ZERO: Jurnal Sains, Matematika dan Terapan
type: channel
description: International Academic Journal Profile ZERO: Jurnal Sains, Matematika dan Terapan Peer-reviewed journal in mathematics, statistics, computer science, and applied ZERO: Jurnal Sains, Matematika dan Terapan is a blind-reviewed academic journal, established in 2017 under the State Islamic University of North Sumatra Medan. The journal publishes high-quality scholarly articles in mathematics, statistics, computer science, information systems, and related applications. Journal Information Subject Mathematics, Statistics, Computer Science, and Applied Language English ISSN 2580-5754 (Online), 2580-569X (Print) Frequency Triannual (three issues per year) DOI Prefix 10.30829 by Crossref Publisher Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sumatera Utara Medan Indexing SINTA 2, DOAJ, Garuda, Google Scholar Contact WhatsApp: +62 852-7000-9767 The journal welcomes original research articles and scholarly contributions in mathematics, statistics, computer science, information systems, and applied interdisciplinary studies. As a triannual journal, ZERO is committed to maintaining academic quality, editorial integrity, and international publication standards.
publisher: UIN Sumatera Utara
language: en-US
publication name: ZERO: Jurnal Sains, Matematika dan Terapan
issn: 2580-569X
copyright: Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
items: Numerical Pricing of European Stock Option Based on Black-Scholes Model Using Crank-Nicolson Method
items: Exploring the Metric Chromatic Number of Uniform, Centralized Uniform, and Cycle Uniform Theta Graphs
items: Forecasting Macroeconomic Variables in Indonesia Using the Vector Autoregressive Method
items: Bayesian Path Modeling Simulation for Stunting Analysis
items: Numerical Pricing of European Options under Proportional Transaction Costs: A Semi-Discretization Approach to the Nonlinear Barles-Soner Model
items: A Bibliometric Analysis of Mathematics Education Research in Elementary Schools: Trends, Institutions, and Post-Pandemic Learning Integration (2020–2025)
items: Comparative Study of Hybrid ARIMA-LSTM and CNN-LSTM for Palm Oil Price Forecasting
items: Integrating Clustering and Hybrid Nonparametric Path Modeling for Waste Management Behavior in Batu City
items: Simplex vs. Revised Simplex for Budget Allocation in Madrasah Teacher and Staff Development Programs under MoRA Kudus, Indonesia
items: A Mixed Integer Linear Programming Based Scheduling Model for Cost Minimization in Sea Tollway Vessel Operations
items: Local Stability Analysis of Leptospirosis SEITR - SI Model with the Influence of Hydroclimatic Rate and Treatment
items: Comparative Analysis of Four Actuarial Cost Methods on Unfunded Actuarial Liability in a Pekanbaru Pension Scheme
items: Spatial Heterogeneity of Tuberculosis Incidence Using Geographically Weighted Negative Binomial Regression (GWNBR) in Indonesia
items: A Conceptual Consortium Blockchain Model to Enhance Integrity and Auditability in Indonesia’s Election Result Recapitulation
items: Family Clustering Based On Y-Chromosome DNA Profile Using Unweighted Pair Group Method with Arithmetic Mean
items: Hierarchical Ensemble Actuarial Method for Motor Claim Reserving under Indonesia’s PSAKBI
items: Crank-Nicolson Finite Difference Pricing of European Call Options under the Black-Scholes Model
items: Mathematical Model of Zakat and Low-Tax Policy Effects on Economic Population
items: Comparative Modeling of Pineapple Production Using Gaussian GLM and Random Forest Regression
items: Optimization of Real-Time Object Detection in Viola-Jones Method with Enhanced AdaBoost
items: Geometric Data Augmentation with a Two-Stage Fine-Tuning Strategy for EfficientNetB3-Based Fruit Condition Classification
items: Optimization of Retention Levels Using the Pentikäinen Method: A Case Study on Unit-Linked Insurance Product
items: Adaptive Continuous Parameter Optimization of ARIMA using a Hybrid GA–PSO Approach for Time Series Forecasting
items: Actuarial Evaluation of Additional Contributions in Early Retirement Programs Using the Spreading Gains and Losses Method

