Penerapan Data Mining Untuk Clustering Kualitas Udara

 Ahmad Rifqi (Universitas Nasional, Jakarta, Indonesia)
 (*)Rima Tamara Aldisa Mail (Universitas Nasional, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: December 13, 2023; Published: December 26, 2023

Abstract

Human health at this time is the key to the continuity of life. Human health is very necessary in the process of development of human life. Environmental health is related to the circumstances or conditions that exist in the surrounding area where you live, whether in a small environment or a large environment. Air quality is the condition of the surrounding air. Air quality is very important for human life because air is what helps humans to live by breathing. With the availability of good air quality, it will certainly be an important factor for an area, not only for health but also for other sectors that interact directly in open areas. The important role of air quality for humans means that more attention needs to be paid and special treatment is given to areas exposed to bad air. The above is a very important problem that must be resolved immediately, if the problem is not resolved immediately it will have an impact on health. The process of solving problems requires a way to resolve them. Where the process of measuring air quality can be seen based on certain conditions or criteria that occur in an area. Data mining is a method used to carry out the problem solving process by processing data. In the process carried out in data mining, there are various ways of solving it. One thing that can be used is clustering. In clustering itself there are various kinds of algorithms such as DBSCAN, K-Means and K-Medoids. In this research, the solution process will use the three algorithms K-Means, K-Medoids and DBSCAN. The purpose of using these three algorithms is to compare the results obtained. In the process carried out in completing data mining, clustering techniques are used using 3 (three) algorithms, namely K-Means, K-Medoids and DBSCAN. The results obtained were that the K-Means algorithm had the highest accuracy value obtained at K=4 with a value of 0.843, for the K-Medoids algorithm the highest value was obtained at K=5 with a value of 0.896 and for the DBSCAN algorithm the highest value was obtained at K=2 with a value of 0.885.

Keywords


Data Mining; Clustering; K-Means; K-Medoids; DBSCAN

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