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All Journal International Journal of Research in Community Services International Journal of Quantitative Research and Modeling
Deva Putra Setyawan
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Physics Social Sciences Other

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Mean-VaR Portfolio Diversification Based on K-Medoids Clustering Deva Putra Setyawan; Alim Jaizul Wahid; Riza Andrian Ibrahim
International Journal of Quantitative Research and Modeling Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i2.1330

Abstract

This study develops a diversified stock portfolio by integrating the Mean-Value at Risk (Mean-VaR) model with K-Medoids clustering. The approach groups stocks according to similar risk-return characteristics before the portfolio optimization stage. The data consist of daily closing prices of LQ45 index constituents from 3 February to 31 July 2025, obtained from the Indonesia Stock Exchange and Yahoo Finance. Of the 45 LQ45 stocks, 18 stocks satisfied the criteria of data completeness, liquidity, market capitalization stability, and sector representation. Clustering was performed using expected return and 95% Value at Risk (VaR) as input variables. The best clustering structure was obtained for two clusters, with a Silhouette Index of 0.6882. The first cluster represents aggressive stocks with relatively high expected returns and higher downside risk, including ANTM, BRPT, AMMN, and MDKA. The second cluster represents defensive stocks with lower risk and more stable returns, including INDF, ASII, ICBP, BBCA, and TLKM. The optimal Mean-VaR portfolio was constructed with minimum inter-cluster allocation constraints of 30% for the aggressive cluster and 70% for the defensive cluster. The resulting portfolio produced a daily expected return of 0.003272 and a 95% VaR of -0.029053. These results indicate that K-Medoids clustering can support portfolio diversification by identifying distinct risk-return groups and improving risk control in investment allocation.
Strengthening Artificial Intelligence and Data Science Literacy for Teachers and Communities: A Cross-Sectoral Community Service Model in Cipari Subdistrict, Cilacap, Central Java Deva Putra Setyawan; Muhammad Rizky Aulia
International Journal of Research in Community Services Vol. 7 No. 3 (2026): International Journal of Research in Community Service (IJRCS)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v7i3.1353

Abstract

The rapid diffusion of generative artificial intelligence (AI) and cloud-based data tools is reshaping how educators design instruction and how micro, small, and medium enterprises (MSMEs) manage their operations, yet communities outside major urban centers continue to face a compounding digital divide that limits their participation in this transformation. This community service program was designed to strengthen AI and data science literacy among non-formal tutors, MSME actors, and cooperative staff in Cipari Subdistrict, Cilacap Regency, Central Java, through a two-day intensive workshop conducted in partnership with Koperasi UMKM “Bina Mandiri” and the tutoring community “Cerdas Bersama.” Forty-five participants completed a three-session curriculum covering generative AI and prompt engineering, cloud-based data management, and interactive data visualization, delivered through hands-on workshops and small-group mentoring grounded in andragogical (adult-learning) principles. Learning gains were measured with a cognitive pre-test/post-test instrument analyzed using the normalized gain (N-Gain) statistic, while participant reactions were captured with a Likert-scale satisfaction questionnaire. The mean score rose from 42.50 on the pre-test to 84.75 on the post-test, yielding an N-Gain of 0.73, classified as a high-category gain; 88% of participants reported being “very satisfied,” and the hands-on prompt-engineering session received the strongest appreciation (92%). Unlike prior community service studies that address either AI literacy for educators or digital literacy for MSMEs in isolation, the novelty of this program lies in its integrated, cross-sectoral curriculum and mixed-method evaluation design that jointly serves formal-adjacent education actors and informal-economy actors within a single sub-district ecosystem. The findings suggest that a short, intensive, andragogically grounded intervention can produce substantial and satisfying literacy gains even where infrastructural constraints persist, and the paper proposes a replicable model together with recommendations for sustaining these gains through longitudinal mentoring.
Co-Authors Alim Jaizul Wahid Muhammad Rizky Aulia Riza Andrian Ibrahim