Numerical Pricing of European Stock Option Based on Black-Scholes Model Using Crank-Nicolson Method
type: item
description: Accurate option pricing is an important issue in quantitative finance, especially in emerging financial markets, which are generally characterized by price volatility and limited historical data. This research evaluates the numerical performance of the classical Crank–Nicolson finite difference method in determining the price of European call options based on the Black–Scholes model using Indonesian stock market data. The Black–Scholes equation is discretized on a uniform spatial and temporal grid, and the numerical solution is verified by comparison with the Black–Scholes analytical solution as a mathematical reference. The numerical results show that the Crank–Nicolson method produces stable and convergent solutions, with a relative error of less than 1% at a sufficiently fine grid resolution. Furthermore, sensitivity analysis to volatility and temporal convergence tests demonstrate the consistency of the numerical solution's behavior to variations in the model's key parameters. These findings indicate that the Crank–Nicolson method provides a reliable numerical approach for evaluating European option pricing within the classical Black–Scholes framework under the analyzed market conditions.
creator: Isnaini Emma Wimala Putri
creator: Rudianto Artiono
rights: Copyright (c) 2026 Isnaini Emma Wimala http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-29
publication date: 2026-01-29
volume: 10
number: 1
starting page: 1
ending page: 11
doi: 10.30829/zero.v10i1.28070

Exploring the Metric Chromatic Number of Uniform, Centralized Uniform, and Cycle Uniform Theta Graphs
type: item
description: Metric coloring allows adjacent vertices of a graph to share the same color provided that their associated distance vectors are distinct, leading to the concept of the metric chromatic number. This notion is closely related to problems of vertex distinguishability and resource allocation in network-like structures. In this paper, we present the first exact determination of the metric chromatic number for three families of theta type graphs: uniform theta graphs, centralized uniform theta graphs, and a newly introduced class called the cycle uniform theta graph, obtained by cyclically arranging uniform theta subgraphs. The proposed construction enables an investigation of how cyclic configurations influence metric coloring behavior. Using a constructive metric coloring approach, exact values of the metric chromatic number are obtained. It is shown that the uniform theta graph and the centralized uniform theta graph both satisfy for all positive integers and . For the cycle uniform theta graph , the metric chromatic number equals when and have the same parity or when is odd and is even. In contrast, when is even and is odd. This latter case arises because the longest path in the cyclic structure has odd length, forcing the graph to have chromatic number three. Since the graph is connected and its chromatic number is at most three, this structural constraint directly implies that three colors are also necessary for a valid metric coloring.
creator: Raventino Raventino
creator: Fransiskus Fran
rights: Copyright (c) 2026 Raventino Raventino, Fransiskus Fran http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-29
publication date: 2026-01-29
volume: 10
number: 1
starting page: 12
ending page: 24
doi: 10.30829/zero.v10i1.27103

Forecasting Macroeconomic Variables in Indonesia Using the Vector Autoregressive Method
type: item
description: This study aims to analyze the dynamic relationship and predict inflation, the rupiah exchange rate, oil and gas exports, and BI interest rates in Indonesia. The Vector Autoregressive (VAR) method was used with monthly data from January 2015 to September 2025, the best model obtained was VAR (2). The Granger causality test results show that most variables are dominated by their own lag effects. Impulse response analysis shows that shocks are temporary and subside in the medium term. Forecast results for October 2025–September 2026 shows relatively stable inflation in the range of 2.42–2.70%. The rupiah exchange rate is expected to depreciate moderately by around 1.4%. Meanwhile, oil and gas exports declined significantly by 3.9%, and the BI interest rate is expected to fall by 12.8% to 4.14%. These findings indicate that Indonesia's macroeconomic stability is dominated by internal dynamics, so monetary policy focuses on inflation and the exchange rate.
creator: Nonong Amalita
creator: Syafriandi Syafriandi
creator: Yenni Kurniawati
creator: Mira Meilisa
creator: Bunga Nafandra
rights: Copyright (c) 2026 Nonong Amalita http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-29
publication date: 2026-01-29
volume: 10
number: 1
starting page: 25
ending page: 37
doi: 10.30829/zero.v10i1.27949

