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All Journal Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan Civilla : Jurnal Teknik Sipil Universitas Islam Lamongan Jurnal Teknika Jurnal JEETech Jurnal Pengabdian Masyarakat Indonesia Jurnal Fortech Jurnal Pemberdayaan: Publikasi Hasil Pengabdian kepada Masyarakat (JPM ITTC ) Jurnal FORTECH
Markhaban Siswanto
Universitas Darul ulum
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Public Health Social Sciences

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Development of an ANFIS-Based Method to Improve the Accuracy of Owner’s Estimated Cost in Construction Cost Management Markhaban Siswanto; Machrus Ali
Civilla : Jurnal Teknik Sipil Universitas Islam Lamongan Vol 11 No 1 (2026): MARET
Publisher : Program Studi Teknik Sipil, Fakultas Teknik, Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/cvl.v11i1.1636

Abstract

An inaccurate Owner’s Estimate Cost (OEC) often triggers procurement failures in the purchasing process, thereby affecting cost performance and the success of government capital expenditure projects in Indonesia from a construction management perspective. The OEC serves as the primary benchmark for assessing the reasonableness of bids in construction procurement; calculation errors may lead to ineffective cost control, financial mismanagement, and regulatory non-compliance. Therefore, this study aims to improve OEC accuracy by developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model supported by a Linear Regression (LR) algorithm to predict price fluctuations that influence cost planning and procurement decisions for public building projects. Project data from state-owned building construction and unit price analysis data for the 2021–2024 period were analyzed to predict 2025 price changes (addenda) for various construction work items. The proposed model achieved strong accuracy, with Root Mean Squared Error (RMSE) values of 0.0108–0.0333 and Mean Absolute Error (MAE) values of 0.0099–0.0261 across multiple work descriptions, indicating a good model fit. These findings confirm that the model outperforms comparable studies in terms of precision and interpretability, and can serve as a data-driven approach to strengthen cost management, estimate planning, and procurement decision-making in construction management.
Co-Authors Abdillah, Ikhwan Achmad Mufliq Agil Haikal, Muhammad Arfaah, Saiful Asnun Parwanti Awang Andhyka Cholil Hasyim Djanah, Miftahul Hasib Al Isbilly, Mohammad Hidayat, Ruslan Kamari, Kamari Lisdiyanto, Angga Lutfiyah, Nanik M Yunus Machrus Ali Machrus Ali Machrus Ali Melati Sukma, Ratih Mohammad Hasib Al Isbilly Muhammad Al Trisendy, Fadhel Muhlasin Muhlasin Muhlasin, Muhlasin Nazariano Rahman Wahyudi Nursalim Putra, Hasannudin Parwanti, Asnun Rizky Aditya Nugroho Rukslin Rukslin Saiful Arfaah Saputro, Arief Slamet Imam Wahyudi Soedarsono Soedarsono Syahri Mu’min Tubagus Fahm Winarti ., Winarti