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All Journal Jurnal Proyek Teknik Sipil Jurnal Pengabdian Vokasi
Shifa Fauziyah
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Civil Engineering, Building, Construction & Architecture Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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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.
Co-Authors Anika Rahma Putri Asri Nurdiana Asri Nurdiana Azizia Kanya Fathiarachman BUDI UTOMO Chan Chow Khuen Hariyono Khoirunnisa Suhandarini Mik Wanul Khosiin Moh Nur Sholeh Nastiti, Helena Putri Nur Fadhilah Prihartini Widiyanti Rehman, Saifur Riza Susanti Sulistiawati Sutanto Sutanto Tasya Amalia Dwiyanti Teguh Hari Sucipto, Teguh Hari Widati Fatmaningrum