cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
acengs@umtas.ac.id
Phone
+6285841953112
Journal Mail Official
ijqrm.rescollacomm@gmail.com
Editorial Address
Jalan Riung Ampuh No. 3, Riung Bandung, Kota Bandung 40295, Jawa Barat, Indonesia
Location
Kota bandung,
Jawa barat
INDONESIA
International Journal of Quantitative Research and Modeling
Published by Research Collaboration Community
ISSN : 27225046     EISSN : 2721477X     DOI : https://doi.org/10.46336/ijqrm
Core Subject : Science, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics
International Journal of Quantitative Research and Modeling (IJQRM) is published 4 times a year and is the flagship journal of the Research Collaboration Community (RCC). It is the aim of IJQRM to present papers which cover the theory, practice, history or methodology of Quatitative Research (QR) and Mathematical Moodeling (MM). However, since Quatitative Research (QR) and Mathematical Moodeling (MM) are primarily an applied science, it is a major objective of the journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of Quatitative Research (QR) and Mathematical Modeling (MM) to real problems are especially welcome. In real applications of Quatitative Research (QR) and Mathematical Moodeling (MM): forecasting, inventory, investment, location, logistics, maintenance, marketing, packing, purchasing, production, project management, reliability and scheduling. In a wide variety of environments: community Quatitative Research (QR) and Mathematical Moodeling (MM), education, energy, finance, government, health services, manufacturing industries, mining, sports, and transportation. In technical approaches: decision support systems, expert systems, heuristics, networks, mathematical programming, multicriteria decision methods, problems structuring methods, queues, and simulation Computational Intelligence Computing and Information Technologies Continuous and Discrete Optimization Decision Analysis and Decision Support Mathematics Education Engineering Management Environment, Energy and Natural Resources Financial Engineering Heuristics Industrial Engineering Information Management Information Technology Inventory Management Logistics and Supply Chain Management Maintenance Manufacturing Industries Marketing Engineering Markov Chains Mathematics Actuarial Sciences Big Data Analysis Operations Research Military and Homeland Security Networks Operations Management Planning and Scheduling Policy Modeling and Public Sector Production Management Queuing Theory Revenue & Risk Management Services Management Simulation Statistics Stochastic Models Strategic Management Systems Engineering Telecommunications Transportation Risk Management Modeling of Economics And so on
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 390 Documents
 35   36   37   38   39 
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.
The Effect of Interest Rates and Inflation on Stock Prices: Evidence from Conventional Banking Companies Listed on the Indonesia Stock Exchange for The Period 2022-2024 Yuli Yulianti; Aldy Agustian; Isyana Rahayu
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.v7i2.1332

Abstract

This study examines the effect of inflation and the Bank Indonesia policy interest rate on stock prices in conventional banking companies listed on the Indonesia Stock Exchange during 2022-2024. The research was motivated by the sensitivity of the banking sector to macroeconomic movements during the post-pandemic recovery period. A quantitative associative design was applied using secondary data from 42 conventional banks selected through puIDR osive sampling, producing 126 firm-year observations. Inflation was measured using the annual inflation rate, the interest rate was proxied by the BI 7-Day Reverse Repo Rate, and stock prices were measured using annual closing prices. The data were analyzed using descriptive statistics, classical assumption tests, multiple linear regression, t-tests, F-test, and coefficient of determination. The findings show that inflation has a positive and significant effect on banking stock prices (B = 0.140; t = 2.468; p = 0.015). The BI interest rate also has a positive and significant effect (B = 0.177; t = 2.175; p = 0.024). Simultaneously, inflation and interest rates significantly explain stock prices (F = 7.093; p = 0.002), with an Adjusted R Square of 0.347. These results indicate that, during the research period, moderate inflation and higher interest rates were inteIDR reted by investors as signals of banking profitability and macroeconomic stabilization rather than purely as risk factors.
Analysis of Stock Return Volatility of PT Asuransi Multi Artha Guna Tbk Using the GARCH-M Model Muh. Yahya; Kalfin Kalfin; Hisyam Ihsan; Atikafairuq Selviana; Andi Widya Pratiwi Anas
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.v7i2.1334

