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Perbandingan Algoritma Apriori dan Fp- Growth Dalam Rekomendasi Barang Pada Sistem Inventaris: Studi Kasus: Toko Dj Sport Kendari

SemanTIK: Teknik Informasi · Vol. 12 No. 1 (2026) · pp. 197–203

DOI
10.55679/semantik.v12i1.263
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

Manajemen inventaris yang efektif merupakan faktor krusial bagi Toko Olahraga DJ Sport Kendari untuk menghindari kerugian akibat kelebihan stok (overstock) atau kekurangan stok (understock). Toko tersebut saat ini menghadapi tantangan dalam pengelolaan persediaan karena proses pencatatan yang masih manual dan ketiadaan sistem rekomendasi barang yang akurat. Penelitian ini bertujuan untuk membandingkan performa algoritma Apriori dan FP-Growth dalam menganalisis pola pembelian pelanggan untuk memberikan rekomendasi barang pada sistem inventaris. Kinerja kedua algoritma dievaluasi berdasarkan kecepatan eksekusi dan nilai confidence yang dihasilkan untuk membentuk aturan asosiasi. Fungsionalitas dan kemudahan penggunaan sistem juga diuji melalui User Acceptance Testing (UAT). Hasil pengujian menunjukkan bahwa Algoritma Apriori menghasilkan aturan asosiasi dengan rata-rata nilai confidence yang lebih tinggi (82.2%), sedangkan FP-Growth mampu menemukan lebih banyak aturan asosiasi (5 aturan) dibandingkan Apriori (3 aturan) dari dataset yang sama. Waktu eksekusi kedua algoritma menunjukkan performa yang hampir setara pada dataset kecil. Sistem ini terbukti memberikan solusi bagi DJ Sport Kendari untuk menemukan pola pembelian pelanggan, mengoptimalkan keputusan stok, dan meningkatkan efisiensi operasional. Berdasarkan hasil UAT, sistem dinilai "Sangat Setuju" oleh pengguna dengan skor akhir 93.44%, yang menandakan sistem telah fungsional, mudah digunakan, dan bermanfaat. Effective inventory management is a crucial factor for DJ Sport Kendari Sports Store in preventing losses caused by overstocking or understocking. The store currently faces challenges in inventory management due to its manual recording process and the absence of an accurate product recommendation system. This study aims to compare the performance of the Apriori and FP-Growth algorithms in analyzing customer purchasing patterns to generate product recommendations within an inventory management system.. The performance of both algorithms was evaluated based on execution time and confidence values generated to produce association rules. The system’s functionality and usability were also assessed through User Acceptance Testing (UAT). The experimental results showed that the Apriori algorithm produced association rules with a higher average confidence value (82.2%), while the FP-Growth algorithm generated a greater number of association rules (five rules) than the Apriori algorithm (three rules) using the same dataset. In terms of execution time, both algorithms demonstrated nearly equivalent performance on a small dataset. The developed system proved to be an effective solution for DJ Sport Kendari Sports Store by identifying customer purchasing patterns, supporting inventory decision-making, and improving operational efficiency. Furthermore, the UAT results indicated that users rated the system as "Strongly Agree", achieving an overall score of 93.44%.

Keywords

Collaboration network of the authors

9 authors · 12 co-authorship links · 2 across different organisations

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

  • Universitas Halu Oleo
  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
La SurimiUniversitas Halu Oleo1
WindaAffiliation unknown1
I Made Alit Dwi SaputraAffiliation unknown1
SubardinAffiliation unknown1
Yuwanda Purnamasari PasrunInstitut Teknologi Sepuluh Nopember1
Amaliya Nurani BasyarahAffiliation unknown1

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">Perbandingan Algoritma Apriori dan Fp- Growth Dalam Rekomendasi Barang Pada Sistem Inventaris : Studi Kasus : Toko Dj Sport Kendari</dc:title>
	<dc:creator>Hidayahtullah, Muhammad Gibran Fitrah</dc:creator>
	<dc:creator>Rizal Adi Saputra</dc:creator>
	<dc:creator>Ilham Julian Efendi</dc:creator>
	<dc:subject xml:lang="en-US">Data Mining</dc:subject>
	<dc:subject xml:lang="en-US">sistem rekomendasi</dc:subject>
	<dc:subject xml:lang="en-US">apriori</dc:subject>
	<dc:description xml:lang="en-US">Manajemen inventaris yang efektif merupakan faktor krusial bagi Toko Olahraga DJ Sport Kendari untuk menghindari kerugian akibat kelebihan stok (overstock) atau kekurangan stok (understock). Toko tersebut saat ini menghadapi tantangan dalam pengelolaan persediaan karena proses pencatatan yang masih manual dan ketiadaan sistem rekomendasi barang yang akurat. Penelitian ini bertujuan untuk membandingkan performa algoritma Apriori dan FP-Growth dalam menganalisis pola pembelian pelanggan untuk memberikan rekomendasi barang pada sistem inventaris. Kinerja kedua algoritma dievaluasi berdasarkan kecepatan eksekusi dan nilai confidence yang dihasilkan untuk membentuk aturan asosiasi. Fungsionalitas dan kemudahan penggunaan sistem juga diuji melalui User Acceptance Testing (UAT). Hasil pengujian menunjukkan bahwa Algoritma Apriori menghasilkan aturan asosiasi dengan rata-rata nilai confidence yang lebih tinggi (82.2%), sedangkan FP-Growth mampu menemukan lebih banyak aturan asosiasi (5 aturan) dibandingkan Apriori (3 aturan) dari dataset yang sama. Waktu eksekusi kedua algoritma menunjukkan performa yang hampir setara pada dataset kecil. Sistem ini terbukti memberikan solusi bagi DJ Sport Kendari untuk menemukan pola pembelian pelanggan, mengoptimalkan keputusan stok, dan meningkatkan efisiensi operasional. Berdasarkan hasil UAT, sistem dinilai "Sangat Setuju" oleh pengguna dengan skor akhir 93.44%, yang menandakan sistem telah fungsional, mudah digunakan, dan bermanfaat.
 Effective inventory management is a crucial factor for DJ Sport Kendari Sports Store in preventing losses caused by overstocking or understocking. The store currently faces challenges in inventory management due to its manual recording process and the absence of an accurate product recommendation system. This study aims to compare the performance of the Apriori and FP-Growth algorithms in analyzing customer purchasing patterns to generate product recommendations within an inventory management system.. The performance of both algorithms was evaluated based on execution time and confidence values generated to produce association rules. The system’s functionality and usability were also assessed through User Acceptance Testing (UAT). The experimental results showed that the Apriori algorithm produced association rules with a higher average confidence value (82.2%), while the FP-Growth algorithm generated a greater number of association rules (five rules) than the Apriori algorithm (three rules) using the same dataset. In terms of execution time, both algorithms demonstrated nearly equivalent performance on a small dataset. The developed system proved to be an effective solution for DJ Sport Kendari Sports Store by identifying customer purchasing patterns, supporting inventory decision-making, and improving operational efficiency. Furthermore, the UAT results indicated that users rated the system as "Strongly Agree", achieving an overall score of 93.44%.</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/263</dc:identifier>
	<dc:identifier>10.55679/semantik.v12i1.263</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol. 12 No. 1 (2026): Vol. 12 No. 1 (2026): SemanTIK Teknik Informasi; 197-203</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/263/77</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Muhammad Gibran Fitrah Hidayahtullah, Rizal Adi Saputra, Ilham Julian Efendi</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>