Mapping of the Reading Literacy Activity Index in East Java Province, Indonesia: an Unsupervised Learning Approach

Abstract

One of the educational problems that must be faced by East Java Province is the low reading culture of the community. The level of reading culture can be indicated by the Reading Literacy Activity Index (Alibaca Index). Alibaca Index of East Java is only 33.19 which value is included in the low category. So, this research uses the indicators that compose the Alibaca Index to classify regencies/cities in East Java Province. The analysis process carried out in this research uses one of the unsupervised learning algorithms, namely the K-Means algorithm. Analysis using the K-Means algorithm for grouping regencies/cities in East Java Province based on the indicators that compose the Alibaca index gives the results that the regencies/cities of East Java Province are divided into 3 clusters based on the optimal number of clusters according to the result of the elbow and silhouette method. Cluster 1 consists of 20 regencies and cities, cluster 2 consists of 10 regencies, and cluster 3 consists of 8 cities. Each cluster has different characteristics, cluster 1 is the cluster with the lowest skill dimension, while the cluster 2 area is an area that dominates the access dimension, alternative dimension, and cultural dimension, meanwhile, the third cluster does not have dominance in these 3 dimensions, which means that cluster 3 is the government's priority for improving reading activities, so the result of the analysis can help the government to develop strategic policies to achieve educational equity, especially concerning literacy levels based on the characteristics of each regency/city in East Java Province.

Unravelling Income Inequality in Indonesia

“…The model used in this study is as follows: This study employed the clustering approach, namely k-means (Sinaga & Yang, 2020), after performing linear regression analysis to classify provinces in Indonesia based on ICT characteristics as reflected by ICT indices. K-means is a machine learning clustering algorithm that seeks to divide data into many clusters based on similar qualities or attributes (Azies & Rositawati, 2021;Monica et al, 2021). The independent variables in this study are ICT indicators such as the ICT access and infrastructure index, the ICT use index, the proportion of the population who has used the internet, and others.…”