Abstract

This study aims to analyze the volatility of stock returns of PT Asuransi Multi Artha Guna Tbk using the Generalized Autoregressive Conditional Heteroskedasticity in Mean (GARCH-M) model during the 2019–2024 period. The data used in this study are secondary data in the form of daily closing stock prices of AMAG.JK obtained from Yahoo Finance, with a total of 1,466 observations. The analytical stages include the calculation of log returns, stationarity testing using the Augmented Dickey-Fuller (ADF) test, Ljung-Box autocorrelation test, ARCH-LM test, selection of the best GARCH model based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), estimation of the GARCH-M model, conditional volatility analysis, and volatility forecasting. The results indicate that the stock return data of AMAG.JK are stationary and contain ARCH effects, making them appropriate for analysis using the GARCH model. Based on the AIC and BIC criteria, the best model selected is GARCH(1,2). The estimation results of the GARCH(1,2)-M model show that the ARCH and GARCH parameters are statistically significant, indicating the presence of volatility clustering and volatility persistence phenomena in the stock returns of AMAG.JK. However, the risk premium parameter in the GARCH-M model is not statistically significant, implying that conditional volatility does not significantly affect expected stock returns. The volatility forecasting results show that the volatility level of AMAG.JK stock tends to increase gradually in future periods. Overall, the GARCH(1,2)-M model is capable of describing the dynamics of volatility in AMAG.JK stock returns during the research period effectively.
Formulation and antibacterial activity testing of essential oil-based hand sanitizer spray citronella grass leaves (cymbopogon nardus) Zakiyya Y.N. Azizah; Indri Wulandari; Ruly Budiono
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.v7i2.1336

Abstract

Hand hygiene is an essential aspect of preventing healthcare-associated infections (HAIs). However, repeated use of alcohol-based hand sanitizers can cause skin dryness, irritation, dermatitis, and even allergic reactions, driving the demand for safer natural active ingredients. This study aims to formulate and evaluate the antibacterial activity of a spray hand sanitizer containing citronella (Cymbopogon nardus) essential oil. This research employed a quantitative–qualitative approach using a laboratory experimental design. The results showed that all HS-CO formulations were homogeneous, exhibited a color transition from clear to milky white as the concentration increased, possessed a characteristic leafy odour, exhibited good spreadability, and maintained pH values within the physiological range of human skin. The antibacterial activity test of citronella oil demonstrated zones of inhibition ranging from 8.93±2.09 mm to 17.02±4.13 mm against Staphylococcus aureus and from 9.28±1.44 mm to 18.19±4.06 mm against Escherichia coli. All formulations (P1, P2, P3, and P4) exhibited exceptional antibacterial efficacy, achieving a log reduction value of 8.18 within only 30 seconds of contact time. This value was equivalent to 100% bacterial reduction, indicating better antibacterial effectiveness than 70% alcohol.
The Effect Of Coconut Water (Cocos nucifera) Concentration In Ramie Wood Chip (Boehmeria nivea) Substrate On The Growth Of Brown Oyster Mushroom (Pleurotus cystidiosus) Qurrotu Ainun Nisa; Ruly Budiono; Suryana; Indri Wulandari; M. Agung Triyudha Agustiana
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.v7i2.1337

