Optimization of Indonesian Telematics Smes cluster: Industry 4.0 challenge

• Autores: E.T Tosida, I Wahyudin, F Andria, A.D Sanurbi, A Wartini
• Localización: Utopía y praxis latinoamericana: revista internacional de filosofía iberoamericana y teoría social, ISSN-e 1316-5216, Nº. Extra 2, 2020, págs. 160-170
• Idioma: inglés
• Títulos paralelos:
  • Optimización del grupo de empresas telemáticas indonesas: Desafío de la industria 4.0
• Enlaces
  • Texto completo (pdf)
• Resumen
  • English

    The purpose of this research is to explain the optimization process of the formation of the Indonesian telematics SMEs cluster. The cluster optimization process is carried out by partitioning around Medoids (PAM) and Fuzzy C-Means (FCM). The data used is the data of SMEs telematics services in Indonesia according to the National Economic Census (SUSENAS) data in 2006 Indonesian Central Bureau of Statistics (CBS). The 2006 data usage is caused by the SUSENAS 2016 not yet released by Indonesian CBS. The cluster of Indonesian telematics SMEs was validated using the Silhouette Coefficient which resulted in a value of upper than 0.99.

  • English

    El objetivo de esta investigación es explicar el proceso de optimización de la formación del grupo de pequeñas y medianas empresas (PYME) telemáticas de Indonesia. El proceso de optimización del clúster se lleva a cabo mediante particionamiento en torno a Medoids (PAM) y Fuzzy C-Means (FCM). Los datos utilizados son los datos de los servicios telemáticos de las PYME en Indonesia según los datos del Censo Económico Nacional (SUSENAS) de la Oficina Central de Estadísticas de Indonesia (CBS) de 2006. El grupo de PYME telemáticas indonesas se validó utilizando el Coeficiente de Silhoutte, que resultó en un valor superior a 0,99.

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