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Open access Jurnal SINTA 3

Optimasi Rute Terdekat Lokasi Bengkel di Kota Kendari Menggunakan Algoritma Ant Colony Optimization Berbasis Mobile

SemanTIK: Teknik Informasi · Vol. 12 No. 1 (2026) · pp. 189–196

DOI
10.55679/semantik.v12i1.287
ISSN
2502-8928, 2460-1446
Published
2026-06-30
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Pertumbuhan kendaraan bermotor di Kota Kendari memicu tingginya kebutuhan layanan bengkel, terutama dalam kondisi darurat. Algoritma pencarian rute konvensional sering kali menghadapi kendala efisiensi pada perangkat mobile saat menangani data dalam jumlah besar. Penelitian ini bertujuan mengimplementasikan algoritma Max-Min Ant System (MMAS) untuk optimasi rute terdekat menuju lokasi bengkel. MMAS dipilih sebagai kebaruan untuk mengatasi kelemahan stagnasi pada algoritma Ant Colony Optimization (ACO) standar. Dataset penelitian mencakup 200 titik koordinat bengkel di Kota Kendari yang dikumpulkan melalui Google Maps API. Pengembangan sistem menggunakan metode Rational Unified Process (RUP). Hasil pengujian menunjukkan bahwa algoritma MMAS mampu memproses hingga 50 node dengan rata-rata waktu eksekusi 1,1 detik. Pengujian User Acceptance Testing (UAT) terhadap 20 responden menghasilkan skor 87,86% (Sangat Baik). Kontribusi utama penelitian ini adalah integrasi mekanisme batasan feromon MMAS pada platform mobile yang menghasilkan pencarian rute lebih stabil dan responsif dibandingkan algoritma deterministik dalam skenario data dinamis. Fast and accurate workshop service discovery is crucial for urban mobility, especially during vehicle emergencies. This study develops a mobile-based nearest workshop search system in Kendari City by implementing the Max-Min Ant System (MMAS) algorithm. The MMAS variant is selected to address the limitations of the standard Ant Colony Optimization (ACO) in search stagnation. The study utilizes a dataset of 200 workshop locations throughout Kendari City. System development follows the Rational Unified Process (RUP) methodology. Testing includes White Box, distance accuracy, and algorithm scalability. Results demonstrate that the MMAS algorithm is highly efficient in handling complex routing, processing up to 50 nodes with an average execution time of 1.1 seconds. Evaluation through User Acceptance Testing (UAT) with 20 respondents yielded a score of 87.86%, categorizing the system as "Excellent." This research contributes a responsive and adaptive navigation solution for mobile-based urban vehicle services.

Keywords

Collaboration network of the authors

8 authors · 18 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
LM Bahtiar AksaraUniversitas Halu Oleo1
Ageng Arya Khrysna DwipanggaUniversitas Halu Oleo1
IsnawatyUniversitas Halu Oleo1
Muhammad Ihsan SaritaUniversitas Halu Oleo1
LM. Fid AksaraUniversitas Halu Oleo1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://semantik.uho.ac.id/index.php/journal/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Optimasi Rute Terdekat Lokasi Bengkel di Kota Kendari Menggunakan Algoritma Ant Colony Optimization Berbasis Mobile   </dc:title>
	<dc:creator>Subardin</dc:creator>
	<dc:creator>Teuku, Razqy Sulaiman</dc:creator>
	<dc:creator>Sutardi</dc:creator>
	<dc:subject xml:lang="en-US">Ant Colony Optimization</dc:subject>
	<dc:subject xml:lang="en-US">Max-Min Ant System</dc:subject>
	<dc:subject xml:lang="en-US">Optimasi Rute</dc:subject>
	<dc:subject xml:lang="en-US">Rute Terdekat</dc:subject>
	<dc:description xml:lang="en-US">Pertumbuhan kendaraan bermotor di Kota Kendari memicu tingginya kebutuhan layanan bengkel, terutama dalam kondisi darurat. Algoritma pencarian rute konvensional sering kali menghadapi kendala efisiensi pada perangkat mobile saat menangani data dalam jumlah besar. Penelitian ini bertujuan mengimplementasikan algoritma Max-Min Ant System (MMAS) untuk optimasi rute terdekat menuju lokasi bengkel. MMAS dipilih sebagai kebaruan untuk mengatasi kelemahan stagnasi pada algoritma Ant Colony Optimization (ACO) standar. Dataset penelitian mencakup 200 titik koordinat bengkel di Kota Kendari yang dikumpulkan melalui Google Maps API. Pengembangan sistem menggunakan metode Rational Unified Process (RUP). Hasil pengujian menunjukkan bahwa algoritma MMAS mampu memproses hingga 50 node dengan rata-rata waktu eksekusi 1,1 detik. Pengujian User Acceptance Testing (UAT) terhadap 20 responden menghasilkan skor 87,86% (Sangat Baik). Kontribusi utama penelitian ini adalah integrasi mekanisme batasan feromon MMAS pada platform mobile yang menghasilkan pencarian rute lebih stabil dan responsif dibandingkan algoritma deterministik dalam skenario data dinamis.
 Fast and accurate workshop service discovery is crucial for urban mobility, especially during vehicle emergencies. This study develops a mobile-based nearest workshop search system in Kendari City by implementing the Max-Min Ant System (MMAS) algorithm. The MMAS variant is selected to address the limitations of the standard Ant Colony Optimization (ACO) in search stagnation. The study utilizes a dataset of 200 workshop locations throughout Kendari City. System development follows the Rational Unified Process (RUP) methodology. Testing includes White Box, distance accuracy, and algorithm scalability. Results demonstrate that the MMAS algorithm is highly efficient in handling complex routing, processing up to 50 nodes with an average execution time of 1.1 seconds. Evaluation through User Acceptance Testing (UAT) with 20 respondents yielded a score of 87.86%, categorizing the system as "Excellent." This research contributes a responsive and adaptive navigation solution for mobile-based urban vehicle services.</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:date>2026-06-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://semantik.uho.ac.id/index.php/journal/article/view/287</dc:identifier>
	<dc:identifier>10.55679/semantik.v12i1.287</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol. 12 No. 1 (2026): Vol. 12 No. 1 (2026): SemanTIK Teknik Informasi; 189-196</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://semantik.uho.ac.id/index.php/journal/article/view/287/76</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Subardin, Razqy Sulaiman Teuku, Sutardi</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>