The study of river performance and infrastructure is not only conducted qualitatively. An instrument is needed to examine the object being observed quantitatively (usually with a minimum and maximum number scale interpretation). The review of the physical condition assessment for the river performance index and river infrastructure has not been developed based on a study of the variables that influence it. Therefore, this study aims to develop a mathematical model of the index of river performance and infrastructure as a decision support system for the integration of programs and activities related to river management. The research location was chosen based on the consideration that there has been no preparation of a performance index model in the Babon River. In this study, the authors use the Smart-PLS (Partial Least Square) application to analyze and narrow the variables and then re-analyze them using the Generalized Reduced Gradient (GRG) method to calculate non-linear equations. There are four variables, eight dimensions, and 51 (indicators) used, with the types of technical, spatial, social, and regulatory variables. Based on the PLS-SEM analysis, the results were narrowed into 4 (four) variables, 8 (eight) dimensions, and 51 (fifty one) indicators that were interrelated with one another. The GRG (Generalized Reduced Gradient) analysis with the solver in Microsoft Excel showed the most influential weights consisting of: technical variables, namely rivers (0.475) and flood problems (0.582); spatial variables, namely land use (0.418) and land cover (% Urban) (0.498); social variables, namely community activities (0.454), settlement density and socio-cultural conditions (0.289), and community participation (0.257); and regulation variables, namely law enforcement efforts (1.000). This research can be used for other watersheds with conditions or characteristics relatively similar to the Babon River. However, research related to this formulation on other watershed conditions still needs to be done.

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AbstrakPerkembangan pusat perbelanjaan yang semakin pesat di kota Kediri menimbulkan adanya persaingan usaha, sehingga dibutuhkan strategi pemasaran seperti segmentasi konsumen pada komponen demografis dan psikografis. Dalam statistika, komponen-komponen tersebut dapat dinyatakan sebagai variabel. Untuk meringkas data dengan banyak variabel, akan digunakan analisis cluster dengan metode k-means cluster. Data yang digunakan adalah data hasil penyebaran kuisioner kepada konsumen Kediri Town Square. Analisis cluster akan dilakukan pada komponen psikografis konsumen Kediri Town Square. Metode sampling yang digunakan adalah systematic random sampling dengan jumlah sampel sebanyak 103 responden. Analisis cluster menghasilkan empat segmen yaitu segmen yang dicirikan oleh konsumen yang lebih mempertimbangkan kualitas karena kualitas bagi mereka adalah nomer satu, segmen yang dicirikan oleh konsumen yang setia dengan merek, segmen yang tidak mempermasalahkan harga, dan segmen yang impulsive yaitu konsumen yang suka membeli barang yang tidak direncanakan sebelumnya. Kata kunci: Analisis Cluster, K-means, Komponen Psikografis Abstract

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