Skripsi
PEMANFAATAN TEKNIK CLUSTERING DALAM PENGELOMPOKKAN INDEKS PEMBANGUNAN LITERASI MASYARAKAT DI INDONESIA MENGGUNAKAN ALGORITMA K-MEANS
Literacy is an individual's ability to read, write, and calculate. However, that concept is no longer suitable or relevant to the rapid development of the modern world. UNESCO defines Literacy as a means of identification, understanding, creation, communication, and awareness of what is happening around and in the world. The literacy rate in Indonesia still ranks 69th out of 81 countries in terms of reading ability, indicating that Indonesia is still lagging behind other countries. Several factors that influence the low literacy rate in Indonesia are the lack of reading interest, where only 0.01% of children in Indonesia enjoy reading. In addition, the uneven distribution of books and facilities in schools and communities remains a problem in Indonesia. This research aims to cluster IPLM data in Indonesia using the K-means algorithm to support literacy development in Indonesia. The IPLM data used is the IPLM data for the year 2023 taken from the official website of the Indonesian Central Statistics Agency https://www.bps.go.id/id . From the results of clustering the IPLM data using the K-means algorithm, 3 clusters were obtained. Cluster 0 consists of low IPLM data filled by 168 Indonesian regencies that need attention from local governments. Cluster 1 is filled by 98 regencies with high IPLM values. And Cluster 2 is filled by 222 regencies with moderate IPLM values that can still be improved. This cluster received a DBI score of 0.5908329776339191.
Inventory Code | Barcode | Call Number | Location | Status |
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2407007120 | T163429 | T1634292024 | Central Library (REFERENS) | Available but not for loan - Not for Loan |
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