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Zero : Jurnal Sains, Matematika, dan Terapan
Published by Universitas Islam Negeri Sumatera Utara
ISSN : 2580569X     EISSN : 25805754     DOI : 10.30829
Core Subject : Science, Education, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics
Arjuna Subject : -
Articles 222 Documents
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Determinants of Dengue Hemorrhagic Fever in Aceh: A Panel Regression Approach Muliani, Fitra; Saumi, Fazrina; Amelia, Amelia; Amalia, Rizki
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.26784

Abstract

Dengue Hemorrhagic Fever (DHF) exhibits substantial variation across districts and over time in Aceh Province, making it suitable for analysis within a panel data framework. This study models district-level DHF incidence using applied econometric techniques based on non-spatial panel data regression, employing a balanced panel dataset of 23 districts/cities observed from 2020 to 2022. The Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM) are estimated and formally compared using the Chow test, Hausman test, and Lagrange Multiplier test, with results consistently indicating that the Fixed Effect Model is the most appropriate specification due to the presence of unobserved, time-invariant district-specific effects. Diagnostic testing identifies heteroskedasticity in the error structure; therefore, the selected FEM is re-estimated using White cross-section robust standard errors to ensure reliable statistical inference. Empirical results show that population density is positively and statistically significantly associated with DHF cases, while the number of health workers is negatively and significantly associated, whereas rainfall, number of hospitals, sanitation coverage, and poverty level do not exhibit statistically significant effects in the final robust specification. The selected model explains approximately 86% of the within-district variation in DHF incidence, demonstrating the importance of appropriate model specification and robust variance estimation in panel data regression applied to epidemiological outcomes, while emphasizing that the estimated relationships represent statistical associations rather than causal effects.
The Valuation of European Options with Transaction Costs Using the Barles-Soner Model Taufik, Muchammad; Artiono, Rudianto
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.27702

Abstract

This study discusses the pricing of European call options by considering transaction costs using the Barles-Soner model. The method used in this study is an analytical and asymptotic approach based on literature studies. Stock and option price data were obtained from Yahoo Finance, while the risk-free interest rate was taken from the Federal Reserve Bank of St. Louis. Volatility was calculated based on historical stock return data, while transaction costs and risk aversion parameters were determined based on previous studies. The Barles-Soner model introduces non-linear effective volatility, which is estimated using an asymptotic approach to obtain effective volatility. The option price calculated using the Barles-Soner model is , higher than the option price using the Black-Scholes model of , with a difference of . These results confirm that transaction costs have a significant effect on option prices. Therefore, the Barles-Soner model is more comprehensive in calculating stock option prices than the Black-Scholes model.
Fuzzy-AHP for Teaching Quality Assessment and Student Performance Prediction in Mathematics Education Program Sihombing, Dame Ifa
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.27041

Abstract

This study proposes a fuzzy-Analytic Hierarchy Process (Fuzzy-AHP) model to evaluate teaching quality and predict student academic performance in a Mathematics Education program, based on data collected from 100 undergraduate Mathematics Education students (n = 100). A structured Evaluation Index System (EIS) comprising six criteria and twenty-six sub-criteria was constructed, with criterion weights derived using AHP based on expert judgments and student responses represented as triangular fuzzy numbers. The model produces composite teaching quality scores through fuzzy aggregation and centroid defuzzification, identifying Integration and Relevance of Teaching as the most influential dimension. Predictive validation using Spearman correlation and linear regression confirms a significant positive relationship between teaching quality and academic performance (ρ = 0.46, p < .01), with instructional quality explaining 21% of performance variance. From an applied mathematics perspective, this study contributes a formally structured fuzzy-AHP modelling framework with empirical predictive validation, advancing teaching quality assessment beyond descriptive ranking toward evidence-based performance prediction.
Technical Assessment of Neuro-Symbolic AI for Cultural and Fractal Analysis of Batik Motifs Tullah, Rahmat; Stianingsih, Lilis
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.26781

