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All Journal Signifikan : Jurnal Ilmu Ekonomi Media Statistika Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series International Journal of Advances in Intelligent Informatics Kubik Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Aplikasi Statistika & Komputasi Statistik Inferensi Madaniya International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Proceeding ISETH (International Summit on Science, Technology, and Humanity) Bulletin of Applied Mathematics and Mathematics Education Aceh International Journal of Science and Technology Eduvest - Journal of Universal Studies
Dedy Dwi Prastyo
Department Of Statistics, Institut Teknologi Sepuluh Nopember
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NOWCASTING GROWTH AT RISK IN INDONESIA: APPLICATION OF MIDAS-QUANTILE REGRESSION MODEL Latifah, Turfah; Akbar, Muhammad Sjahid; Prastyo, Dedy Dwi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0673-0690

Abstract

One of the main problems faced by policymakers in economic monitoring is the limited availability of predictive tools that can comprehensively and in real time measure economic growth risks, particularly amid financial market volatility and rapid changes in economic indicators. This study aims to nowcast Indonesian economic growth using the Growth at Risk (GaR) approach by applying the Mixed Data Sampling-Quantile Regression (MIDAS-QR) model. This approach predicts economic risks across different quantiles, capturing best- and worst-case scenarios by integrating multi-frequency indicators, namely the Financial Conditions Index (FCI), External Financial Environment Index (EFEI), and Macroeconomic Prosperity Leading Index (MPLI), summarized using Principal Component Analysis (PCA). Prediction accuracy is evaluated using Quantile Mean Absolute Error (QMAE), Quantile Root Mean Squared Error (QRMSE), and Clark-West (CW) test metrics. The analysis utilizes a dataset of Indonesia covering the period from January 2001 to March 2025, combining quarterly GDP growth data as the dependent variable and monthly predictor variables sourced from the Central Statistics Agency (BPS), Bank Indonesia, and the Indonesia Stock Exchange. The findings show that the MIDAS-QR model significantly improves the accuracy of GaR forecasting in Indonesia relative to conventional approaches. It effectively captures risk asymmetries across quantiles, minimizes predictive errors, and facilitates the timely detection of economic downturns, offering valuable insights for early action. This study highlights the strategic role of high-frequency data in enhancing forecast precision and real-time economic risk monitoring in Indonesia. The application of the MIDAS-QR model presents a valuable tool for policymakers in formulating proactive responses to global economic uncertainty and fostering resilient economic growth.
Identification of Banking Stock Risk Factors through Stochastic Search Variable Selection in a CoVaR Models Based on Quantile Regression and Quantile Autoregressive Almas, Luqyana; Prastyo, Dedy Dwi; Rahayu, Santi Puteri
KUBIK Vol 10 No 2 (2025): IN PRESS
Publisher : Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung

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Abstract

The stability of the banking sector is crucial for maintaining economic balance, particularly in Indonesia where banks play a central role in the financial system. Conventional risk measures such as Value-at-Risk (VaR) mainly capture individual bank risk and are limited in assessing systemic risk arising from interbank spillovers. This study proposes an integrated systemic risk framework that combines Quantile Autoregressive (QAR) based VaR estimation with Conditional Value-at-Risk (CoVaR) derived from quantile regression, while incorporating Stochastic Search Variable Selection (SSVS) to identify key risk factors. The QAR approach accommodates asymmetry and heavy-tailed characteristics of bank return distributions, whereas CoVaR measures the conditional impact of bank distress on the overall financial system.  The SSVS is implemented within a Bayesian framework to select significant market and macroeconomic variables based on posterior inclusion probabilities. Model performance is evaluated using the Kupiec Proportion of Failures (POF) test. The results show that QAR-based VaR effectively captures tail risk at the 5% and 1% quantiles. CoVaR estimates reveal heterogeneity in systemic risk exposure, with medium-sized and digital banks exhibiting greater sensitivity to systemic stress than large banks. Overall, the CoVaR–SSVS model demonstrates superior validation performance and estimation stability compared to the conventional CoVaR approach.
The Development of a Financial Risk Meter for Indonesian Public Banks Using LASSO-QR and LASSO-QRNN Husna Afanyn Khoirunissa; Dedy Dwi Prastyo; Isnandar Slamet; Sugiyanto Sugiyanto; Bayutama Isnaini
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38631

