Implementation of Brute Force Algorithm for Digital Land Mapping Information System: Implementasi Algoritma Brute Force untuk Sistem Informasi Pemetaan Tanah Digital

Mohamad  Irfan; Hajra Rasmita  Ngemba; Syaiful Hendra; Syahrullah Syahrullah; Nouval Trezand Lapatta; Odai Amer  Hamid

doi:10.33050/tmj.v10i1.2271

Authors

Mohamad Irfan Tadulako University https://orcid.org/0009-0008-0360-8288 (unauthenticated)
Hajra Rasmita Ngemba Tadulako University https://orcid.org/0000-0001-8727-9267 (unauthenticated)
Syaiful Hendra Tadulako University https://orcid.org/0000-0002-0037-5895 (unauthenticated)
Syahrullah Syahrullah Tadulako University https://orcid.org/0009-0005-4810-9181 (unauthenticated)
Nouval Trezand Lapatta Tadulako University https://orcid.org/0000-0002-0959-9596 (unauthenticated)
Odai Amer Hamid Northern Technical University https://orcid.org/0000-0002-4457-9179 (unauthenticated)

DOI:

https://doi.org/10.33050/tmj.v10i1.2271

Abstract

The Land Asset Mapping Information System of the Palu City Local Government was developed to streamline digital land record management and enhance public service delivery. However, users experience substantial delays averaging 3-5 minutes per query during manual data searches. This study aims to optimize search efficiency by implementing the Brute force string-matching algorithm, allowing users to retrieve precise land records through direct pattern input. A waterfall system development methodology was systematically applied across five phases: requirements analysis, system design, PHP/JavaScript implementation, White Box testing, and maintenance. The research team collaborated closely with 12 technical officers from the City Spatial Planning and Land Office to validate system requirements and evaluate real-world performance. The implementation of the Brute force algorithm reduced average search times by 68\% (from 185s to 59s) while maintaining 100\% accuracy in test datasets containing 5,000+ land records. Rigorous testing confirmed the algorithm's reliability across various edge cases, including partial matches and special character inputs. The application of the Brute force method has transformed the system's search functionality, particularly for frequent queries involving land parcel IDs and owner names. These improvements have increased daily processing capacity by 40\%, significantly benefiting urban planning and dispute resolution workflows. While demonstrating excellent performance for medium-sized datasets, the solution presents opportunities for future enhancement through hybrid approaches combining Brute force with indexing techniques for large-scale deployments beyond 50,000 records.

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