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All Journal Media Statistika Jurnal Ilmu Dasar Statistika Jurnal Matematika & Sains Jurnal Fourier BAREKENG: Jurnal Ilmu Matematika dan Terapan IJID (International Journal on Informatics for Development) STATISTIKA
Epha Diana Supandi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

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COMPARISON OF ROBUST ESTIMATION ON MULTIPLE REGRESSION MODEL Jana, Padrul; Rosadi, Dedi; Supandi, Epha Diana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0979-0988

Abstract

This study aimed to compare the robustness of the OLS method with a robust regression model on data that had outliers. The methods used on the robust regression model were M-estimation, MM-estimation, and S-estimation. The step taken was to check the characteristics of the data against outliers. Furthermore, the data were modeled with and without outliers using the OLS method and the M-, MM-, and S-estimations. The results were very different between the data with and without the outlier models in the OLS method. It was reflected in the intercept and standard error variables generated from the models. Meanwhile, the regression model with the M-, MM-, and S-estimations was quite stable and able to withstand the presence of outliers. Based on the three estimations that were robust against the outliers, the MM-estimation was the best candidate because, in addition to having a stable intercept parameter estimation, it also had the smallest standard error, which was 61.9 in the resulting model.
CLUSTER ANALYSIS OF K-MEANS AND WARD METHOD IN FORMING A ROBUST PORTFOLIO: AN EMPIRICAL STUDY OF JAKARTA ISLAMIC INDEX Zain, Zuva Amalina; Mussafi, Noor Saif Muhammad; Supandi, Epha Diana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp537-546

Abstract

Building a portfolio is one method of reducing investment risk. Cluster analysis can shorten the time required to choose companies for a portfolio because it makes it easy to put firms in the same category together. To maintain the best state of the portfolio cluster analysis in the case of data containing outliers, K-means, and ward cluster analysis are employed in conjunction with a robust portfolio strategy. K-means clustering is a popular method for grouping data by assigning observations to clusters based on proximity to the cluster’s center meanwhile the Ward method is based on the size of the distance between clusters by minimizing the number of squares. This study seeks to determine the robust portfolio performance comparison outcomes produced by K-Means and Ward clustering utilizing the Sharpe ratio criterion. The Sharpe ratio is one of the most widely used methods to evaluate a portfolio’s risk-adjusted performance. The greater a portfolio's Sharpe ratio, the better its risk-adjusted performance. Stocks included in the Jakarta Islamic Index 70 (JII70) are used in this research. The results of the formation of a robust portfolio on K-Means clustering produce a return rate of 0.01038627 and risk of 0.1066364, while in the Ward cluster, the portfolio profit rate is obtained at 0.01632749 and the risk is 0.1340073. Based on the Sharpe ratio criteria, in this case, the robust portfolio with the Ward cluster is superior to the K-Means cluster because it produces a higher Sharpe value.
Co-Authors Abdurakhman Abdurakhman Akhmad Fauzy Anatansyah Ayomi Anandari Dedi Rosadi Jana, Padrul Luh Putu Ratna Sundari Mohd. Rizam Abu Bakar Muhammad Wakhid Musthofa Noor Saif Muhammad Mussafi Riska Yulianti Vanti, Eka Nur Widiawati, Alfina Viona Isabela Zain, Zuva Amalina