Analisis Pengaruh Faktor Penggunaan Baju Baru (Fast fashion) ke Pengguna Baju Bekas (Thrifting) Menggunakan Metode K-Means Clustering (Studi Kasus: Toko Thriftboys.id)

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Published: Jan 1, 2022
DOI: https://doi.org/10.35870/jtik.v6i1.394
Keywords:
Thrift Data Mining K-Means Fast fashion

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Raffi Dima Sampurno
Universitas Nasional
Agung Triayudi
Universitas Nasional
Ratih Titi Komala Sari
Universitas Nasional

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.

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How to Cite
Sampurno, R. D., Triayudi, A., & Komala Sari, R. T. (2022). Analisis Pengaruh Faktor Penggunaan Baju Baru (Fast fashion) ke Pengguna Baju Bekas (Thrifting) Menggunakan Metode K-Means Clustering (Studi Kasus: Toko Thriftboys.id). Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 6(1), 117–124. https://doi.org/10.35870/jtik.v6i1.394
Issue
Vol. 6 No. 1 (2022): JANUARY-MARCH 2022
Section
Computer & Communication Science
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Author Biographies

Raffi Dima Sampurno, Universitas Nasional

Program Studi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional

Agung Triayudi, Universitas Nasional

Program Studi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional

Ratih Titi Komala Sari, Universitas Nasional

Program Studi Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional

References

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).

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