Published: 2022-01-01
Analisis Pengaruh Faktor Penggunaan Baju Baru (Fast fashion) ke Pengguna Baju Bekas (Thrifting) Menggunakan Metode K-Means Clustering (Studi Kasus: Toko Thriftboys.id)
DOI: 10.35870/jtik.v6i1.394
Downloads
Abstract
The emergence of a habit in Indonesia and even the world, namely the use of used clothes (thrifting) became the idea for this research. This new habit is known to be able to reduce the production of textile waste in the world. The purpose of this study was to obtain analysis results that affect the use of new clothes (fast fashion) on the use of used clothes (thrifting). Using the K-means Clustering method and using several parameters, including age, quality, price, and sustainability or awareness. The method of collecting data is through a questionnaire and the research material is the buyers found in the online store Instagram (thriftboys.id). From the results of the clustering process that researchers have done using the K-means algorithm with manual calculations and rapidminer applications, the conclusions consist of; 1) In clusters 1 and 2 the age factor shifting from fast fashion to thhirft is 23 years, while in cluster 3 it is 25 years, 2) In the three clusters the average income that shifts from fast fashion to thhirf is group 3 or the range of 200000 -500000, 3) In the third cluster, people switch from fast fashion to thrift because of the good quality of goods, and 4) In clusters 2 and 3 more people are aware of textile waste for the world. Meanwhile, cluster 1 has the same number of conscious and unconscious waste.
Keywords
Thrift ; Data Mining ; K-Means ; Fast fashion
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 10 No. 3 (2026)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2022
-
License: CC BY 4.0
-
Copyright: © 2022 Authors
-
DOI: 10.35870/jtik.v6i1.394
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Raffi Dima Sampurno
Program Studi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional
Agung Triayudi
Program Studi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional
Ratih Titi Komala Sari
Program Studi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional
-
Putri, D.Y. and Suhartini, R., 2018. Upcycle Busana Casual Sebagai Pemanfaatan Pakaian Bekas. E-Journal, 7(01), pp.12-22.
-
Saputro, R.L., 2018. THRIFTSTORE SURABAYA (Studi Deskriptif Tentang Upaya Mempertahankan Eksistensi Pakaian Bekas Sebagai Budaya Populer di Surabaya) (Doctoral dissertation, Universitas Airlangga).
-
Mustofa, M., 2019. Penerapan Algoritma K-Means Clustering pada Karakter Permainan Multiplayer Online Battle Arena. Jurnal Informatika, 6(2), pp.246-254.
-
Bastian, A., 2018. Penerapan algoritma k-means clustering analysis pada penyakit menular manusia (studi kasus kabupaten Majalengka). Jurnal Sistem Informasi, 14(1), pp.28-34.
-
Triayudi, A. and Nathasia, N.D., 2020. Analysis of Factors Affecting Student Graduation Using the K-Means clustering Method: Analysis of Factors Affecting Student Graduation Using the K-Means clustering Method. Jurnal Mantik, 3(4), pp.135-143.
-
Raharja, M.A. and Supriana, I.W., 2019. Analisis Klasifikasi Tinggkat Kesehatan Lembaga Perkreditan Desa (Lpd) Menggunakan Metode K-Means Clustering. Jurnal Teknologi Informasi dan Komputer, 5(1).
-
Fadhilah, A.M., Wahyuddin, M.I. and Hidayatullah, D., 2020. Analisis Faktor yang Mempengaruhi Perokok Beralih ke Produk Alternatif Tembakau (VAPE) menggunakan Metode K-Means Clustering. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 5(2), pp.219-225.
-
Joseph, S.I.T. and Thanakumar, I., 2019. Survey of data mining algorithm’s for intelligent computing system. Journal of trends in Computer Science and Smart technology (TCSST), 1(01), pp.14-24.
-
Indriyani, F. and Irfiani, E., 2019. Clustering Data Penjualan pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means. JUITA: Jurnal Informatika, 7(2), pp.109-113.
-
Dinata, R.K., Safwandi, S., Hasdyna, N. and Azizah, N., 2020. Analisis K-Means Clustering pada Data Sepeda Motor. INFORMAL: Informatics Journal, 5(1), pp.10-17.
-
Blasi, S., Brigato, L. and Sedita, S.R., 2020. Eco-friendliness and fashion perceptual attributes of fashion brands: An analysis of consumers’ perceptions based on twitter data mining. Journal of Cleaner Production, 244, p.118701.
-
Viloria, A. and Lezama, O.B.P., 2019. Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Computer Science, 151, pp.1201-1206.
-
Rezaee, M.J., Eshkevari, M., Saberi, M. and Hussain, O., 2021. GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game. Knowledge-Based Systems, 213, p.106672.
-
Hozumi, Y., Wang, R., Yin, C. and Wei, G.W., 2021. UMAP-assisted K-means clustering of large-scale SARS-CoV-2 mutation datasets. Computers in biology and medicine, 131, p.104264.
-
Ghadiri, M., Samadi, S. and Vempala, S., 2021, March. Socially fair k-means clustering. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 438-448).
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.