Abstract

The cultivation of brown oyster mushrooms (Pleurotus cystidiosus) often faces constraints due to the limited availability of suitable growth media. This study aims to evaluate the potential of ramie wood chips as an alternative substrate and coconut water as a nutritional supplement to enhance mushroom growth. Coconut water is rich in plant growth regulators (PGRs) such as cytokinins, while ramie chips contain high cellulose and lignin content, making them a promising combination for a cultivation medium. The research was conducted using a Completely Randomized Design (CRD) consisting of four coconut water concentrations: 0% (K0), 25% (K1), 50% (K2), and 100% (K3), with six replications. The observed parameters included mycelial growth rate, time of first harvest (Days After Inoculation/DAI), harvest frequency, number of fruiting bodies, pileus diameter, and substrate weight loss. The data obtained were analyzed using Analysis of Variance (ANOVA). Significant results were further evaluated using Duncan’s Multiple Range Test (DMRT) at a 5% significance level to determine the differences between treatments.
Dynamic Conditional Correlation with EGARCH Volatility for Conditional Value-at-Risk Portfolio Optimization: Evidence from IDX Blue-Chip Stocks Moch Panji Agung Saputra; Jumadil Saputra
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.v7i2.1338

Abstract

This study proposes a novel portfolio optimization framework that integrates the Dynamic Conditional Correlation (DCC) model with Exponential GARCH (EGARCH) volatility estimation to compute time-varying Conditional Value-at-Risk (CVaR) as a downside risk measure. Unlike classical Mean-Variance (MV) optimization which assumes constant correlations and normally distributed returns, the proposed DCC-EGARCH-CVaR model captures asymmetric volatility responses to market shocks and dynamic cross-asset co-movements, yielding a more realistic representation of financial risk in emerging markets. The study employs daily closing price data from five blue-chip stocks listed on the Indonesia Stock Exchange (IDX) — BBCA, BBRI, TLKM, ASII, and UNVR spanning January 2020 to December 2024. Results demonstrate that the EGARCH(1,1) model with Student-t innovations outperforms GARCH(1,1) and GJR-GARCH based on AIC/BIC criteria, confirming the presence of leverage effects in all return series. The DCC-EGARCH-CVaR optimized portfolio achieves a 9.0% higher Sharpe ratio compared to the classical minimum-variance portfolio, while simultaneously reducing the 95% CVaR by 6.6%. Portfolio weights derived from the DCC-EGARCH-CVaR framework are more diversified and responsive to regime shifts in market conditions compared to static MV optimization. These findings provide practical implications for risk-aware asset allocation in Indonesian capital markets.
Negative Binomial Regression with Climatic and Sociodemographic Covariates for Modeling Overdispersed Dengue Hemorrhagic Fever Counts: Evidence from Bandung City, Indonesia Rifki Saefullah; Natasya Tamarysma Putri; Mugi Lestari
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.v7i2.1339

Abstract

Dengue Hemorrhagic Fever (DHF) remains one of the most prevalent vector-borne diseases in Indonesia, with Bandung City consistently reporting high annual incidence. Count regression models have been widely applied in disease epidemiology; however, many studies default to Poisson regression without testing for overdispersion, which violates a fundamental modeling assumption when variance exceeds the mean. This study proposes a Negative Binomial Regression (NBR) framework that jointly incorporates climatic variables (monthly rainfall, mean temperature, relative humidity) and sociodemographic covariates (population density, drainage quality index, vegetation cover) to model weekly DHF case counts across 30 sub-districts of Bandung City from 2019 to 2023. Overdispersion was formally assessed using the Cameron-Trivedi test. Incidence Rate Ratios (IRRs) and 95% confidence intervals were estimated for all predictors. Model selection was performed via AIC, BIC, and likelihood ratio tests against a Poisson baseline. Results demonstrate significant overdispersion (dispersion parameter ), confirming the appropriateness of NBR over Poisson regression. Monthly rainfall (IRR = 1.008, p < 0.001), lagged one-week cases ), and population density emerged as significant positive predictors, while drainage quality index was protective. The NBR model achieved substantially lower AIC (2810 vs 3240) and BIC (2820 vs 3245) compared to Poisson. These findings provide quantitative evidence for spatiotemporal DHF surveillance and can guide targeted vector-control resource allocation in urban West Java
Efficient Frontier Analysis of Islamic and Conventional Bank Stock Portfolios: Evidence from Four IDX-Listed Issuers Using the Markowitz Model Dede Irman; Alim Jaizul Wahid
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.v7i2.1340

