Penerapan Algoritma K-Means Clustering Terhadap Angka Partisipasi Murni pada Jenjang SMP di Kabupaten Malang

Patrycia, Holly and Bhalqis, Anissa Andiar (2024) Penerapan Algoritma K-Means Clustering Terhadap Angka Partisipasi Murni pada Jenjang SMP di Kabupaten Malang. Working Paper. Faculty of Computer Science: Departemen of Data Science. (Unpublished)

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Abstract

Education is the main key in developing the quality of human resources, but Indonesia faces significant challenges related to the education system, as revealed by the results of the 2018 PISA survey which ranked Indonesia 74th out of 79 countries. With the quality of education considered to be very poor compared to other countries, improving the quality of education is the main focus for achieving superior human resources. The government recognizes the importance of the Pure Enrollment Rate (APM) as an indicator of the success of educational programs. Pure Participation Rate (APM) is a comparison between school age students at an educational level and the population of the appropriate age, expressed as a percentage. The NER at the Malang Regency Middle School level, although it has increased from 2016-2018, is stable at around 80%, below the government's target in 2019 of 82%. The government faces serious challenges in increasing the APM in the region. This research focuses on grouping pure participation figures at the junior high school level in Malang Regency using the K-Means Clustering algorithm. The method used involves data analysis of the total enrollment rates at junior high school level in 2021 and 2022 in the region. The application of the K-Means Clustering algorithm is one of the approaches in data mining, with the aim of helping to group based on the high and low pure enrollment rates at the junior high school level in 2021 and 2022 in Malang Regency. Data processing uses Python software with the initial steps of collecting data, editing, data visualization, creating elbow graphs, and accuracy using silhouette scores. Based on the cluster results, 2 groups were obtained from the method that was carried out, namely high and low. The sub-districts that have the highest pure participation rates at the junior high school level are Kepanjen and Bululawang sub-districts, which are 529.46% and 365.90% respectively. But it will decrease in 2022, namely 142.03% for Kepanjen and 150.56% for Bululawang. This decline could be caused by various factors such as policy changes, demographic changes, or adjustments to educational infrastructure. The analysis results from this project show the use of the K-Means Clustering algorithm for pure enrollment rate data at the junior high school level in Malang Regency with a silhouette score accuracy of 0.843 which shows a very good level of clustering quality for this data. This analysis can help the government identify and overcome challenges in improving the NER and the overall quality of education.

Item Type:

Monograph (Working Paper)

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