIMUR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ci-tech.v3i01.55

Abstract

A toll road is one of the economic growth facilities of a country, Indonesia in the last seven years has succeeded in building 1900 km and one of them is Trans-Java. The Gempol-Pasuruan section-2 toll road project is a densely populated route and there are many intersections in the form of roads and rivers so there are many overpass or underpass structures in the project. Although the bridge structure is a sub-project of the main road, if the productivity does not perform well, it will have a significant impact on the overall progress. Therefore, this study wants to analyze the multiple impacts of one of the critical works of the bridge project, such as the pilling work on the total toll road progress through the earn value management (EVM) method. EVM is a technique that is quite popular both among practitioners and academics; however, analysis of complex projects (main projects-sub projects) has never been carried out in-depth and it will be a unique challenge in this study. There are four steps in this research, the first step is project scheduling in which the engineer can use the previous database to prepare the work breakdown structure (WBS), work sequences, and project duration. The second step is the critical path process, at this stage, a slack analysis is carried out for each activity, and setting a network diagram to generate critical paths for the toll-bridge project. Resource management is the third step that involves a lot of documents from the market and internal databases, starting with assigning resources, inputting quantity and unit prices, and doing the leveling process so that the supply chain runs well. The last step is the earn value process, at this stage, the engineer only needs to input the project progress, set the baseline, and finally get the earn value project and project performance index consisting of SPI (schedule) and CPI (cost). Finally, after going through several stages of research, it has been proven that the pilling work is on a critical path and several scenarios must be carried out to maintain its productivity. Then, the average SPI value is 0.94 < 1, while the SPI value is 1 which means that the Gempol-Pasuruan section-2 toll road project is experiencing delays and is within the project budget. In conclusion, the application of the EVM method on MS. Project 2013 is able to evaluate complex projects well and several potential technologies can also be considered in further study to improve the performance.
Achieving optimal contractor selection: an AI-driven particle swarm optimization method Moh Nur Sholeh; Mik Wanul Khosiin; Asri Nurdiana; Shifa Fauziyah
Jurnal Proyek Teknik Sipil Vol 6, No 2 (2023): September
Publisher : Civil Infrastructure Engineering and Architectural Design

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/potensi.2023.19629

Abstract

Contractor selection plays a vital role in project management, where factors such as cost, quality, and time must be carefully considered. This study presents an innovative approach to optimize contractor selection using an AI-driven method based on Particle Swarm Optimization (PSO). The objective is to achieve the best possible selection of contractors by considering multiple criteria simultaneously. Real-world data on cost estimates, quality scores, and project times are collected and normalized for fair comparison. The PSO algorithm is utilized to search for the optimal combination of contractors that minimizes cost, maximizes quality, and minimizes project time. The proposed weighted objective function evaluates the performance of each contractor based on the selected criteria. The results demonstrate the effectiveness of the AI-driven PSO method in achieving optimal contractor selection. The findings highlight the potential of using AI techniques for decision-making in project management, enabling project stakeholders to make informed and data-driven contractor selection decisions. This research contributes to the growing body of knowledge on AI applications in project management and provides practical insights for project managers and stakeholders involved in contractor selection processes.
Co-Authors Ali Muktiyanto Asri Nurdiana Indradi Wijatmiko Moh Nur Sholeh Shifa Fauziyah Tatang Ari Gumanti Wei-Chih Wang Widi Hidayat