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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 241 Documents
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Comparative Study of Hybrid ARIMA-LSTM and CNN-LSTM for Palm Oil Price Forecasting Putri, Rizki Alifah; Notodiputro, Khairil Anwar; Susetyo, Budi
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.27631

Abstract

The forecasting of highly volatile time series data remains a significant challenge due to complex, non-linear patterns. This study compared the performance of two hybrid frameworks, ARIMA-LSTM and CNN-LSTM, which were designed to integrate the statistical strengths of traditional models with the computational power of deep learning. In these architectures, the ARIMA component was utilized to extract linear trends, while the LSTM and CNN layers were employed to identify and manage non-linear dynamics within the data. Utilizing 384 monthly palm oil price data points (1993-2024) sourced from FRED, the models were evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results demonstrated that the hybrid CNN-LSTM outperformed the ARIMA-LSTM and individual models, achieving a superior MAPE of 6.69%. These findings indicated that the integration of Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks was more effective in capturing the complexities of price fluctuations. Practically, the study concluded that accurate forecasting served as a critical tool for market stabilization, thereby supporting broader goals of financial certainty and ecological sustainability.
Integrating Clustering and Hybrid Nonparametric Path Modeling for Waste Management Behavior in Batu City Hidayatulloh, Moh Zhafran; Solimun, Solimun; Fernandes, Adji Achmad Rinaldo; Rizqia, Anggun Fadhila; Junianto, Fachira Haneinanda
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.26671

Abstract

This study formulates a hybrid nonparametric path analysis utilizing truncated spline-Fourier series integrated with Fuzzy C-Means clustering methodologies to investigate nonlinear behavioral patterns in community-based waste management. The model was utilized on survey data from 210 respondents in Bumiaji District, Batu City, encompassing Environmental Quality, Use of Waste Banks, Use of the 3R Principles, and Economic Benefits from Waste. Two distinct behavioral groups with varying emphasis on Use of Waste Banks and Environmental Quality were identified by the analysis. The hybrid model obtained a high coefficient of determination (0.891) and captures nonlinear relationships more successfully than the traditional linear approach. These results emphasize how crucial nonlinear behavioral dynamics are in influencing waste management behavior/decisions. The proposed framework helps local governments create more focused and efficient waste-management plans by offering a useful and adaptable analytical for understanding community behavior.
Simplex vs. Revised Simplex for Budget Allocation in Madrasah Teacher and Staff Development Programs under MoRA Kudus, Indonesia Fakhriyana, Dina; Anggreini, Mia Ning
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.26870

Abstract

This study compared the Simplex and Revised Simplex Methods for allocating the 2025 budget for the madrasah teacher and staff development programs under the Madrasah Education Division of the Ministry of Religious Affairs (MoRA) in Kudus, Indonesia. A quantitative case study design was employed to formulate a weighted linear programming model that identifies the most beneficial combination of programs under fixed budget constraints, with an emphasis on procedural comparison rather than policy impact. Both methods converged to the same optimal solution after four iterations, indicating equivalent computational performance for the small-scale model. The optimal allocation resulted in implementable program frequencies and achieved a maximum total weighted benefit of 129,133.6, primarily by prioritizing cost-free, high-benefit training programs while maintaining compliance with budget limits. Methodologically, the study demonstrates a transferable and reproducible linear programming framework that contributes to applied mathematics by illustrating how Simplex-based techniques can be adapted as transparent decision-support tools for small-scale public-sector budgeting problems. Although the Revised Simplex Method offers theoretical advantages for larger models, empirical differences in this case were minimal due to the model’s scale. The study was limited to a single fiscal year and did not include sensitivity analysis, which should be addressed in future research.
A Mixed Integer Linear Programming Based Scheduling Model for Cost Minimization in Sea Tollway Vessel Operations Ayuniar, Jauza Ananda; Astuti, Yuliani Puji
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28254

