Winsorization for Outliers in Clustering Non-Cyclical Stocks with K-Means and K-Medoids Winsorization untuk Penanganan Pencilan dalam Penggerombolan Saham Sektor Consumer Non-Cyclical dengan K-Means dan K-Medoids
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Non-cyclical consumer sector stocks are often chosen by investors because the products in this sector are essential products that always in demand by society. Therefore, the demand for these products tends to be stable and defensive or less affected by economic shocks. However, it does not guarantee that every stock in this sector has good performance, thus it is necessary to group stocks based on their fundamental indicators in the form of financial ratios. This research aims to identify the best method by considering outliers and determining the clusters with the best fundamental performance as a recommendation for investors to make the right investment decisions. The data used in this study is secondary data with observations in the form of 50 non-cyclical consumer sector stocks. The variables used are Earning per Share, Return on Equity, Return on Assets, Debt to Equity Ratio, Price to Earnings Ratio, and Price to Book Value. The clustering results indicated that K-Medoids is the best clustering method, both on the data before and after handling extreme outliers with winsorization approach. However, the optimum number of clusters before and after winsorization are different, with 3 and 6 clusters. Considering the influence of extreme outliers and to get a more informative clustering result, the clustering result after the application of winsorization technique was chosen, which resulted in 6 clusters. Cluster 1, which consists of AALI, GGRM, INDF, and SGRO can be recommended because it has excellent fundamental performance, especially in terms of Earning per Share in 2022.
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Aufa Hasibuan, R., Wira Rizki, S., & Perdana INTISARI, H. (2021). Perbandingan Metode Fuzzy Clustering Means Dan Single Linkage Pada Pengelompokan Saham Lq45. Buletin Ilmiah Math. Stat. Dan Terapannya (Bimaster), 10(3): 361–368.
Fitriani, R. (2016). Pengaruh NPM, PBV, dan DER Terhadap Harga Saham pada Perusahaan Sub Sektor Makanan dan Minuman di Bursa Efek Indonesia.
Gentle, J. E., Kaufman, L., & Rousseuw, P. J. (1991). Finding Groups in Data: An Introduction to Cluster Analysis. Biometrics, 47(2): 788. https://doi.org/10.2307/2532178
Gentle, J. E., Kaufman, L., & Rousseuw, P. J. (2009). Finding Groups in Data: An Introduction to Cluster Analysis. Biometrics, 47(2): 788. https://doi.org/10.2307/2532178
Gorunescu, F. (2022). Data Mining Concepts,Models and Techniques. In Mining of Massive Datasets (Vol. 2). Berlin: Springer.
Hervita Nenobais, A., Sia Niha, S., & Manafe, H. A. (2022). Pengaruh Return on Asset (ROA), Return on Equity (ROE), Net Profit Margin (NPM) dan Earning Per Share (EPS) terhadap Harga Saham (Suatu Kajian Studi Literatur Manajemen Keuangan Perusahan). Jurnal Ekonomi Manajemen Sistem Informasi, 4(1): 10–22. https://doi.org/10.31933/JEMSI.V4I1.1146
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning. https://doi.org/10.1007/978-1-0716-1418-1
Khairunnisa, I. W. (2018). Perbandingan Algoritma Pengelompokan Non-Hierarki K-Medians Dan K-Medoids Pada Data Yang Mengandung Pencilan.
KSEI. (n.d.). Masuki Usia 26 Tahun, KSEI Siap Implementasikan 3 Rencana Strategis. 2023.
Oded Maimon, L. R. (2005). Data Mining and Knowledge Discovery Handbook. Data Mining and Knowledge Discovery Handbook. https://doi.org/10.1007/B107408/COVER
Prabowo, A. A. (2023). Perbandingan Metode K-Means, K-Medoids, dan Hierarki untuk Penggerombolan Kabupaten/Kota di Sumatera Utara Berdasarkan Jenis Kekerasan terhadap Perempuan.
Rand R. Wilcox. (2021). Introduction to Robust Estimation and Hypothesis Testing | ScienceDirect.
Ristiyanti. (2017). Cluster Ensemble dalam Penggerombolan Provinsi di Indonesia Berdasarkan Indikator Pelayanan Kesehatan Ibu Hamil.
Soepriyono, G., & Triayudi, A. (2023). Perbandingan Kinerja Algoritma Clustering Data Mining Untuk Prediksi Harga Saham Pada Reksadana dengan Davies Bouldin Index. Jurnal Media Informatika Budidarma, 7(4): 2061–2073. https://doi.org/10.30865/mib.v7i4.6623
Tukey, J. W. (1962). The Future of Data Analysis. The Annals of Mathematical Statistics, 33(1): 1–67. https://doi.org/10.1214/AOMS/1177704711’,
Wu, J. (2012). Advances in K-means Clustering. https://doi.org/10.1007/978-3-642-29807-3
Xu, R., & Wunsch, D. (2008). Clustering. Piscataway (NJ): Wiley-IEEE Press.
Yana, D., & Agustiningsih, W. (2022). Pengaruh Return on Equity (RoE), Debt to Equity Ratio (DER) dan Price Earnings Ratio (Per) Terhadap Harga Saham. AKSELERASI: Jurnal Ilmiah Nasional, 4(2): 67–77. https://doi.org/10.54783/JIN.V4I2.566