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All Journal Media Statistika Jurnal Ilmu Dasar Statistika Jurnal Matematika & Sains Telematika Jurnal Fourier Jurnal Sains Matematika dan Statistika PERFORMA : Media Ilmiah Teknik Industri Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri BAREKENG: Jurnal Ilmu Matematika dan Terapan IJID (International Journal on Informatics for Development) Jurnal Riset Statistika Journal of Industrial Engineering and Halal Industries JURTI Jurnal Teknik Industri STATISTIKA
Epha Diana Supandi
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Religion 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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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.
Pembentukan Portofolio Robust pada Saham Syariah Indonesia Diana Supandi, Epha
Jurnal Riset Statistika Volume 5, No. 2, Desember 2025, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v5i2.8716

Abstract

Abstract: This study aims to analyze the formation of an optimal Islamic stock portfolio using a robust mean–variance approach with three robust estimation methods: Minimum Covariance Determinant (MCD), Minimum Volume Ellipsoid (MVE), and Orthogonalized Gnanadesikan–Kettenring (OGK). The MCD estimator works by selecting the most homogeneous subset of data from all observations so that the determinant of the covariance matrix of that subset is minimized. The MVE estimator works by finding the ellipsoid with the smallest volume. Meanwhile, the OGK estimator offers high computational efficiency, stable estimation for high-dimensional data, and the ability to produce a positive-definite covariance matrix through an orthogonalization process. The data used consist of weekly returns of seven stocks listed in the Indonesia Sharia Stock Index (ISSI) for the period January 2020–December 2024. Based on the Sharpe Ratio analysis, the three portfolio models exhibit significant differences in return-to-risk efficiency. The OGK-based portfolio consistently achieves the highest Sharpe Ratio at all return levels compared to MCD and MVE. This confirms the superiority of OGK as a stable, efficient, and robust estimator in constructing Islamic stock portfolios. Abstrak: Penelitian ini bertujuan untuk menganalisis pembentukan portofolio saham syariah yang optimal menggunakan pendekatan robust mean–variance dengan tiga metode estimasi robust, yaitu Minimum Covariance Determinant (MCD), Minimum Volume Ellipsoid (MVE), dan Orthogonalized Gnanadesikan–Kettenring (OGK).  Estimasi MCD bekerja dengan memilih subset data yang paling homogen dari keseluruhan observasi, sehingga determinan matriks kovarians subset tersebut minimum. Estimasi MVE bekerja dengan mencari elipsoid dengan volume terkecil. Sedangkan estimasi OGK menawarkan efisiensi komputasi tinggi, estimasi yang stabil pada data berdimensi besar, serta kemampuan menghasilkan matriks kovarians positive-definit melalui proses orthogonalization. Data yang digunakan merupakan return mingguan tujuh saham dalam Indeks Saham Syariah Indonesia (ISSI) periode Januari 2020–Desember 2024.  Berdasarkan analisis Sharpe Ratio, ketiga model portofolio menunjukkan perbedaan signifikan dalam efisiensi return terhadap risiko. Portofolio berbasis OGK secara konsisten memiliki Sharpe Ratio tertinggi di semua level return dibandingkan MCD dan MVE. Hal ini menegaskan keunggulan OGK sebagai estimator yang stabil, efisien, dan robust dalam pembentukan portofolio saham syariah.
A Full-Profile Conjoint Analysis to Identify University Students’ Preferences Toward Food Delivery Services: A Case Study of the Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta Supandi, Epha Diana; Rochmiyani, Fitri
Journal of Industrial Engineering and Halal Industries Vol. 6 No. 2 (2025): Journal of Industrial Engineering and Halal Industries (JIEHIS)
Publisher : Industrial Engineering Department, Faculty of Science and Engineering, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiehis.5592

Abstract

The rapid development of digital technology has transformed consumer behavior, particularly in fulfilling food needs through online food delivery services. University students represent one of the most active user groups due to their high mobility and preference for practical lifestyles. This study aims to analyze students’ preferences toward food delivery service attributes and to identify the most influential factors in determining their choices. The research involved 100 respondents from the Faculty of Science and Technology, Universitas Islam Negeri Sunan Kalijaga Yogyakarta, using the traditional conjoint analysis method with a full-profile design approach. Six attributes were examined: type of service, price, payment method, courier service, food quality, and service quality. The results indicate that price has the highest level of importance (25.401%), followed by type of service (22.230%), service quality (16.231%), courier service (15.926%), payment method (10.960%), and food quality (9.252%). The most preferred combination of attributes includes GrabFood or GoFood services with promotional prices, uniformed couriers, diverse food options, and fast delivery. These findings suggest that promotional pricing strategies and service quality improvements are key factors for online food delivery providers to enhance customer satisfaction and attract student users.
Portfolio Risk Assessment Using VaR and CVaR: A Comparative Study of Variance–Covariance Method and Monte Carlo Simulation Epha Diana Supandi; Atika Oktavia
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3120

Abstract

This study examines portfolio risk in Indonesia’s energy sector by applying Value at Risk (VaR) and Conditional Value at Risk (CVaR) under the Variance–Covariance and Monte Carlo Simulation approaches. The analysis focuses on ten stocks from the oil and gas as well as coal subsectors listed on the Indonesia Stock Exchange (IDX), using monthly closing price data from January 2020 to December 2024. A Weighted Scoring Method (WSM) is first employed to select stocks with superior fundamentals and liquidity, based on market capitalization, return on equity, debt-to-equity ratio, net profit margin, trading volume, and dividend yield. An optimal portfolio is then constructed using the Maximum Sharpe Ratio (MSR) framework, resulting in a portfolio dominated by PTBA, MEDC, and MBAP. Portfolio risk is subsequently estimated using VaR and CVaR at the 95% and 99% confidence levels under both the Variance–Covariance and Monte Carlo approaches. The empirical results indicate that CVaR consistently produces higher risk estimates than VaR, highlighting its superior ability to capture tail risk. Furthermore, the Variance–Covariance method yields slightly more conservative CVaR estimates compared to Monte Carlo Simulation, which is attributed to the near-normal distribution of portfolio returns during the observation period. Model validity is confirmed through backtesting using the Kupiec test, which shows that the VaR estimates satisfy statistical adequacy criteria. Overall, the findings suggest that while the Variance–Covariance approach remains effective under normality assumptions, Monte Carlo Simulation offers greater flexibility in modeling extreme market conditions. This study contributes to the literature by providing empirical evidence on comparative risk estimation methods in Indonesia’s highly volatile energy sector.
Co-Authors Abdurakhman Abdurakhman Akhmad Fauzy Anatansyah Ayomi Anandari Atika Oktavia Cindy Caroline Dedi Rosadi Eka Nur Vanti Ira Setyaningsih Ira Setyawati Jana, Padrul Luh Putu Ratna Sundari Mohd. Rizam Abu Bakar Muhammad Wakhid Musthofa Noor Saif Muhammad Mussafi Pipit Pratiwi Rahayu PRAMONO, YUDA EKA Riska Yulianti Rochmiyani, Fitri Sri Puji Lestari Vanti, Eka Nur Veladita Apriyanti Wahyu Suryadi Suryadi Widiawati, Alfina Viona Isabela Yogi Anggara Zain, Zuva Amalina