Abstract

This study presents a structured literature review of Neuro-Symbolic Artificial Intelligence (NSAI) approaches for extracting cultural semantics and fractal features from Batik motifs. A structured multi-database screening (2015-2025) yielded 69 peer-reviewed studies, which were synthesized thematically. The review identifies three key findings: existing vision-based models generally lack explicit mechanisms for encoding intangible cultural rules; hybrid neural-symbolic approaches demonstrate improved interpretability and compositional reasoning; and fractal-based descriptors show promise for representing culturally grounded motif structures. Based on these findings, this study proposes a conceptual NSAI framework that combines symbolic knowledge representations with fractal feature modeling, without empirical validation at this stage. The synthesis highlights potential applications in motif recognition, generative motif modeling, and computer-assisted cultural heritage preservation. Overall, NSAI offers a feasible and explainable conceptual framework for modeling Batik's intangible cultural knowledge.
Spatial Grouping of Tornado-Relevant Wind Regimes Areas in Indonesia to Enhance Disaster Risk Mitigation Capacity Ardelita, Sela Naren; Sofro, A'yunin
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.27088

Abstract

Tornadoes are major weather hazards in Indonesia, where wind variability is important for assessing disaster risk and supporting energy planning. This study conducts a short-term (one-year) analysis by identify similarities in regional wind speed patterns using a time-series clustering approach, treating monthly average wind speeds in 2024 as proxies for tornado-relevant wind regimes rather than direct tornado occurrence data. Agglomerative hierarchical clustering is integrated with three distance measures-Dynamic Time Warping (DTW), Autocorrelation Function (ACF), and Short Time Series (STS)-and optimized using Brain Storm Optimization (BSO) to determine optimal distance weighting and cluster numbers. The results indicate that DTW provides the best performance, yielding a two-cluster solution with a Silhouette Coefficient of 0.5292. The first cluster exhibits relatively stable wind patterns, while the second shows higher temporal variability. This framework provides a data-driven basis for region-specific wind energy planning and tornado-adaptive infrastructure considerations in Indonesia.
Optimizing Electric Vehicle Charging Station Placement in Banyumas Using Graph Domination Theory Prasetyo, Yogo Dwi; Sumardi, Hari
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.25502

Abstract

This study applies graph theory to optimize the placement of electric vehicle (EV) charging stations in Banyumas Regency, Indonesia, using real geospatial data from 27 sub-districts. Each sub-district is modeled as a vertex, with edges defined by a 10 km coverage radius. The domination number is employed to identify the minimum number of charging stations required to ensure full spatial coverage. Unlike prior EV infrastructure studies in Indonesia that primarily rely on demand-based heuristics or clustering methods, this research explicitly guarantees coverage through graph domination theory. To enhance robustness, the domination-based solution is compared with a Set Covering Problem solved using Ant Colony Optimization (ACO). Both approaches consistently identify six strategic locations, achieving 100% coverage while reducing infrastructure requirements by approximately 78% compared to a one-station-per-sub-district strategy. The results provide practical guidance for policymakers and urban planners by supporting cost-efficient, scalable, and equitable EV charging infrastructure deployment in regions with early-stage EV adoption.
Hybrid ARIMA-LSTM Model for Gold Price Forecasting at Pegadaian Pujiarini, Erna Hudianti
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.25962

Abstract

Accurate forecasting of digital gold prices at PT Pegadaian is essential for managing volatility driven by macroeconomic factors, including exchange rates, inflation, and global gold prices. Conventional models present limitations: ARIMA effectively captures linear trends but fails to model non-linear patterns, whereas LSTM handles non-linearity but is prone to overfitting and poor generalization. This study proposes a hybrid ARIMA-LSTM model based on a quantitative time series approach. The analysis uses secondary data comprising daily digital gold prices from PT Pegadaian (2024-2025) and related macroeconomic indicators obtained from BPS and Bank Indonesia. Data are preprocessed to ensure stationarity and quality prior to modeling. The hybrid model combines linear forecasts from ARIMA with LSTM modeling of the resulting non-linear residuals. The hybrid model achieved MSE = 54,294.23, MAE = 113.56, and RMSE = 233.01 on the test set, representing reductions of approximately 75% in MSE, 54% in MAE, and 50% in RMSE relative to the standalone LSTM on testing data. The hybrid model outperforms both individual ARIMA and LSTM models in terms of generalization and accuracy. A primary limitation is the use of manual hyperparameter tuning; implementation of automated methods, such as grid search or Bayesian optimization, could further improve performance and robustness.
Rainfall Risk Modelling for Rice Farming Using Continuous Hidden Markov Models Martal, David Vijanarco; Setiawaty, Berlian; Budiarti, Retno
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.25443

