IDENTIFIKASI LOKASI KLUSTER DAN DINAMIKA INDUSTRI KECIL DAN MENENGAH (IKM) DI KALIMANTAN TENGAH, 2005-2009

This study aims to identify the location of clusters of Small and Medium Industries (SMI), the main industries that determines the classification based on SME clusters in 14 districts / cities in Central Kalimantan Province. The data used are secondary data from the BPS data of Central Kalimantan pr...

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Bibliographic Details
Main Authors: , Fahmy Fauzy, , Prof. Dr. Mudrajad Kuncoro, M.Soc.Sc.
Format: Thesis
Published: [Yogyakarta] : Universitas Gadjah Mada 2011
Subjects:
ETD
Description
Summary:This study aims to identify the location of clusters of Small and Medium Industries (SMI), the main industries that determines the classification based on SME clusters in 14 districts / cities in Central Kalimantan Province. The data used are secondary data from the BPS data of Central Kalimantan province and the raw data from the Office of Industry and Commerce of Central Kalimantan over the period of 2005-2009. The research used Geographic Information Systems (GIS), Pearson correlation test and Spearman-Rank, Static Location Quotient (SLQ), and Dynamic Location Quotient (DLQ) and discriminant analysis. The analysis showed the SMI clusters have been concentrating geographically in West Kotawaringin, Municipality of Palangka Raya, Pulang Pisau Regency, and Kapuas Regency. Leading subsectors employment based in ISIC 31 are North Barito Regency, Sukamara, Lamandau, Seruyan, Gunung Mas, East Kotawaringin and East Barito Regency. ISIC 33 has been clustered in the West Kotawaringin Regency, Katingan Regency, the City of Palangka Raya, Pulang Pisau Regency, Gunung Mas and East Barito Regency. ISIC 36 has been clustered in North Barito regency and East Barito. ISIC 38 excels in East Kotawaringin and North Barito regency. Based on discriminant analysis, four predictor variables in Central Kalimantan between 2005 and 2009 were wages, population, age of firm, and labor productivity. The finding indicates that population is the best predictor in distinguishing clusters and non-industrial SMI.