Abstract

This study investigates and compares the portfolio performance of Islamic and conventional bank stocks listed on the Indonesia Stock Exchange (IDX) using the Markowitz Mean-Variance optimization model. Four issuers were selected: PT Bank Rakyat Indonesia Tbk (BBRI), PT Bank Mandiri Tbk (BMRI), PT Bank Rakyat Indonesia Syariah Tbk (BRIS), and PT Bank Pembangunan Daerah Banten Tbk (BPAA), representing two conventional and two Islamic banking stocks respectively. Daily closing price data spanning from January 2021 to December 2023 (756 trading days) were employed to compute expected returns, variance, covariance, and correlation coefficients. Two optimal portfolios were constructed for each category: the Minimum-Variance Portfolio (MVP) and the Maximum-Sharpe Portfolio (MSP). Performance evaluation was carried out through multiple metrics including the Sharpe Ratio, Treynor Ratio, Jensen's Alpha, and Sortino Ratio. Results indicate that Islamic bank portfolios consistently outperform conventional bank portfolios on a risk-adjusted basis. The Maximum-Sharpe Islamic portfolio achieved a Sharpe Ratio of 1.765 compared to 1.342 for its conventional counterpart. These findings suggest that Islamic banking stocks, with their inherently lower leverage and prohibition on speculative instruments, exhibit more favourable risk-return characteristics, providing actionable insights for investors seeking Shariah-compliant investment alternatives.
Applying Machine Learning Algorithms to Predict Employee Turnover Intention: A Comparative Model Analysis Siti Hadiaty Yuningsih; Fahmi Sidiq; Yasir Salih
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.v7i2.1344

Abstract

Employee turnover represents a major challenge for organizations because it increases recruitment and training costs, disrupts operational continuity, and reduces organizational performance. Although machine learning has been widely applied to employee attrition prediction, most studies focus on comparing algorithms using the complete feature set, with limited attention to the predictive contribution of different employee information domains. This study aims to identify the most informative attribute domains for turnover prediction, compare the performance of ANN, RF, and SVM, and evaluate whether reduced-domain models can achieve performance comparable to full-feature models. The study utilized the IBM HR Analytics Employee Attrition dataset containing 1,470 employee records. Thirty predictive attributes were organized into six conceptual domains: Personal Information, Job Characteristics, Compensation, Work Environment, Career Development, and Relationship & Supervision. Twelve domain-based model configurations were developed and evaluated using ANN, RF, and SVM. Model development employed SMOTE to address class imbalance and repeated 10-fold cross-validation, while final evaluation was conducted on an independent holdout validation dataset. The results show that multi-domain models consistently outperform single-domain configurations. Compensation and Career Development emerged as the strongest standalone domains, while Work Environment was present in all top-performing models. The highest validation accuracy was achieved by M0-SVM (84.01%), whereas M11-SVM achieved comparable performance (82.65%) using only 16 attributes. M11-ANN produced the highest ROC AUC (0.782), indicating superior discriminative capability. Feature importance analysis identified OverTime, MonthlyIncome, Age, TotalWorkingYears, and YearsAtCompany as the most influential predictors. These findings demonstrate that domain composition is as important as algorithm selection in employee turnover prediction and highlight the importance of work environment, compensation, and career development factors in supporting data-driven employee retention strategies.
The Role of Industrial Operators and IIoT in AI/ML-Based Process Optimization: A Bibliometric Analysis and Research Gap Identification in the Industry 4.0 Era Renda Sandi Saputra; Rifki Saefullah
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.v7i2.1345

