KUBIK: Jurnal Publikasi Ilmiah Matematika
KUBIK: Jurnal Publikasi Ilmiah Matematika is a scientific journal published by the Mathematics Department UIN Sunan Gunung Djati Bandung in collaboration with ADMAPETA (Association of Lecturers and Programs of Mathematics Studies and Mathematics Education Program PTKIN. This journal contains scientific papers from academics, researchers, and practitioners about research on pure and applied Mathematics. KUBIK: Jurnal Publikasi Ilmiah Matematika has been accredited as SINTA 3 by Ministry of Research, Technology and Higher Education, Republic of Indonesia as an academic journal (SK Dirjen Dikti number: 73/E/KPT/2023). KUBIK: Jurnal Publikasi Ilmiah Matematika is published twice a year in May and November. Each paper is original work in the fields of pure and applied Mathematics. Subjects that are suitable for publication include, but are not limited to, the following fields: Fuzzy Systems and their Applications Geometry Theories and their Applications Graph Theories and their Applications Real Analysis and its Applications Operations Research and its Applications Statistical Theories and their Applications Dynamical Systems and their Applications Mathematical Modeling and its Applications Discrete Mathematics and its Applications Computer Science and its Applications Actuarial Mathematics and its Applications Thus, we invite academics, researchers, and practitioners to participate in submitting their work to this journal. ISSN: 2686-0341 (Online) 2338-0896 (Printed)
Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung
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Model Indeks Tunggal Dalam Optimalisasi Portofolio Saham dan Long Short Term Memory dalam Peramalan Harga Saham Optimal
Abstrak Dalam berinvestasi, berbagai pilihan saham dihadapkan oleh investor. Strategi yang diperlukan oleh investor dapat meminimalkan risiko dan memaksimalkan potensi keuntungan. Salah satu yang diperlukan dengan pendekatan Model Indeks Tunggal untuk dapat mengoptimalkan saham dalam portofolio, sehingga dapat memberikan keputusan saham yang optimal. Pada saham yang telah optimal dapat dilanjutkan untuk diramalkan harga saham terpilih. Harga saham memiliki pergerakan ketidakpastian, sehingga diperlukan pendekatan Long Short-Term Memory (LSTM). Pendekatan LSTM untuk menangani kompleksitas harga saham. Pendekatan LSTM ini dapat memberikan hasil peramalan harga saham yang akurat pada periode mendatang. Tujuan penelitian untuk mengoptimalisasi portofolio saham serta meramalkan harga saham optimal terpilih untuk periode mendatang. Data penelitian menggunakan data saham Blue Chip. Pada tahapan pertama dilakukan dengan Model Indeks Tunggal dan tahapan kedua meramalkan harga saham yang telah optimal terpilih dengan LSTM. Hasil tahapan pertama diperoleh 13 saham yang termasuk ke dalam portofolio optimal. Pada hasil tahapan kedua diperoleh peramalan kinerja harga saham dengan performa terbaik. Akurasi peramalan kinerja harga saham diperoleh dengan MAPE sebesar 4,23%. Hasilnya dapat dikatakan peramalan dari kinerja harga saham memiliki akurasi sangat baik. Kata kunci: Portofolio Saham, Model Indeks Tunggal, , Long Short-Term Memory (LSTM)
Copyright (c) 2025 Kurnia' Aeni https://creativecommons.org/licenses/by-sa/4.0
Identification of Banking Stock Risk Factors through Stochastic Search Variable Selection in a CoVaR Models Based on Quantile Regression and Quantile Autoregressive
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.
Copyright (c) 2025 Luqyana Almas, Dedy Dwi Prastyo, Santi Puteri Rahayu https://creativecommons.org/licenses/by-sa/4.0
Statistical Optimization of Experimental Conditions for Enhanced Removal of Heavy Metals from Wastewater
Increasing concentrations of heavy metals in industrial liquid waste demand the development of more efficient treatment strategies to minimize the impact on the environment and public health. This study aims to statistically optimize experimental conditions to improve the efficiency of heavy metal removal (especially Pb²⁺, Cd²⁺, and Cr⁶⁺) from synthetic liquid waste through the batch adsorption process. A well-planned experimental design was implemented using the Response Surface Methodology (RSM) approach with Central Composite Design (CCD) to evaluate the individual influences as well as interactions of four key variables: the initial concentration of the metal, the pH of the solution, the adsorbent dose, and the contact time. The results of the experiment were modeled in the form of second-order polynomial regression, and model validation was carried out strictly through variety analysis (ANOVA), determination coefficients (adjusted R² and R²), and lack-of-fit tests. The optimization process successfully identified a combination of operating parameters that significantly improved the elimination efficiency, reaching a level above 95% at the optimized conditions that had been validated. The residue analysis showed the fulfillment of the assumptions of normality, homogeneity of variance, and error independence, thus confirming the predictive reliability of the model. These findings confirm the effectiveness of RSM-based optimization approaches in wastewater treatment research, as well as the importance of statistical-based experimental planning in maximizing process efficiency. This approach provides a robust framework for advanced applications in industrial waste management and sustainable environmental engineering.