Bayesian Path Modeling Simulation for Stunting Analysis
type: item
description: This research assesses the interaction between sample size, model complexity, error variance, and their collective impact on the execution of Bayesian path modeling within the context of stunting, using Monte Carlo simulation over 100 datasets over 18 different conditions. The results indicate that the sample size positively impacts the stability of the model, but the more intricate the model and the more noise there is in the data, the greater the effect. A 0.3 error variance unpredictedly lowered the RMSE on various complex scenarios (eg. for nonlinear structures, it decreased from 0.222 to 0.079), and low error variance, combined with nonlinear pathways, led to lower CI coverage (<0.85) and lower ESS, indicating there was difficulty recovering the true parameters A unique contribution of the research is that, in Bayesian modeling, sample size is not the only driver of model complexity, noise, and stunting research. This information is beneficial in providing evidence on how to choose priors, structure models, and conclusively derive results for Bayesian causal analysis in the field of public health.
creator: Celia Sianipar
creator: Adji Achmad Rinaldo Fernandes
creator: Solimun Solimun
creator: Septi Nafisa Ulluya Zahra
rights: Copyright (c) 2026 Celia Sianipar, Adji Achmad Rinaldo Fernandes, Solimun Solimun, Septi Nafisa Ulluya Zahra http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-29
publication date: 2026-01-29
volume: 10
number: 1
starting page: 38
ending page: 48
doi: 10.30829/zero.v10i1.26824

Numerical Pricing of European Options under Proportional Transaction Costs: A Semi-Discretization Approach to the Nonlinear Barles-Soner Model
type: item
description: The classical Black–Scholes model assumes a frictionless market, which often leads to the undervaluation of option premiums when transaction costs are present. This study prices European call options under proportional transaction costs using the nonlinear Barles–Soner framework and a semi-discretization–based numerical approach. Using historical stock data from PT XYZ (an anonymized Indonesian equity), the results show that transaction costs significantly increase effective volatility and generate systematic deviations from classical Black–Scholes prices. In particular, option premiums increase by IDR 392.33 and IDR 776.66 for transaction cost parameters of 0.015 and 0.030, respectively, compared with the frictionless benchmark. These findings confirm that ignoring transaction costs leads to substantial underpricing and that the proposed framework provides a more realistic and conservative valuation for hedging and risk management in emerging markets.
creator: Dwi Maya Firanti Noor
creator: Rudianto Artiono
rights: Copyright (c) 2026 Dwi Maya Firanti Noor http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-29
publication date: 2026-01-29
volume: 10
number: 1
starting page: 49
ending page: 59
doi: 10.30829/zero.v10i1.28084

A Bibliometric Analysis of Mathematics Education Research in Elementary Schools: Trends, Institutions, and Post-Pandemic Learning Integration (2020–2025)
type: item
description: Research on elementary mathematics education in the post pandemic period reflects changes in publication patterns and research topics. This study aims to analyze publication trends, institutional contributions, and forms of learning integration in elementary mathematics education research published between 2020 and 2025. The method employed is a bibliometric analysis of 921 Scopus-indexed articles using RStudio. The analysis covers annual publication trends, institutional productivity, authors’ geographical distribution, keyword-based thematic clusters, and citation patterns. The results show variations in annual publication output, with an increasing trend observed since 2023. Monash University and Universitas Negeri Yogyakarta are identified as the institutions with the highest publication output. Cluster analysis identifies three main thematic groups: student-oriented and numeracy-focused research, studies related to methodological approaches and teacher development, and research concerning pedagogy and curriculum. These findings describe the characteristics and thematic distribution of post pandemic research in elementary mathematics education.
creator: Lola Aprianda
creator: Karsoni Berta Dinata
rights: Copyright (c) 2026 Lola Aprianda, Karsoni Berta Dinata http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-26
publication date: 2026-02-26
volume: 10
number: 1
starting page: 60
ending page: 68
doi: 10.30829/zero.v10i1.28108

Comparative Study of Hybrid ARIMA-LSTM and CNN-LSTM for Palm Oil Price Forecasting
type: item
description: 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.
creator: Rizki Alifah Putri
creator: Khairil Anwar Notodiputro
creator: Budi Susetyo
rights: Copyright (c) 2026 Rizki Alifah Putri, Khairil Anwar Notodiputro, Budi Susetyo http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-26
publication date: 2026-02-26
volume: 10
number: 1
starting page: 69
ending page: 79
doi: 10.30829/zero.v10i1.27631