Abstract

The Sea Tollway Program plays a critical role in Indonesia’s national logistics system by improving maritime connectivity and reducing regional disparities in goods distribution. However, operational inefficiencies in vessel scheduling and prolonged berthing times continue to limit its effectiveness. This study addresses these challenges by formulating a deterministic Mixed Integer Linear Programming (MILP) based vessel scheduling model with capacity and cargo flow constraints aimed at minimizing time-dependent operational cost and improving berthing time efficiency. A case study is conducted on Sea Tollway Route H-1 using operational data from the first semester of 2025. The optimization model is implemented using the PuLP library and solved with the CBC solver. The results show that the optimized schedules consistently reduce operational costs by approximately 5–8% per voyage and decrease berthing time by about 12–17%, corresponding to an average reduction of two hours per voyage. Statistical significance testing confirms that these improvements are not due to random variation, while sensitivity analysis demonstrates the robustness of the optimized solutions under changes in key operational parameters. Overall, the proposed MILP-based framework provides a mathematically sound and practically applicable decision-support tool for improving vessel scheduling and operational efficiency in Sea Tollway maritime logistics.
A Conceptual Consortium Blockchain Model to Enhance Integrity and Auditability in Indonesia’s Election Result Recapitulation Pranowo, Muhammad Hadi; Nurhadryani, Yani; Hermadi, Irman
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.27710

Abstract

Election results recapitulation requires stakeholder participation, data integrity, and auditability. However, electronic systems remain largely centralized and provide limited support for multi-stakeholder involvement, traceable audit trails, and verifiable record integrity. This study formalizes the recapitulation process as a rule-based state-transition model and proposes a consortium Blockchain architecture as a parallel recapitulation channel. Built on Hyperledger Fabric, the network comprises peer nodes representing election officials, supervisors, and witnesses, and is governed by smart contracts with policy-driven endorsement. The model encodes stakeholder roles, cryptographic document binding through IPFS content identifiers and hash verification, and rule-based state validation, with emphasis on arithmetic consistency and plenary time-window constraints. The artifact is evaluated through regulatory compliance mapping and computational testing. The results show that the model enables multiparty verification, preserves auditable transaction history, and maintains verifiable linkage between recapitulation records and supporting documents, while constraining accepted state transitions to defined arithmetic and procedural rules.
Family Clustering Based On Y-Chromosome DNA Profile Using Unweighted Pair Group Method with Arithmetic Mean Dewi, Meira Parma; Soedarsono, Nurtami
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28505

Abstract

Indonesia is a diverse nation composed of numerous ethnic groups, each with distinct physical and genetic characteristics. Genetic similarities within ethnic populations can be examined through DNA profiling, particularly by analyzing Short Tandem Repeat loci. In Indonesia, DNA profiling has been widely applied in forensic identification and paternity testing. This study focuses on classifying the Javanese population into sub-tribes based on STR profile similarities using divisive hierarchical clustering. The optimal number of clusters was determined by the smallest Sum of Squared Errors (SSE), with the lowest value of 72583.12and the highest Silhouatte coeffisien value is 0.78, yielding seven sub-tribe clusters. Subsequently, these sub-tribe clusters were further classified into family clusters using Y-chromosome STR (YSTR) data, which traces paternal lineage. The clustering process employed the Unweighted Pair Group Method with Arithmetic Mean, resulting in 21 family clusters. Compared to k-means clustering, divisive clustering produced sub-tribe clusters with more balanced population sizes. The establishment of sub-tribe and family clusters enhances the efficiency of individual identification, as DNA profile matching can be performed at the cluster level rather than across the entire population. This approach provides a more systematic framework for forensic applications and victim identification, particularly in cases involving male individuals where YSTR data is critical.
Hierarchical Ensemble Actuarial Method for Motor Claim Reserving under Indonesia’s PSAKBI Awaloedin, Mulawarman
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28134

