Perbandingan Kinerja Algoritma Clustering Data Mining Untuk Prediksi Harga Saham Pada Reksadana dengan Davies Bouldin Index

 Gatot Soepriyono (Universitas Nasional, Jakarta, Indonesia)
 (*)Agung Triayudi Mail (Universitas Nasional, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: August 5, 2023; Published: October 31, 2023

Abstract

Mutual funds are a container that can be used to accommodate funds from the public which will later be distributed to the owners of the company. The ease of investing in share prices cannot be separated from the ease of obtaining information. The share price that is very popular with the public is the share price for banks, whether privately owned or government owned. However, even though banks are very close and popular with capital market players, this does not rule out the possibility of a decline in share prices. This problem is not a problem that can be considered trivial and ignored, if you continuously experience losses from the capital market it will certainly give rise to distrust or a lack of interest in the public to participate in investing in companies. Predictions for stock prices must be done well and correctly and get accurate results, therefore it is necessary to use a special technique or method to help carry out the prediction process until results are obtained with a good level of accuracy. The expected prediction process is in line with the concept of data mining. The process of applying clustering for predictions is also considered very suitable, this is because in stock prices there is no target class for each data. The K-Means algorithm and K-Medoids algorithm are part of cluster data mining to be used to make predictions based on cluster formation. The purpose of the comparison is to get more reliable results, where these results can be seen from better algorithm performance. The performance measurement process for the K-Means and K-Medoids algorithms will later be assessed based on the Davies Bouldin Index (DBI). The results of the research show that the performance results of the K-Means algorithm are better than the K-Medoids algorithm. This is proven by the DBI value obtained from the K-Means algorithm being no more than 0.6, while in the K-Medoids algorithm the DBI value obtained is up to 5.822. Overall, each stock data has an optimal cluster based on the clustering process with the K-Means algorithm. The optimal cluster results in BMRI stock data, the optimal cluster is at K=4 with a DBI value of 0.501. In the BBNI stock data, the optimal cluster is at K=4 with a DBI value of 0.500. In the BBCA stock data, the optimal cluster is at K=3 with a DBI value of 0.441. In the BNGA stock data, the optimal cluster is at K=2 with a DBI value of 0.263. In the BDMN stock data the optimal cluster is at K=2 with a DBI value of 0.028 and in the MEGA stock data the optimal cluster is at K=4 with a DBI value of 0.353.

Keywords


Performance Comparison; Clustering; Data Mining; Stock Price Prediction; Davies Bouldin Index

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