Automatic Classification; Crowdsourcing; Cultural Heritage; Cultural Heritages; Cultural Institutions; Data Acquisition; Data Collecting; Data Collection; Geographical Distribution; Geographical Locations; Intangible Cultural Heritages; Internet Media;

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070813304&doi=10.1109%2fIAC.2018.8780481&partnerID=40&md5=c39011e25f5f713625a1c0381ea90bd0

10.1109/IAC.2018.8780481

Indonesia is rich in cultural heritage. Based on BPS data in 2015, there were 6238 intangible and 1413 tangible cultural heritage [1]. Ironically with this great wealth, the 2014 Podes survey shows that Indonesian participation in cultural visits and cultural performances is very low (5-15%) [2]. So it is not surprising that about 127 of Indonesian intangible cultural heritage are on the verge of extinction, one of which is the result of the low level of cultural socialization among the community. Cultural socialization can actually be done through internet media considering that the public's exposure to devices and the internet is increasing year by year[3]. However, the cultural data available on the internet has not been integrated so that it cannot be optimally utilized. Data on cultural heritage is currently spread to many parties: cultural institutions such as museums; cultural community; and individuals. Data on cultural heritage in individuals are prone to be lost if it is not stored and/or inherited. The problem is, how to collect data that is spread everywhere and then store it in a structure that can answer questions such as the number of cultures that exist, statistics, cultural distribution regarding geographical location, and kinship relations between one culture and another. Facing this problem, crowdsourcing is a solution opportunity that allows data collection from many parties. Crowdsourcing is a new way of utilizing human resources spread throughout the world to do certain jobs. The low-quality problem of crowdsourcing technology can be handled by combining crowdsourcing systems with automatic classification algorithms or with procedure or algorithm to select the appropriate worker due to their performance. In this study, a crowdsourcing machine will be built to collect heritage cultural data from various sources. The collected data is stored in an ontology format that can answer questions: statistics on cultural data, number of current cultures, geographical distribution of cultural objects, and intercultural kinship.