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PENDAMPINGAN INVENTARISASI FAKTOR PENYEBAB BANJIR DAN ROB DI DAS KENDAL KABUPATEN KENDAL riza susanti; Asri Nurdiana; Shifa Fauziyah; Sutanto Sutanto
Jurnal Pengabdian Vokasi Vol 1, No 4 (2020): Nopember 2020
Publisher : Sekolah Vokasi Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (523.748 KB) | DOI: 10.14710/jpv.2020.9380

Abstract

Banjir dan rob atau masuknya air laut ke daratan karena pengaruh pasang-surut air laut, akhir-akhir ini menjadi masalah serius di wilayah Pantura Kab. Kendal. Banjir dan rob menjadikan kawasan yang terkena dampaknya menjadi permukiman kumuh karena insfrastruktur dan fasilitas umum yang tidak berfungsi.  Dampak dari banjir dan rob di DAS Kabupaten Kendal antara lain adanya genangan pada pemukiman, ratusan hektar tambak mengalami gagal panen, dan gangguan pada akses jalan pantura. Pemerintah Provinsi Jawa Tengah maupun Kabupaten/kota setempat telah melakukan upaya penanganan lokal baik perencanaan maupun konstruksi tetapi belum saling terintegrasi dengan baik. Untuk itu diperlukan pengaturan lebih lanjut yang salah satunya dengan penyusunan Studi Penanganan Banjir dan Rob di Kabupaten Kendal. Dengan mengetahui penyebab banjir dan rob, diharapkan dapat menunjang perencanaan wilayah, perencanaan dan pengembangan infrastruktur / sarana prasarana dasar, perencanaan perumahan / pemukiman maupun pengembangan ekonomi wilayah dalam upaya mitigasi bencana tersebut.
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.
Water Hyacinth Fiber and Glass Powder in Eco-Based GRC Ceiling Production Zahra, Fathiya Haniffa; Bagas Danu Ega; Shifa Fauziyah; Anno Mahfuda
Rekayasa Sipil Vol. 20 No. 2 (2026): Rekayasa Sipil Vol. 20 No. 2
Publisher : Department of Civil Engineering, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.rekayasasipil.2026.020.02.1

Abstract

GRC (Glassfiber Reinforced Cement) ceiling panels are widely used in building construction in Indonesia due to their strength and durability. However, increasing demand highlights the need for more sustainable and cost-effective alternatives. This study investigates the substitution of conventional materials with environmentally friendly waste: glass powder as a partial replacement for cement, and water hyacinth fibers—commonly regarded as invasive weeds—as a substitute for fiberglass. An experimental approach was applied using variations in water hyacinth fiber content (0%, 15%, 30%, 45%) and glass powder content (0%, 5%, 10%, 12.5%). The optimal density was achieved with a mixture of 30% water hyacinth fiber and 10% glass powder, reaching 1.87 g/cm³, which is 0.06 g/cm³ higher than standard GRC panels. The lowest water absorption, at 12.4%, occurred with a combination of 15% fiber and 5% glass powder—0.9% lower than that of conventional panels. The highest flexural strength, 87.149 kgf/cm², was recorded with 30% water hyacinth fiber and 10% glass powder, exceeding that of standard GRC panels by 10.788 kgf/cm². Cost analysis revealed a significant reduction in production costs. The most cost-efficient composition—30% water hyacinth fiber and 10% glass powder—reduced manufacturing costs by Rp 9,049,248 compared to conventional GRC panels. This study highlights the potential of utilizing locally available waste materials to improve the mechanical properties of GRC ceiling panels while reducing both environmental impact and production costs. The findings offer a promising advancement toward sustainable building material innovation in Indonesia.