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Journal of Innovative and Creativity (Joecy)
Article . 2025 . Peer-reviewed
License: CC BY SA
Data sources: Crossref
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Segmentasi Konsumen Zero Waste Menggunakan Metode Gaussian Mixture Model dan Fuzzy C-Means Berdasarkan Preferensi dan Perilaku Pembelian

Authors: Ni Luh Putu Ika Candrawengi; I Gusti Ngurah Putu Dharmayasa; I Gede Fery Surya Tapa; Anak Agung Sagung Istri Ratu;

Segmentasi Konsumen Zero Waste Menggunakan Metode Gaussian Mixture Model dan Fuzzy C-Means Berdasarkan Preferensi dan Perilaku Pembelian

Abstract

The increasing concern for the environmental problems has led to the growth of zero-waste stores as a form of sustainable consumption. This study aims to identify consumer segmentation in zero waste stores in North Kuta, Bali, based on preferences and purchasing behavior. Data was gathered through reliable and valid questionnaires involving 80 respondents who had shopped at three zero waste stores. The analysis was conducted using GMM (Gaussian Mixture Model) and FCM (Fuzzy C-Means) algorithms, with evaluation using silhouette coefficient and ICD Rate. The results showed that GMM was more optimal in forming homogeneous clusters (ICD Rate: 0.715). Three main clusters were identified: Eco-Engaged Advocates (respondents who are loyal and environmentally conscious), Value-Conscious Supporters (respondents who focus on product value), and Occasional Shoppers (pragmatic respondents who are responsive to promotions). The findings provide strategic implications for businesses in developing a more adaptive and data-driven marketing approach.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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