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Sistem Pemilihan Rekomendasi Produk UMKM Kopi menggunakan Metode K-Nearest Neigbors

SemanTIK: Teknik Informasi · Vol. 11 No. 2 (2025)

DOI
10.55679/semantik.v11i2.177
ISSN
2502-8928, 2460-1446
Published
2025-12-16
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Seiring dengan meningkatnya tren konsumsi kopi di berbagai kalangan dan semakin beragamnya preferensi konsumen terhadap produk kopi, hal ini juga dipicu oleh banyaknya produk kopi bermunculan di pasaran dengan berbagai varian harga, jenis olahan, dan asal kopi yang ditawarkan oleh pelaku Usaha Mikro, Kecil, dan Menengah (UMKM). Semakin banyaknya varian produk yang beredar membuat konsumen semakin sulit menemukan produk kopi yang sesuai dengan preferensi mereka. Metode K-Nearest Neighbors (KNN) menawarkan pendekatan yang efektif untuk membantu konsumen dalam menemukan rekomendasi produk kopi. Penelitian ini mengimplementasikan metode KNN mengukur jarak kedekatan antara preferensi pengguna dan karakteristik produk menggunakan dua metrik pengukuran, yaitu Euclidean dan Manhattan. Hasil evaluasi pengujian menunjukkan bahwa metrik jarak Euclidean memberikan tingkat akurasi tertinggi sebesar 92.2%, diikuti oleh Manhattan sebesar 91.8%. Berdasarkan hasil tersebut, Euclidean merupakan pilihan optimal dalam sistem rekomendasi yang dikembangkan, terbukti mampu memberikan rekomendasi produk kopi kepada konsumen dengan tingkat akurasi mencapai 92,2%. Along with the increasing trend of coffee consumption across various demographics and the growing diversity of consumer preferences for coffee products, this is also driven by the multitude of coffee products emerging in the market with various price ranges, types of processing, and origins offered by micro, small, and medium enterprises (MSMEs). The increasing variety of products available makes it more difficult for consumers to find coffee products that match their preferences. The K-Nearest Neighbors (KNN) method offers an effective approach to help consumers find coffee product recommendations. This research implements the KNN method to measure the proximity between user preferences and product characteristics using two measurement metrics, namely Euclidean and Manhattan. The evaluation results show that the Euclidean distance metric provides the highest accuracy level of 92.2%, followed by Manhattan at 91.8%. Based on these results, Euclidean is the optimal choice in the developed recommendation system and has proven capable of providing coffee product recommendations to consumers with the best recommendation results.

Keywords

Collaboration network of the authors

7 authors · 9 co-authorship links

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

  • Politeknik Negeri Sriwijaya
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dimas AndreansyahPoliteknik Negeri Sriwijaya1
Suroso SurosoPoliteknik Negeri Sriwijaya1
Nur HopipahPoliteknik Negeri Sriwijaya1
Mohammad FadhliPoliteknik Negeri Sriwijaya1

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) 
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	<dc:title xml:lang="en-US">Sistem Pemilihan Rekomendasi Produk UMKM Kopi menggunakan Metode K-Nearest Neigbors</dc:title>
	<dc:creator>M. Ardiansyah</dc:creator>
	<dc:creator>Taqwa, Ahmad</dc:creator>
	<dc:creator>Ade Silvia Handayani</dc:creator>
	<dc:subject xml:lang="en-US">K-Nearest Neighbor</dc:subject>
	<dc:subject xml:lang="en-US">Kopi</dc:subject>
	<dc:subject xml:lang="en-US">Jarak Metrik Euclidean</dc:subject>
	<dc:description xml:lang="en-US">Seiring dengan meningkatnya tren konsumsi kopi di berbagai kalangan dan semakin beragamnya preferensi konsumen terhadap produk kopi, hal ini juga dipicu oleh banyaknya produk kopi bermunculan di pasaran dengan berbagai varian harga, jenis olahan, dan asal kopi yang ditawarkan oleh pelaku Usaha Mikro, Kecil, dan Menengah (UMKM). Semakin banyaknya varian produk yang beredar membuat konsumen semakin sulit menemukan produk kopi yang sesuai dengan preferensi mereka. Metode K-Nearest Neighbors (KNN) menawarkan pendekatan yang efektif untuk membantu konsumen dalam menemukan rekomendasi produk kopi. Penelitian ini mengimplementasikan metode KNN mengukur jarak kedekatan antara preferensi pengguna dan karakteristik produk menggunakan dua metrik pengukuran, yaitu Euclidean dan Manhattan. Hasil evaluasi pengujian menunjukkan bahwa metrik jarak Euclidean memberikan tingkat akurasi tertinggi sebesar 92.2%, diikuti oleh Manhattan sebesar 91.8%. Berdasarkan hasil tersebut, Euclidean merupakan pilihan optimal dalam sistem rekomendasi yang dikembangkan, terbukti mampu memberikan rekomendasi produk kopi kepada konsumen dengan tingkat akurasi mencapai 92,2%.
Along with the increasing trend of coffee consumption across various demographics and the growing diversity of consumer preferences for coffee products, this is also driven by the multitude of coffee products emerging in the market with various price ranges, types of processing, and origins offered by micro, small, and medium enterprises (MSMEs). The increasing variety of products available makes it more difficult for consumers to find coffee products that match their preferences. The K-Nearest Neighbors (KNN) method offers an effective approach to help consumers find coffee product recommendations. This research implements the KNN method to measure the proximity between user preferences and product characteristics using two measurement metrics, namely Euclidean and Manhattan. The evaluation results show that the Euclidean distance metric provides the highest accuracy level of 92.2%, followed by Manhattan at 91.8%. Based on these results, Euclidean is the optimal choice in the developed recommendation system and has proven capable of providing coffee product recommendations to consumers with the best recommendation results.</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:date>2025-12-16</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/177</dc:identifier>
	<dc:identifier>10.55679/semantik.v11i2.177</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol. 11 No. 2 (2025): Vol. 11 No. 2 (2025): SemanTIK Teknik Informasi</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/177/57</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 M. Ardiansyah, Ahmad Taqwa, Ade Silvia Handayani</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>