IMPLEMENTASI ALGORITMA GREEDY MATCHING PADA SISTEM REKOMENDASI JASA TUKANG BANGUNAN

Rosidin; Misbahus  Sudur; Raditya Danar  Dana; Ratna Dewi Lestyorini; Sukarsa

doi:10.37859/seis.v6i2.10848

Authors

Rosidin STMIK IKMI Cirebon
Misbahus Sudur Fakultas Ilmu Komputer, Universitas Nahdlatul Ulama Cirebon
Raditya Danar Dana STMIK IKMI Cirebon
Ratna Dewi Lestyorini STMIK IKMI Cirebon
Sukarsa Fakultas Ilmu Komputer, Universitas Nahdlatul Ulama Cirebon

DOI:

https://doi.org/10.37859/seis.v6i2.10848

Keywords: Greedy Matching, Recommendation System, Construction Worker Services, Web

Abstract

The small-scale construction industry in Warnasari District, Brebes Regency, still faces inefficiencies in finding daily construction workers because the recruitment process is commonly carried out through gathering points and word-of-mouth information. This condition creates income uncertainty for workers and makes it difficult for employers to assess workers’ skills, location, and reputation. This study aims to develop a web-based recommendation system for construction worker services by implementing the Greedy Matching algorithm. The main contribution of this research is the application of weighted matching based on skill, location distance, and performance rating criteria to generate fast and measurable worker recommendations. The system was developed using the Rapid Application Development (RAD) method, while the recommendation process was carried out by calculating the heuristic value of each candidate and selecting the candidate with the highest score as a local optimal solution. The evaluation was conducted through Black Box testing to assess the validity of system functions and a Greedy Matching calculation simulation to evaluate the candidate ranking output. The test results showed that all system features functioned validly with a success rate of 100%. The calculation simulation results showed that the candidate with a score of 97.0 was placed in the first position compared to another candidate with a score of 74.0. These results indicate that the system is able to generate measurable construction worker service recommendations based on the applied weighted criteria.

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