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All Journal Inferensi Journal of Applied Science, Engineering, Technology, and Education Mattawang: Jurnal Pengabdian Masyarakat Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology VARIANSI: Journal of Statistics and Its Application on Teaching and Research ARRUS Jurnal Pengabdian Kepada Masyarakat SMART: Jurnal Pengabdian Kepada Masyarakat
Rais, Zulkifli
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Workshop on Student Graduation Decisions Using Statistical Methods at Takalar State Senior High School 7 Annas, Suwardi; Ahmar, Ansari Saleh; Rais, Zulkifli; H.S, Rahmat; Tri Utomo, Agung
ARRUS Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.abdiku4458

Abstract

This community service program was conducted at SMA Negeri 7 Takalar to enhance teachers’ ability to utilize statistical methods specifically logistic regression to support data-driven graduation decisions. The training addressed challenges related to manual graduation assessment processes that often lack objective analytical support. Participants were introduced to the basic concepts of logistic regression, followed by hands-on practice using an interactive R Shiny dashboard to analyze student data and estimate graduation probabilities. The results indicate that teachers were able to understand and apply statistical analysis procedures, interpret logistic regression outputs, and recognize the importance of evidence-based decision-making. This activity not only improved teachers’ data literacy but also supported digital transformation efforts in education and strengthened collaboration between Universitas Negeri Makassar and SMA Negeri 7 Takalar. The program is expected to contribute to more accurate, transparent, and data-informed graduation assessments in the future.
Perbandingan Model Value-at-Risk (VaR) Hybrid GARCH-EVT dan Model Standar dalam Pengukuran Risiko Ekstrem pada Portofolio Saham Sektoral di Indonesia Annisa Syalsabila; Ikhwana, Nur; Utomo, Agung Tri; Rahmanda, Lalu Ramzy; Rais, Zulkifli
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm461

Abstract

This study aims to construct an optimal portfolio and compare the accuracy of various Value-at-Risk (VaR) models in measuring the risk of stock portfolios in the Indonesia Stock Exchange (IDX). The optimal portfolio is formed using the Minimum Variance Portfolio (MVP) method based on 11 sector-representative stocks for the period 2019–2025. The risk performance of this portfolio is then evaluated using six VaR models: Variance–Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), and the hybrid GARCH–EVT model. Model accuracy is assessed through backtesting using the Kupiec Proportion of Failures (POF) test and the Christoffersen Conditional Coverage (CC) test at the 95% and 99% confidence levels. The optimization results indicate that the MVP portfolio is dominated by defensive sectors such as consumer non-cyclicals (ICBP.JK) and large-cap banking (BBCA.JK). Backtesting results show that although all models perform adequately at the 95% level, standard models (VC, MC, GARCH) fail to capture extreme risk at the 99% level. In contrast, the GARCH–EVT model satisfies the backtesting criteria and emerges as the most accurate and superior model for predicting extreme losses.Penelitian ini bertujuan untuk membangun portofolio optimal dan membandingkan akurasi berbagai model Value-at-Risk (VaR) dalam mengukur risiko portofolio saham di Bursa Efek Indonesia (BEI). Portofolio optimal dibentuk menggunakan metode Minimum Variance Portfolio (MVP) dari 11 saham perwakilan sektor periode 2019-2025. Kinerja risiko portofolio ini kemudian diukur menggunakan enam model VaR: Variance-Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), dan model hybrid GARCH-EVT. Akurasi model diuji menggunakan backtesting Uji Kupiec (POF) dan Uji Christoffersen (CC) pada tingkat kepercayaan 95% dan 99%. Hasil optimisasi menunjukkan portofolio MVP didominasi oleh sektor defensif seperti consumer non-cyclicals (ICBP.JK) dan perbankan big-cap (BBCA.JK). Hasil backtesting menunjukkan bahwa meskipun semua model akurat pada tingkat 95%, model standar (VC, MC, GARCH) gagal mengukur risiko ekstrem pada tingkat 99%. Sebaliknya, model GARCH-EVT terbukti memenuhi uji dan menjadi model yang paling akurat dan superior untuk memprediksi kerugian ekstrem.
Co-Authors Abidin, Muh. Zulkifli Ahmar, Ansari Saleh Aidid, Muhammad Kasim Akbar Iskandar Alfairus, Muh. Qodri Amira, Husnul Annisa Syalsabila Asti Dewi Putri Aswi, Aswi Azrahwati Botto-Tobar, Miguel Fahmuddin S, Muhammad H.S, Rahmat Hardianti Hafid Haris, A. Sulfiana Hasnita, Hasnita Ikhwana, Nur Indrayasaro Mar'ah, Zakiyah Muhammad Arif Tiro, Muhammad Arif Muhammad Kasim Aidid Nusrang, Muhammad Poerwanto, Bobby Purnamasari, A. Bida Rahmanda, Lalu Ramzy Rahmat, Rahmat Wahyudi Refaldy, Muhammad Rezky, Akhmad Rezky Ramadhana T Risqi, Shopia Ruliana Ruliana Ruliana, Ruliana Rusdianto Saida, Saida Sitti Masyitah Meliyana R. Sudarmin Sudarmin Sudarmin Suwardi Annas Syahputra, Akbar Taufiq Hidayat Tri Utomo, Agung Triutomo, Agung Utomo, Agung Tri Yuniar, Eka Citra Yusniar Rasjid, Yusniar