Integrating Clustering and Hybrid Nonparametric Path Modeling for Waste Management Behavior in Batu City
type: item
description: 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.
creator: Moh Zhafran Hidayatulloh
creator: Solimun Solimun
creator: Adji Achmad Rinaldo Fernandes
creator: Anggun Fadhila Rizqia
creator: Fachira Haneinanda Junianto
rights: Copyright (c) 2026 Moh Zhafran Hidayatulloh, Solimun Solimun, Adji Achmad Rinaldo Fernandes, Anggun Fadhila Rizqia, Fachira Haneinanda Junianto http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-26
publication date: 2026-02-26
volume: 10
number: 1
starting page: 80
ending page: 91
doi: 10.30829/zero.v10i1.26671

Simplex vs. Revised Simplex for Budget Allocation in Madrasah Teacher and Staff Development Programs under MoRA Kudus, Indonesia
type: item
description: 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.
creator: Dina Fakhriyana
creator: Mia Ning Anggreini
rights: Copyright (c) 2026 Dina Fakhriyana http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-27
publication date: 2026-02-27
volume: 10
number: 1
starting page: 92
ending page: 102
doi: 10.30829/zero.v10i1.26870

A Mixed Integer Linear Programming Based Scheduling Model for Cost Minimization in Sea Tollway Vessel Operations
type: item
description: 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.
creator: Jauza Ananda Ayuniar
creator: Yuliani Puji Astuti
rights: Copyright (c) 2026 Jauza Ananda Ayuniar, Yuliani Puji Astuti http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-27
publication date: 2026-02-27
volume: 10
number: 1
starting page: 103
ending page: 113
doi: 10.30829/zero.v10i1.28254

Local Stability Analysis of Leptospirosis SEITR - SI Model with the Influence of Hydroclimatic Rate and Treatment
type: item
description: Leptospirosis is a zoonotic disease whose transmission is influenced by interactions between humans, vectors, and environmental conditions. This study proposes a novel SEITR–SI model that simultaneously integrates treatment dynamics and hydroclimate-dependent transmission within a single analytically tractable framework. Unlike previous SEIR–SI and SIR–SI formulations that either exclude treatment or incorporate hydroclimatic effects through explicit environmental compartments, the proposed model represents hydroclimatic influence implicitly through transmission intensity while preserving mathematical simplicity. The local stability of the disease-free and endemic equilibria is analyzed using the basic reproduction number and the Routh–Hurwitz criterion. The analysis confirms classical threshold behavior governed by , while numerical simulations reveal that increased hydroclimatic transmission intensity elevates endemic infection levels even under higher treatment rates. These findings highlight that treatment alone may be insufficient in hydroclimate-sensitive environments and underscore the importance of integrated control strategies combining medical intervention with environmental and vector management.
creator: Ega Ananta Erlangga
creator: Budi Priyo Prawoto
rights: Copyright (c) 2026 Ega Ananta Erlangga http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-27
publication date: 2026-02-27
volume: 10
number: 1
starting page: 114
ending page: 123
doi: 10.30829/zero.v10i1.28376

Comparative Analysis of Four Actuarial Cost Methods on Unfunded Actuarial Liability in a Pekanbaru Pension Scheme
type: item
description: Unfunded Actuarial Liability (UAL) is an important indicator in assessing the sustainability of a pension fund program, as it reflects the difference between actuarial liabilities and available assets. This study aims to compare the amount of UAL generated by four pension funding methods, namely Accrued Benefit Cost (ABC), Entry Age Normal (EAN), Unit Credit (UC), and Aggregate Cost Method (AGG). A quantitative actuarial valuation is conducted using administrative data from active civil servants of PT. TASPEN (Persero) Pekanbaru Branch Office in 2025 under uniform actuarial assumptions, including the TASPEN mortality table, interest rates, and salary growth rates. A deterministic sensitivity analysis on the discount rate and salary growth rate was conducted to examine the robustness of unfunded actuarial liability estimates. The results show that the ABC, UC, and EAN methods produce identical aggregate UAL values of approximately Rp. 16.841.220.000, - while the Aggregate Cost Method yields a higher UAL of about Rp.22.173.660.000, - due to collective liability recognition. Furthermore, the EAN and AGG methods exhibit relatively more stable contribution patterns over participants’ service periods, whereas the ABC and UC methods are more sensitive to variations in participants’ age and length of service. These findings indicate that the selection of actuarial cost methods has significant practical implications for pension fund management, particularly in terms of funding stability, contribution planning, and long-term sustainability of pension schemes.
creator: Putri Isnaini Cahyaning Baiti
creator: Nasrullah Nasrullah
creator: Annisa Hevita Gustina Kumalasari Saefulloh
creator: Tika Kristin Marbun
rights: Copyright (c) 2026 Putri Isnaini Cahyaning Baiti, Nasrullah - -, Annisa Hevita Gustina Kumalasari Saefulloh, Tika Kristin Marbun http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-27
publication date: 2026-02-27
volume: 10
number: 1
starting page: 124
ending page: 131
doi: 10.30829/zero.v10i1.27520

