Prayogo, Doddy
Institute of Research and Community Outreach - Petra Christian University

Published : 6 Documents
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Implementasi Metode Metaheuristik Symbiotic Organisms Search Dalam Penentuan Tata Letak Fasilitas Proyek Konstruksi Berdasarkan Jarak Tempuh Pekerja Prayogo, Doddy; Gosno, Richard Antoni; Evander, Richard; Limanto, Sentosa
Jurnal Teknik Industri Vol 19, No 2 (2017): Desember 2017
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.482 KB) | DOI: 10.9744/jti.19.2.103-114

Abstract

Penelitian ini menyelidiki performa dari metode metaheuristik baru bernama symbiotic organisms search (SOS) dalam menentukan tata letak fasilitas proyek konstruksi yang optimal berdasarkan jarak tempuh pekerja. Dua buah studi kasus tata letak fasilitas digunakan untuk menguji akurasi dan konsistensi dari SOS. Sebagai tambahan, tiga metode metaheuristik lainnya, yaitu particle swarm optimization, artificial bee colony, dan teaching–learning-based optimization, digunakan sebagai pembanding terhadap algoritma SOS. Hasil simulasi mengindikasikan bahwa algoritma SOS lebih unggul serta memiliki karakteristik untuk menghasilkan titik konvergen lebih cepat jika dibandingkan dengan metode metaheuristik lainnya dalam proses optimasi tata letak fasilitas proyek konstruksi.
Metaheuristic-Based Machine Learning System for Prediction of Compressive Strength based on Concrete Mixture Properties and Early-Age Strength Test Results Prayogo, Doddy
Civil Engineering Dimension Vol 20, No 1 (2018): MARCH 2018
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (887.032 KB) | DOI: 10.9744/ced.20.1.21-29

Abstract

Estimating the accurate concrete strength has become a critical issue in civil engi­neer­ing. The 28-day concrete cylinder test results depict the concretes characteristic strength which was prepared and cast as part of the concrete work on the project. Waiting 28 days is important to guarantee the quality control of the procedure, even though it is a slow process. This research develops an advanced machine learning method to forecast the concrete compressive strength using the concrete mix proportion and early-age strength test results. Thirty-eight historical cases in total were used to create the intelligence prediction method. The results obtained indicate the effectiveness of the advanced hybrid machine learning strategy in forecasting the strength of the concrete with a comparatively high degree of accuracy calculated using 4 error indicators. As a result, the suggested study can provide a great advantage for construction project managers in decision-making procedures that depend on early strength results of the tests.
A Comparative Study on Bio-Inspired Algorithms in Layout Optimization of Construction Site Facilities Prayogo, Doddy; Sutanto, Jessica Chandra; Suryo, Hieronimus Enrico; Eric, Samuel
Civil Engineering Dimension Vol 20, No 2 (2018): SEPTEMBER 2018
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.94 KB) | DOI: 10.9744/ced.20.2.102-110

Abstract

A good arrangement of site layout on a construction project is a fundamental component of the project’s efficiency. Optimization on site layout is necessary in order to reduce the transportation cost of resources or personnel between facilities. Recently, the use of bio-inspired algorithms has received considerable critical attention in solving the engineering optimization problem. These methods have consistently provided better performance than traditional mathematical-based methods to a variety of engineering problems. This study compares the performance of particle swarm optimization (PSO), artificial bee colony (ABC), and symbiotic organisms search (SOS) algorithms in optimizing site layout planning problems. Three real-world case studies of layout optimization problems have been used in this study. The results show that SOS has a better performance in comparison to the other algorithms. Thus, this study provides useful insights to construction practitioners in the industry who are involved in dealing with optimization problems
OPTIMASI MULTI-OBJEKTIF PERMASALAHAN TIME-COST-QUALITY TRADE-OFF PADA PROYEK SOHO X DENGAN MENGGUNAKAN METODE METAHEURISTIK Ho, Michael; Gozal, Renaldy; Tanojo, Effendy; Prayogo, Doddy
Jurnal Dimensi Pratama Teknik Sipil Vol 7, No 2 (2018): AGUSTUS 2018
Publisher : Jurnal Dimensi Pratama Teknik Sipil