Abstract

This study develops and evaluates a hierarchical ensemble actuarial approach for motor claim reserving under Indonesia’s PSAKBI framework. The method integrates traditional actuarial techniques with modern machine learning models in a structured ensemble design to enhance predictive accuracy, reliability, and transparency. Using motor insurance claim data, the ensemble was compared against conventional single-model reserving practices. Results show that the proposed approach achieves lower prediction error (MSE = 220.3), accurate calibration (94.7% coverage), and more stable reserve estimates across accident years. Beyond statistical performance, the design emphasizes interpretability by tracing predictions to weighted contributions of base models, thereby avoiding black-box behavior. These findings highlight the practical relevance of hybrid ensemble reserving in regulated environments, offering a transparent and robust solution aligned with PSAKBI requirements. The study contributes to the literature by demonstrating how hybrid actuarial ensembles can balance methodological rigor, machine learning flexibility, and regulatory compliance in insurance reserving.
Crank-Nicolson Finite Difference Pricing of European Call Options under the Black-Scholes Model Adawiyyah, Robiyatul; Artiono, Rudianto
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28482

Abstract

This study aims to determine the price of European call options using the Crank–Nicolson finite difference method in the Black–Scholes model with stock data from XYZ Company for the period January 2025 to December 2025. Annual volatility is calculated based on historical closing price data, while numerical option prices are obtained through the Crank–Nicolson finite difference scheme and compared it with the Black–Scholes analytical solution as a reference. The results show that the Crank–Nicolson method produces a call option price of 596.08, while the Black–Scholes analytical solution gives a value of 612.50. The relative difference between the two methods is 2.68%, which indicates a good level of accuracy for the numerical method used. These findings indicate that the Crank–Nicolson finite difference method is capable of providing a stable and accurate numerical approach to determining the price of European call options. In practical terms, the results of this study contribute to the application of numerical-based option pricing models in emerging markets, particularly in conditions of dynamic volatility, where analytical approaches may have limitations in implementation
Mathematical Model of Zakat and Low-Tax Policy Effects on Economic Population Subhan, Muhammad; Sriningsih, Riry
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28497

Abstract

Many Islamic countries implement low-tax policies, but these often fail to reduce inequality and poverty. Zakat has a function to redistribute wealth, but previous studies fail to explore its dynamic interaction with tax systems. This study builds a mathematical model that describes the combined effects of zakat and low-tax policies on economic population dynamics. We develop a system of differential equations that represents wealthy, middle, and poor populations, then analyze it qualitatively by finding equilibrium points and their stability. Analysis reveals a single asymptotically stable equilibrium point, indicating that zakat effectively reduces the wealth gap, even under a low-tax setting. We also established a threshold condition for the middle-to-poor population ratio, guiding policymakers on designing effective poverty interventions. This study lays a theoretical groundwork that incorporates Islamic financial institutions into poverty reduction strategies.
Spatial Heterogeneity of Tuberculosis Incidence Using Geographically Weighted Negative Binomial Regression (GWNBR) in Indonesia Sriningsih, Riry; Soleh, Mohammad; Subhan, Muhammad; Gusty, Reni Prima
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28500

Abstract

Poisson regression is widely used for count data but relies on the equidispersion assumption, which is often violated in epidemiological data due to overdispersion. Negative Binomial Regression (NBR) addresses this issue by introducing a dispersion parameter. However, both models assume spatial homogeneity of parameters. This study applies Geographically Weighted Negative Binomial Regression (GWNBR) to analyze tuberculosis (TB) cases across 38 provinces in Indonesia in 2024. The response variable is the number of TB cases, with predictors including population density, smoking prevalence (age   15), poverty rate, and number of hospitals. Overdispersion was confirmed (deviance/df = 12,020), justifying the use of NBR. Model comparison shows that GWNBR provides improved fit relative to global models, with lower AIC than the NBR model (716.45 vs 732.29). Spatial heterogeneity was confirmed by the Breusch–Pagan test (BP = 21.011; p  0.01). Provinces exhibit distinct patterns of significant determinants; for example, in West Sumatra, poverty and smoking show strong positive local effects, while in several eastern provinces smoking is not significant. These findings highlight the importance of spatially adaptive TB control policies rather than uniform national strategies.

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