Spatial Heterogeneity of Tuberculosis Incidence Using Geographically Weighted Negative Binomial Regression (GWNBR) in Indonesia
type: item
description: 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.
creator: Riry Sriningsih
creator: Mohammad Soleh
creator: Muhammad Subhan
creator: Reni Prima Gusty
rights: Copyright (c) 2026 Riry Sriningsih, Mohammad Soleh, Muhammad Subhan, Reni Prima Gusty http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-30
publication date: 2026-03-30
volume: 10
number: 1
starting page: 132
ending page: 143
doi: 10.30829/zero.v10i1.28500

A Conceptual Consortium Blockchain Model to Enhance Integrity and Auditability in Indonesia’s Election Result Recapitulation
type: item
description: 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.
creator: Muhammad Hadi Pranowo
creator: Yani Nurhadryani
creator: Irman Hermadi
rights: Copyright (c) 2026 Muhammad Hadi Pranowo, Yani Nurhadryani, Irman Hermadi http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-30
publication date: 2026-03-30
volume: 10
number: 1
starting page: 144
ending page: 156
doi: 10.30829/zero.v10i1.27710

Family Clustering Based On Y-Chromosome DNA Profile Using Unweighted Pair Group Method with Arithmetic Mean
type: item
description: 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.
creator: Meira Parma Dewi
creator: Nurtami Soedarsono
rights: Copyright (c) 2026 Meira Parma Dewi, Nurtami Soedarsono http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-30
publication date: 2026-03-30
volume: 10
number: 1
starting page: 157
ending page: 167
doi: 10.30829/zero.v10i1.28505

Hierarchical Ensemble Actuarial Method for Motor Claim Reserving under Indonesia’s PSAKBI
type: item
description: 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.
creator: Mulawarman Awaloedin
rights: Copyright (c) 2026 Mulawarman - Awaloedin http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-30
publication date: 2026-03-30
volume: 10
number: 1
starting page: 168
ending page: 179
doi: 10.30829/zero.v10i1.28134

Crank-Nicolson Finite Difference Pricing of European Call Options under the Black-Scholes Model
type: item
description: 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
creator: Robiyatul Adawiyyah
creator: Rudianto Artiono
rights: Copyright (c) 2026 Robiyatul Adawiyyah http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-30
publication date: 2026-03-30
volume: 10
number: 1
starting page: 180
ending page: 189
doi: 10.30829/zero.v10i1.28482

Mathematical Model of Zakat and Low-Tax Policy Effects on Economic Population
type: item
description: 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.
creator: Muhammad Subhan
creator: Riry Sriningsih
rights: Copyright (c) 2026 Muhammad Subhan http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-30
publication date: 2026-03-30
volume: 10
number: 1
starting page: 190
ending page: 201
doi: 10.30829/zero.v10i1.28497

Comparative Modeling of Pineapple Production Using Gaussian GLM and Random Forest Regression
type: item
description: This study aims to conduct a comparative modelling of pineapple production at PT Great Giant Pineapple (GGP) using Gaussian GLM as parametric statistical approach and Random Forest Regression method as machine learning based on monthly data from 2014 to 2022. Multicollinearity testing and distribution fitting were conducted to validate the Gaussian assumption. For the Random Forest Regression, hyperparameters were optimized by tuning the number of trees (ntree) and the number of predictors at each split (mtry) with model stability evaluated using Out-of-Bag (OOB) error. The Gaussian GLM achieved a MAPE of 8.41% (R² = 0.106) for the GP3 clone and 11.27% (R² = 0.149) for the F180 clone. Random Forest Regression produced a testing MAPE of 9.28% (R² = 0.144) for GP3 and 12.11% (R² = 0.105) for F180. While both models achieved low prediction error based on MAPE, they differed in identifying influential variables and showed limited explanatory power as indicated by low R² values. The Gaussian GLM identifies air pressure as significant for both clones and rainfall for F180 clone, while Random Forest consistently identifies rainfall as the most influential predictor. These findings confirm the complementary strengths of parametric and machine learning approaches in supporting climate-based production planning and risk mitigation.
creator: Radot MH Siahaan
creator: Indah Gumala Andirasdini
creator: Fuji Lestari
creator: Dwi Mahrani
creator: Amalia Listiani
rights: Copyright (c) 2026 Indah Gumala Andirasdini http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-30
publication date: 2026-03-30
volume: 10
number: 1
starting page: 202
ending page: 212
doi: 10.30829/zero.v10i1.28721