Abstract

Climate change has increased rainfall variability and unpredictability, significantly impacted agricultural productivity, and raised the risk of crop failure, particularly in rain-fed rice farming systems. This study models rainfall data from Tabanan, Bali, using a continuous-time Hidden Markov Model (HMM) to identify latent weather states and assess the associated risk of rice crop failure. The model assumes four hidden states, each generating rainfall observations following a Gamma distribution. Simulation results produced Mean Absolute Percentage Error (MAPE) values below 5% for training and testing sets, indicating strong model performance in replicating rainfall patterns. Risk analysis compared simulated rainfall with rice crop water requirements across three planting periods. The second planting period (July-October) exhibited the highest risk at 3.75%. Compared to other predictive models, HMM offers superior capability in capturing temporal rainfall structure and identifying critical transition phases, making it highly suitable for agricultural risk assessment and climate-adaptive planning.
Comparative Analysis of SmartPLS, WarpPLS, and R Studio: Accuracy, Features, Usability, and Licensing Prasetyo, Fajar Hari; Edo Kisworo, Dika Kurnia; Mahardhika, Indra Bagus; Ghifari, Shoim
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.27040

Abstract

Partial Least Squares Structural Equation Modeling (PLS-SEM) is widely applied to analyze relationships among latent variables using different software tools. This study compared SmartPLS, WarpPLS, and R Studio from quantitative and qualitative perspectives. A synthetic dataset (N=100) with a simple reflective model (X1, X2→Y) was analyzed under equivalent settings, including reflective indicators and bootstrapping with 5,000 resamplings, to ensure structural equivalence and highlight algorithmic differences. Quantitative results showed consistent external loadings above 0.70, with small numerical deviations (overall MAD=0.039) and the largest variation in item Y4 (MaxDiff=0.087). Internal model estimates were stable, with minor differences in path coefficients (MaxDiff≤0.040) and larger variation in R² for R Studio (MaxDiff=0.098), reflecting differences in latent score calculations. Bootstrapping confirmed significance (T>1.96; p<0.05), though variability in T statistics was observed across software. Qualitatively, SmartPLS excelled in usability and visualization, WarpPLS in nonlinear analysis, and R Studio in flexibility and cost-effectiveness. Computationally, SmartPLS consumed the most memory, R Studio was moderate, and WarpPLS was most efficient, with all execution times under five seconds. These findings suggest that software choice should consider not only numerical accuracy but also usability, licensing, and computational efficiency to align with research objectives and user competencies.
Application of Goal Programming Model for Optimization Tofu Production Planning Br Sinaga, Juli Antasari; Saragih, Rajainal; Lilis, Lilis; Purba, Yoel Octobe; Saragih, Reagen Surbakti
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.26556

Abstract

Previous studies on production planning in food SMEs have largely focused on single-objective optimization and often lack explicit optimality benchmarks under real resource constraints. This study addresses this gap by developing a multi-objective production planning model using Goal Programming for a small-scale tofu enterprise. The proposed model simultaneously considers profit targets, demand fulfillment, raw material availability, and labor hour limitations. The novelty of this research lies in the use of deviation-based optimality benchmarks and its practical implementation using POM-QM for Windows. Model validation is conducted through a real case study, resulting in an optimal production mix of 724 units of white tofu, 568 units of fried tofu, and 517 units of yellow tofu, achieving a monthly profit of IDR 73.90 million with zero positive deviation from the profit target. Resource utilization remains within capacity limits, as indicated by negative deviations in raw material usage and labor hours. These results demonstrate that Goal Programming is an effective and practical decision-support tool for resource-constrained food SMEs.

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