Abstract

Banking companies will have a domino effect when one company fails that causes systemic risk in Indonesia. Moreover, Indonesia has a history of economic crises. This study presents a series of systemic risk measures for Indonesia, the Financial Risk Meter (FRM) with the LASSO-QR model, a novel application within the context of Indonesian data. Then, this study enhances the FRM methodology by incorporating the QRNN method to account for the nonlinear dependencies of return values across different companies, and applies the novel LASSO-QRNN method to measure FRM for Indonesia. This study employs a quantitative empirical approach using secondary financial and macroeconomic time-series data. This study developed LASSO-QR and LASSO-QRNN models applied to log-return data of public banks in Indonesia and macroeconomic variables to measure the FRM. These models captured financial risk characteristics by adjusting LASSO parameters with a moving window approach. The FRM indicated high-risk periods in mid-2020 and the first quarter of 2021 for the LASSO-QR, extending into the third quarter of 2021 for the LASSO-QRNN. This study contributes new insights into risk measures for individual banks and the banking system in Indonesia. Additionally, this offers solutions for measuring daily systemic risk that can account for both linear and nonlinear dependencies among companies.
THE EFFECT OF PRODUCT ATTRIBUTES AND CUSTOMER REVIEWS ON SALES PERFORMANCE ON THE TOKOPEDIA E-COMMERCE PLATFORM Aditya Prasetio; Dedy Dwi Prastyo
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 7 (2026): JUNE
Publisher : RADJA PUBLIKA

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Abstract

This study analyzes the influence of product attributes and customer reviews on sales performance on the Tokopedia e-commerce platform. Data were collected via web scraping from Tokopedia's electronics category (laptops and smartphones), yielding 463 products as the final sample. Product attributes (price, store status, brand clarity, and product description) and customer review indicators (review volume, average star rating, and sentiment score) were used as independent variables, while sales performance (sold count) served as the dependent variable. Sentiment analysis was conducted using a lexicon-based text mining approach with an Indonesian sentiment lexicon. Data analysis was performed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. Results indicate that product attributes have a positive and significant effect on sales performance (β = 0.141, t = 5.706, p < 0.05), and customer reviews have a highly significant effect (β = 0.823, t = 46.768, p < 0.05). Together, both variables explain 76.9% of the variance in sales performance (R² = 0.769). Customer reviews, particularly review volume, are the dominant determinant, while store status is the most influential product attribute indicator.
Co-Authors A. Tuti Rumiati Abdul Karim Abdul Karim Adatul Mukarohmah Aditya Prasetio Affandy*, Nur Azizah Ahsan, Muhammad Akhmad Faturohman Almas, Luqyana Bambang Widjanorko Otok Bambang Widjanorko Otok Bayutama Isnaini Budi Manfaat budi manfaat, budi Danny Hermawan Adiwibowo Data Iranata dwi purwanto Eksiandayani, Santi Era Ardhya Pramesti Erma Oktania Permatasari Faturohman, Akhmad Fithriasari, Kartika Forestryani, Veniola Fredi Suryadi Fredi Suryadi Hendri Prabowo Heri Kuswanto Hidayatul Khusna Husna Afanyn Khoirunissa Husna Miratin Nuroini Ismaini Zain Isnandar Slamet Latifah, Turfah Luh Putu Shintya Handayani Maharsi, Rengganis Woro Maulana, Mahendra Andiek Muhammad Hisyam Lee Muhammad Sjahid Akbar Nadjadji Anwar Novi Ajeng Salehah Prandipa, Rayza Pusaka, Semerdanta Rhifda Zukhrufi Rizki Nanda Savera Roosyidah, Nila Ayu Nur S Suhartono, S Salafiyah Isnawati Santi Puteri Rahayu Saputra, Wisnowan Hendy Setiawan Setiawan Setiawan Setiawan Shofi Andari Sugiyanto Sugiyanto Suhartono Suhartono Suhartono Suhartono Suhartono Suhartono Supriyatna, Putu Krishnanda Veniola Forestryani Vita Ratnasari Wardoyo, Wasis Wibawati Wibawati Widhianingsih, Tintrim Dwi Ary Yusop, Zulkifli