Optimization of Real-Time Object Detection in Viola-Jones Method with Enhanced AdaBoost
type: item
description: Face recognition is a widely used biometric technology in security systems, automated attendance, and surveillance applications. This study proposes an enhanced real-time face detection method by integrating a modified AdaBoost-based feature selection strategy into the Viola–Jones framework. The applied mathematical contribution of this study lies in formulating the optimization process as an empirical risk minimization model with adaptive boosting weight updates to reduce face recognition error. The proposed approach optimizes the weighting of weak classifiers by prioritizing Haar-like features with minimal weighted classification error at each boosting iteration, thereby improving discriminative capability. Experiments were conducted on a camera-based dataset consisting of face and non-face samples under varying illumination and pose conditions. Prior to optimization, the system achieved a precision of 70.04% and a recall of 70.05%. After applying the proposed enhancement, precision increased to 81.04% and recall to 90.02%. These results demonstrate that the modified AdaBoost integration significantly improves detection accuracy while remaining suitable for real-time face detection applications.
creator: Sucitra Sahara
creator: Rizqi Agung Permana
creator: Mely Mailasari
rights: Copyright (c) 2026 Sucitra Bina Sarana Sahara, Rizqi Agung Permana, Mely Mailasari http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-02
publication date: 2026-04-02
volume: 10
number: 1
starting page: 213
ending page: 223
doi: 10.30829/zero.v10i1.27876

Geometric Data Augmentation with a Two-Stage Fine-Tuning Strategy for EfficientNetB3-Based Fruit Condition Classification
type: item
description: Accurate fruit condition classification is essential for automated food safety assessment, particularly due to health risks associated with chemical contaminants such as formalin. However, reliable generalization in automated inspection systems remains challenging because limited visual variation in image datasets often leads to overfitting in deep learning models. To address this challenge, this study proposes an EfficientNetB3-based framework that integrates geometric data augmentation with a structured two-stage fine-tuning strategy to improve robustness and training stability. The proposed model achieved 99% test accuracy with consistent cross-dataset performance. The framework also demonstrated stable optimization behavior across training stages, indicating improved generalization capability. From a practical perspective, the proposed approach may support scalable food quality monitoring and automated sorting in agricultural supply chains, as well as preliminary food safety screening in large-scale inspection processes.
creator: Hana Sajida Azhurra
creator: Sugiyarto Surono
creator: Aris Thobirin
rights: Copyright (c) 2026 Hana Sajida Azhurra, Sugiyarto Surono, Aris Thobirin http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-03
publication date: 2026-04-03
volume: 10
number: 1
starting page: 224
ending page: 236
doi: 10.30829/zero.v10i1.28735

Optimization of Retention Levels Using the Pentikäinen Method: A Case Study on Unit-Linked Insurance Product
type: item
description: This study evaluates the adequacy of surplus reinsurance retention for unit-linked insurance products in Indonesia under changing market and regulatory conditions. Using 2024 portfolio statistics of Insurance X, the study applies the Pentikäinen method, which is grounded in ruin theory, to determine model-based retention levels and compare them with the company’s actual retention policy. The analysis incorporates premium income, claim experience, reserve considerations, and security loading assumptions. The results show that the company’s actual retention of IDR 500,000,000 is higher than the retention levels generated by the Pentikäinen model, namely IDR 275,000,000 at 0% loading, IDR 305,000,000 at 10% loading, and IDR 335,000,000 at 20% loading. Simulation results further indicate that lower retention reduces the insurer’s net retained claims and improves financial stability, as reflected in a decline in the loss ratio from 6.75% under the actual retention to 6.12%, 5.95%, and 5.74%, respectively. However, lower retention also increases reinsurance premium cessions, implying a trade-off between risk reduction and cost efficiency. The findings suggest that the retention range of IDR 305,000,000 to IDR 335,000,000 provides a more balanced outcome than both the current retention and the lowest simulated retention. This study contributes to the literature by providing an empirical application of the Pentikäinen method in surplus reinsurance for unit-linked products in Indonesia and offers a more objective basis for retention policy adjustment.
creator: Karin Amelia Safitri
creator: Siti Mahsa Khalilah
creator: Niken Yulia Astuti
rights: Copyright (c) 2026 Karin Amelia Safitri http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-08
publication date: 2026-04-08
volume: 10
number: 1
starting page: 237
ending page: 249
doi: 10.30829/zero.v10i1.28535

