OPTIMASI CLUSTER K-MEANS MENGGUNAKAN METODE ELBOW PADA DATA PENGGUNA NARKOBA DENGAN PEMROGRAMAN PYTHON

Abstract

As we know, drugs are very illegal in Indonesia because drug abuse can have very dangerous effects. Drugs are substances that have side effects such as hallucinations, reduced awareness and arousal of the user. Technological advances continue to change over time so that information needs are very much needed in life. Currently, data on drug users is very extensive, so that adequate information presentation techniques are needed so that the information received is very accurate and in accordance with the user's needs. Therefore, it is necessary to carry out a data mining process on drug user data to obtain useful information for users. This study aims to prove Elbow's performance to produce optimal clusters of drug user data using the K-Means algorithm as a data grouping method. Cluster optimization is obtained from the Elbow method, which is executed with Google Collaboratory using the Python programming language. The test results show that the Elbow method works very well in producing the optimal cluster, which is found at k = 3 with the SSE difference value of 1257.862 with k test = 5.