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (537.912 KB)

Abstract

Trade-off antara time, cost, dan quality seringkali terjadi dalam merencanakan sebuah proyek. Tiga aspek ini adalah faktor yang signifikan dalam menentukan pilihan alternatif dalam melaksanakan sebuah proyek. Apabila tiga aspek ini dapat dioptimasikan dengan baik, maka proyek yang direncanakan pun akan diuntungkan. Studi ini mengoptimasi time-cost-quality trade-off (TCQTO) pada proyek SOHO X dengan menggunakan algoritma particle swarm optimization (PSO) dan syimbiotic organisms search (SOS). Proses optimasi dilakukan dengan memilih alternatif-alternatif setiap pekerjaan proyek SOHO X yang memiliki nilai time, cost, dan quality masing-masing sehingga didapatkan sebuah susunan alternatif aktivitas yang menghasilkan solusi yang optimal. Studi ini juga membandingkan kinerja antara algoritma metaheuristik PSO dan SOS dalam mengoptimasikan pilihan-pilihan alternatif setiap aktivitas pada proyek X. Algoritma yang bisa menghasilkan solusi yang lebih optimal akan lebih cocok digunakan dalam penyelesaian masalah TCQTO, dalam penelitian ini SOS berhasil mendapatkan solusi yang lebih optimal. Dengan mengetahui algoritma metaheuristik yang lebih cocok maka perencana dapat lebih tepat dalam menentukan metode metaheuristik yang dipakai dalam merencanakan proyek.
OPTIMASI DESAIN STRUKTUR PORTAL BAJA DENGAN METODE METAHEURISTIK Tjiptarahardja, Giannina Allan; Tjong, Wong Foek; Prayogo, Doddy
Jurnal Dimensi Pratama Teknik Sipil Vol 7, No 2 (2018): AGUSTUS 2018
Publisher : Jurnal Dimensi Pratama Teknik Sipil

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.532 KB)

Abstract

Studi ini menelusuri performa algoritma metaheuristik dalam mengoptimasi struktur portal baja. Algoritma yang digunakan dalam penelitian ini termasuk particle swarm optimization (PSO), differential evolution (DE), teaching–learning-based optimization (TLBO) dan symbiotic organisms search (SOS). Struktur yang akan dianalisa berupa portal 1 bentang 8 lantai, 2 bentang 3 lantai dan 3 bentang 15 lantai. Struktur tersebut diberi pembebanan dan batasan-batasan yang sesuai dengan yang diinginkan. Hasil optimasi dibandingkan dengan studi yang telah dilakukan sebelumnya untuk memastikan validitas program yang telah dibuat. Lalu struktur tersebut diberi batasan sesuai dengan peraturan Indonesia, AISC 2001 (LRFD Specification for Structural Steel Buildings). Hasil eksperimen yang didapat menunjukan bahwa SOS adalah algoritma yang paling bisa diandalkan dalam mengoptimasi struktur berskala besar. SOS meraih standard deviasi terendah dan konvergensi tercepat untuk semua struktur yang ditinjau. Namun DE adalah algoritma yang paling efisien dalam meraih berat struktur minimum.
Optimization of resource leveling problem under multiple objective criteria using a symbiotic organisms search Prayogo, Doddy; Kusuma, Christianto Tirta
Civil Engineering Dimension Vol 21, No 1 (2019): MARCH 2019
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/ced.21.1.43-49

Abstract

Bad scheduling and resource management can cause delays or cost overruns. Optimization in solving resource leveling is necessary to avoid those problems. Several objective criteria are used to solve resource leveling. Each of them has the same objective, which is to reduce the fluctuation of resource demand of the project. This study compares the performance of particle swarm optimization (PSO) and symbiotic organisms search (SOS) in solving resource leveling problems using separate objective functions in order to find which one produces a better solution. The results show that SOS produced a better solution than PSO, and one objective function is better in solving resource leveling than the others.