Adaptive Continuous Parameter Optimization of ARIMA using a Hybrid GA–PSO Approach for Time Series Forecasting
type: item
description: Accurate forecasting of financial time series remains challenging due to non-stationarity, complex data patterns, and difficulties in parameter optimization within traditional models. Although ARIMA is widely used, its performance is often limited by static parameter estimation and sensitivity to evolving data structures. Existing metaheuristic-based approaches have attempted to address these issues; however, many lack adaptive mechanisms that account for varying data complexity. This study proposes a Continuous Hybrid ARIMA–Metaheuristic (GA–PSO) framework with adaptive parameter tuning guided by Model Complexity Assessment (MCA). The framework enables continuous optimization of ARIMA parameters, allowing the model to dynamically adapt to changing time-series characteristics. Empirical results demonstrate consistent improvements in forecasting performance compared to the baseline ARIMA model. For instance, in the Gold dataset (300 observations), the model achieved RMSE = 56.96, MAE = 41.86, and MAPE = 1.12%, indicating stable and accurate predictions. Statistical validation using the Diebold–Mariano test further confirms the significance of these improvements. The main contribution lies in the integration of adaptive GA–PSO optimization with complexity-aware tuning, which enhances both forecasting stability and responsiveness. However, the findings also indicate the presence of heteroscedasticity in several cases, suggesting that volatility dynamics are not fully captured by the current framework. This limitation highlights the need for incorporating volatility-aware models, such as ARIMA–GARCH, to better represent time-varying variance and improve forecasting robustness in future research.
creator: Fitri Andini Ritonga
creator: Sutarman Sutarman
creator: Syahriol Sitorus
rights: Copyright (c) 2026 Fitri Andini Ritonga http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-01
publication date: 2026-04-01
volume: 10
number: 1
starting page: 250
ending page: 264

Actuarial Evaluation of Additional Contributions in Early Retirement Programs Using the Spreading Gains and Losses Method
type: item
description: This study examines the actuarial and funding implications of accelerated retirement in a defined benefit pension scheme by integrating the Projected Unit Credit (PUC) method with the Spreading Gains and Losses approach. While both methods are widely applied in pension valuation, limited empirical evidence evaluates their combined implementation under retirement age acceleration scenarios, particularly in Indonesian public sector schemes. This study addresses that gap using secondary administrative employment data of 87 female civil servants obtained from the Investment and One-Stop Integrated Services Office of Lampung Province (Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu Provinsi Lampung), grouped into four entry-age cohorts (22–25 years). The analysis compares normal retirement at age 58 with accelerated retirement at age 50, assuming a 5% annual effective interest rate and 8% biennial salary growth. The results indicate that, at valuation age 45, actuarial liabilities increase by approximately 49.8% under retirement at age 50 relative to age 58. The shorter discounting period and earlier benefit payments outweigh the reduced contribution period, resulting in the emergence of Unfunded Actuarial Liability (UAL). The resulting Past Service Liability (PSL) is amortized over five years, requiring additional contributions ranging from IDR 27.06 million to IDR 82.05 million across entry-age groups. These findings highlight the high sensitivity of pension funding to retirement age assumptions and emphasize the importance of actuarial impact assessments prior to policy implementation. However, the deterministic framework and relatively small sample size limit broader generalization of the results.
creator: Dwi Mahrani
creator: Miftha Ulya Nazima
creator: Ayu Sofia
creator: Tiara Yulita
rights: Copyright (c) 2026 Dwi Mahrani, Miftha Ulya Nazima, Ayu Sofia, Tiara Yulita http://creativecommons.org/licenses/by-sa/4.0
license: http://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-05
publication date: 2026-04-05
volume: 10
number: 1
starting page: 265
ending page: 277
doi: 10.30829/zero.v10i1.28726