Abstract

The rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies has transformed manufacturing systems under the Industry 4.0 paradigm, enabling data-driven process optimization, predictive decision-making, and intelligent production management. Despite substantial growth in this research domain, previous bibliometric studies reported limited visibility of the Industrial Internet of Things (IIoT) and industrial operators within the AI/ML-based process optimization literature. This study aims to examine the evolution of these research themes and assess how the knowledge structure of the field has developed during the transition from Industry 4.0 to Industry 5.0. A bibliometric analysis was conducted using 362 publications retrieved from Dimensions.ai covering the period 2020–2026. Bibliometric performance indicators were analyzed using Bibliometrix (R), while science mapping and keyword co-occurrence analyses were performed using VOSviewer 1.6.20. The results reveal a continuous increase in publication output and the emergence of six major thematic clusters. AI and Smart Factory technologies remain the dominant research themes, followed by Smart Manufacturing and Cyber-Physical Systems. The analysis further shows that IIoT has evolved into a distinguishable thematic component connected to industrial connectivity, edge computing, and sensor infrastructures. In addition, a new human-centered cluster has emerged, characterized by concepts such as Operator 4.0, human-in-the-loop systems, collaborative robotics, and human-centered AI. Although both IIoT and operator-related themes have gained visibility, their thematic prominence remains lower than that of the dominant AI and smart manufacturing clusters. The findings indicate a gradual shift toward a more integrated manufacturing paradigm that combines intelligent algorithms, industrial connectivity, and human expertise, reflecting the broader transition from Industry 4.0 to Industry 5.0.

Page 39 of 39 | Total Record : 390

 35   36   37   38   39 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol. 7 No. 2 (2026): International Journal of Quantitative Research and Modeling (IJQRM) Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling Vol. 6 No. 4 (2025): International Journal of Quantitative Research and Modeling Vol. 6 No. 3 (2025): International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025) Vol. 6 No. 2 (2025): International Journal of Quantitative Research and Modeling Vol 6, No 1 (2025) Vol. 6 No. 1 (2025): International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024): International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024) Vol 5, No 3 (2024) Vol. 5 No. 3 (2024): International Journal of Quantitative Research and Modeling Vol. 5 No. 2 (2024): International Journal of Quantitative Research and Modeling Vol. 5 No. 1 (2024): International Journal of Quantitative Research and Modeling Vol. 4 No. 4 (2023): International Journal of Quantitative Research and Modeling Vol 4, No 3 (2023) Vol. 4 No. 3 (2023): International Journal of Quantitative Research and Modeling Vol. 4 No. 2 (2023): International Journal of Quantitative Research and Modeling Vol 4, No 2 (2023) Vol. 4 No. 1 (2023): International Journal of Quantitative Research and Modeling Vol 4, No 1 (2023) Vol. 3 No. 4 (2022): International Journal of Quantitative Research and Modeling Vol 3, No 4 (2022) Vol 3, No 3 (2022) Vol. 3 No. 3 (2022): International Journal of Quantitative Research and Modeling Vol 3, No 2 (2022) Vol. 3 No. 2 (2022): International Journal of Quantitative Research and Modeling Vol. 3 No. 1 (2022): International Journal of Quantitative Research and Modeling Vol 3, No 1 (2022) Vol 2, No 4 (2021) Vol. 2 No. 4 (2021): International Journal of Quantitative Research and Modeling Vol. 2 No. 3 (2021): International Journal of Quantitative Research and Modeling Vol 2, No 3 (2021) Vol. 2 No. 2 (2021): International Journal of Quantitative Research and Modeling Vol 2, No 2 (2021) Vol. 2 No. 1 (2021): International Journal of Quantitative Research and Modeling Vol 2, No 1 (2021) Vol. 1 No. 4 (2020): International Journal of Quantitative Research and Modeling Vol 1, No 4 (2020) Vol 1, No 3 (2020) Vol. 1 No. 3 (2020): International Journal of Quantitative Research and Modeling Vol. 1 No. 2 (2020): International Journal of Quantitative Research and Modeling Vol 1, No 2 (2020) Vol 1, No 1 (2020) Vol. 1 No. 1 (2020): International Journal of Quantitative Research and Modeling More Issue