Copyright (c) 2025 Pismia Sylvi https://creativecommons.org/licenses/by-sa/4.0
Logistic Regression Analysis to Determine Factors Influencing Career Choices of Undergraduate Alumni
An undergraduate alumni has three career choices, namely work, postgraduate study or entrepreneurship. The success of an alumni's career is not only influenced by academic knowledge, but also by other factors that reflect the individual's personal and professional abilities. These factors are certainly obtained from the experiences that the alumni have gone through while studying for their undergraduate program. The respondents of this study were 100 undergraduate alumni of Universitas Negeri Medan in 2021 and 2022. This study aims to determine the effect of GPA (X1), study period (X2), number of achievements (X3), organizational activity (X4), work intensity during undergraduate (X5), and TOEFL score (X6) on alumni career choices (Y). In logistic regression analysis, the independent variable must have at least two categories so that the alumni's career choices (Y) are working (0) and postgraduate study (1). The results of the study showed that only the work intensity factor during S1 (X5) and TOEFL score (X6) had a significant influence on alumni career choices. The Logistic Regression Model based on the influential variables was . The percentage of model accuracy can predict correctly by 72.0%.
Copyright (c) 2025 Didi Febrian, Putri Harliana, Nurul Ain Farhana https://creativecommons.org/licenses/by-sa/4.0
Modeling The Open Unemployment Rate Using A Ridge-Fourier Series Estimator In Nonparametric Regression
Nonparametric regression is widely used to model relationships between variables when the functional form of the regression curve remains unspecified. The Fourier series estimator is effective for capturing periodic and complex nonlinear patterns. However, in multivariable settings, strong correlations among predictors may lead to multicollinearity, resulting in unstable parameter estimates due to the near-singularity of the design matrix. Although Fourier-based estimators have been extensively developed, their formulation has not explicitly addressed this issue. This study proposes a ridge-Fourier series estimator for nonparametric regression to obtain stable parameter estimation in the presence of multicollinearity. The estimator is derived within a penalized likelihood framework by incorporating a ridge penalty into the Fourier series representation and estimating the parameters. The oscillation and ridge penalty parameters are selected simultaneously using the Generalized Cross Validation (GCV) criterion. The proposed method is applied to the 2024 Open Unemployment Rate data covering districts and cities in West Java. The optimal model is obtained at the oscillation combination of [4, 4, 4, 2] with a ridge parameter of 0.0336, accompanied by a coefficient determination of 91.1%, indicating that the proposed ridge-Fourier estimator provides more stable estimation and improved predictive performance under multicollinearity conditions.
Copyright (c) 2025 Sidratul Muthahharah, I Budiantara, Vita Ratnasari https://creativecommons.org/licenses/by-sa/4.0
Comparative Evaluation of RK4 and ABM4 Methods for Underdamped Transient Response Analysis of RLC Circuits
The transient response of series RLC circuits in the underdamped regime displays oscillatory behavior that requires numerical method capable of preserving both amplitude and phase accuracy. This study aims to compare the numerical performance of two fourth-order integration methods, namely the classical Runge-Kutta method (RK4) and the Adams-Bashforth-Moulton predictor-corrector method (ABM4), in simulating the transient response of a series RLC circuit. The circuit dynamics are formulated as a system of first-order ordinary differential equations derived from Kirchhoff’s Voltage Law and solved numerically using both methods. The simulations are performed using practical circuit parameters representative of low-power AC systems and zero initial conditions. Numerical simulation is evaluated in terms of computational efficiency, global accuracy measured by root mean square error relative to the analytical solution, and local accuracy assessed through peakwise relative error at the first oscillation peak. The results show that ABM4 consistently requires less computational time than RK4 for the same number of time steps due to its multi-step formulation. However, RK4 provides smaller global and peakwise errors, indicating superior phase accuracy. These results highlight a trade-off between computational efficiency and numerical accuracy in underdamped RLC transient simulations.
Copyright (c) 2025 David Eka Putra, Elvi Syukrina Erianto, Halim Mudia https://creativecommons.org/licenses/by-sa/4.0
Bellman-Ford Algorithm for Optimizing Drinking Water Distribution by Perumda Air Minum Tirta Raharja in Cicalengka
Access to clean water in Cicalengka District, Bandung Regency, remains limited, with current coverage reaching only 5.08% of the population. Perumda Air Minum Tirta Raharja has set a target to expand drinking water service to 44% by 2030. To support this goal, this study investigates the optimization of the water distribution pipe network in Cicalengka. The research applies the Bellman-Ford algorithm to model the distribution network as a weighted, undirected, and connected graph, where customer houses are represented as vertices, pipe connections as edges, and pipe lengths as weights. Using data from the existing network and customer locations, the algorithm was implemented to identify optimal distribution paths. The results yielded two shortest path alternatives between the specified source and destination nodes. These findings demonstrate the potential of graph-based optimization in improving distribution planning and can serve as a reference for the development and management of future water supply infrastructure.
Copyright (c) 2025 Anie Lusiani, Euis Sartika https://creativecommons.org/licenses/by-sa/4.0