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All Journal Brilliance: Research of Artificial Intelligence International Journal of Engineering Continuity
Ulfah, Khaerani
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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Revealing Consumer Preferences in the Fashion Industry Using K-Means Clustering Sulianta, Feri; Ulfah, Khaerani; Amalia, Endang
International Journal of Engineering Continuity Vol. 3 No. 2 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i2.280

Abstract

The fashion industry, driven by rapidly shifting e-commerce trends and consumer preferences, demands precise data analysis to optimize marketing strategies and enhance customer satisfaction. This study utilizes data mining techniques, specifically K-Means Clustering and the Elbow Method, to reveal consumer preferences within a dataset of 1,000 fashion product sales records, which include attributes such as product ID, name, brand, category, price, rating, color, and size. By grouping data into distinct clusters based on price and rating preferences, the analysis uncovers four key consumer segments. The optimal number of clusters is confirmed using the WCSS (Within-Cluster Sum of Square) method. These insights offer valuable guidance for refining marketing strategies in the fashion industry. Future research should consider additional variables and employ advanced tools for deeper analysis.
Application of the Mean-Shift Method in Grouping the Influence of Labor Market Information on Labor Absorption in West Java Province Ulfah, Khaerani; Rumaisa, Fitrah
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5920

Abstract

The imbalance between the number of job seekers and the availability of jobs is a challenge in the labor market in West Java Province. This study aims to group districts/cities based on the influence of labor market information on labor absorption using the Mean-Shift algorithm. Data were obtained from BPS for the 2019–2023 period, covering the number of job seekers, vacancies, and job placements. Data were processed through cleaning, transformation, normalization, and aggregation of a five-year average. Clustering was carried out using the Mean-Shift algorithm with an optimal bandwidth of 0.474611, resulting in two clusters with a Silhouette Score of 0.4943. The first cluster consists of areas with low labor absorption rates, characterized by the number of job seekers that are not comparable to vacancies and job placements. The second cluster includes areas with higher and more balanced labor absorption. The results of the study show that the Mean-Shift algorithm is able to group regions based on labor market characteristics. These findings suggest that labor market information can be used to map regions based on labor absorption rates in a more targeted manner, as well as support the formulation of data-based employment policies.
Co-Authors Amalia, Endang Fitrah Rumaisa Sulianta, Feri