PENERAPAN PARTIAL LEAST SQUARE-MODIFIED FUZZY CLUSTERING UNTUK SEGMENTASI DALAM PEMODELAN DATA PENGIRIMAN PAKET JNE MEDAN

Aulia Mumtazah Pasya, Ismail Husein, R. Maisaroh Rezyekiyah Siregar

Abstract


JNE Express is one of the companies engaged in the field of shipping goods, the high level of shipping goods carried out and the many types of goods and types of services available at JNE Express, therefore knowledge is needed regarding the variables that affect the level of shipping goods and segmentation of the types of goods to be shipped. In this study, it is suspected that the type of service and special handling variables on the package affect the level of shipping goods carried out by JNE Express, Medan branch in the outbound unit. The approach used to determine the relationship between latent variables and model segmentation in shipping data is PLS-MFC (Partial Least Square-Modified Fuzzy Clustering). The results of the study showed that all variable indicators were significant where 4 of the 5 services type variable indicators were significant to the quantity of shipping goods, and 2 of the 2 special handling variable indicators were significant. The optimal segment results obtained based on the lowest FPI and NCE values were 2 segments or classes with an FPI value of 0.998 and an NCE value of 1.242. Where the exogenous latent variable that affects the high quantity of goods is the type of service with the REG indicator


Keywords


quantity of goods, PLS, PLA-MFC

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DOI: https://doi.org/10.30743/mes.v10i1.10202

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