ANALISIS CLUSTER METODE K-MEANS DAN WARD DALAM PEMBENTUKAN PORTOFOLIO ROBUST (STUDI KASUS: DATA SAHAM INDEKS JII 70 PERIODE 1 DESEMBER 2021 -1 DESEMBER 2023)

Zuva Amalina Zain, NIM.: 20106010034 (2024) ANALISIS CLUSTER METODE K-MEANS DAN WARD DALAM PEMBENTUKAN PORTOFOLIO ROBUST (STUDI KASUS: DATA SAHAM INDEKS JII 70 PERIODE 1 DESEMBER 2021 -1 DESEMBER 2023). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Investment is the activity of placing funds in the present with the hope of making a profit within a certain period of time. Apart from profits, to get maximum results, investors need to consider risk. One way to minimize risk is to diversify it in the form of a portfolio. Portfolio Mean Variance is a method of forming portfolio weights by utilizing historical data on individual shares. Cluster analysis can reduce the time needed to select the stocks used to form a portfolio because stocks from the same category can be simply and easily grouped into one cluster. Thus, K-means and ward cluster analysis is used with a robust portfolio approach so that portfolio cluster analysis remains optimal in situations where data contains outliers. The aim of this research is to find out the comparative results of the performance of robust portfolios formed based on K-Means and Ward groupings. This research data uses shares listed on the Jakarta Islamic Index 70 (JII70). In the initial stage, clustering techniques using the K-Means and Ward methods were used. The results of group analysis show that two clusters were formed, namely the K-Means and Ward clusters, where the K-Means cluster consists of FILM, MAPI, TAPG and HRUM shares, while the Ward cluster consists of BUKA, PTPP, FILM and MAPI shares. The results of the stock performance analysis comparison show that the Ward cluster is better because the Sharpe Ratio value in the Ward cluster (0.1208591) is greater than the K-Means cluster (0.0961657).

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