ZERO: Jurnal Sains, Matematika dan Terapan
Blind-Reviewed Academic Journal ZERO: Jurnal Sains, Matematika dan Terapan Publishing scholarly works in mathematics, statistics, computer science, information systems, applied sciences, mathematical modeling, data science, and interdisciplinary applications ZERO: Jurnal Sains, Matematika dan Terapan is a blind-reviewed academic journal established in 2017 under the Universitas Islam Negeri Sumatera Utara Medan. The journal publishes high-quality scholarly articles in mathematics, statistics, computer science, information systems, and related applications. Submit Manuscript Current Issue Author Guidelines Indexed Journal Information ISSN 2580-5754 Online | 2580-569X Print DOI Prefix 10.30829 by Crossref Publisher Department of Mathematics, Universitas Islam Negeri Sumatera Utara Medan Contact WA: +62 852-7000-9767 Abstracting and Indexing ZERO: Jurnal Sains, Matematika dan Terapan is indexed in several academic databases and indexing platforms, supporting the visibility and accessibility of published articles. SINTA 2 DOAJ Garuda Google Scholar
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Cheating Detection and Identification in Shamir Secret Sharing Scheme using Parity-Based Verification
Shamir's (k, n)-threshold secret sharing scheme provides information-theoretic secrecy against unauthorized subsets. However, it does not verify whether submitted shares are genuine during reconstruction. This study proposes a parity-based modification of Shamir's scheme. In the proposed construction, each share is extended into a triplet over Zp (the integers modulo p). The additional component ci serves as an authenticated verification parameter bound to the participant identity and share value. Under the authenticated parity model, the proposed scheme detects and identifies forged shares before reconstruction. Once a forged share is identified, its original value can be restored locally from the authenticated parity value. The cheating success probability is bounded by 1/p. The construction also attains the Ogata-Kurosawa-Stinson lower bound on share size and preserves the threshold reconstruction property. The secret is embedded as the leading coefficient ak-1 and recovered using a recursive divided-difference formulation, requiring O(k2) field operations and O(k) memory after verification. Runtime evaluation over a 256-bit prime field shows that reconstruction remains practical for large reconstruction sets. The additional parity component yields information rate rho = 1/2. It also requires only one extra field element per participant. A blockchain wallet key-distribution case study is included to illustrate how authenticated parity values can support share verification and correction in institutional key recovery. Compared with prior cheating-detection schemes, the proposed construction achieves an OKS-bound cheating probability within a Shamir-based framework. It also supports recursive coefficient recovery and post-identification share correction.
Copyright (c) 2026 Mirza Farhan Azhari, Sugi Guritman, Jaharuddin Jaharuddin, Teduh Wulandari Mas'oed http://creativecommons.org/licenses/by-sa/4.0
Multi-State Markov Net Premium Valuation for Chronic Illness Riders with Care Benefits in Indonesia
This study develops a net premium valuation model for chronic illness riders with care benefits in Indonesia using a discrete-time multi-state Markov framework. Indonesian 2023 prevalence, mortality, and population data are used as inputs. Age-group prevalence of stroke, chronic kidney disease, and hypertension is interpolated to single ages and used to derive model-implied transition probabilities under Markov assumptions. These probabilities are combined with a population-weighted unisex mortality basis to calculate net premiums under the actuarial equivalence principle. Results show that the probability of remaining healthy decreases with age, while illness and death probabilities increase. Hypertension gives the largest contribution to illness transitions. For entry ages 35-65, the annual net premium rises from IDR 1.62 million to a peak of IDR 3.25 million at age 61, then declines slightly. The model provides a population-level basis for diagnosis-based chronic illness rider pricing in settings without longitudinal incidence data or claims histories.
Copyright (c) 2026 Irma Fauziah, Hamida Hamida, Suma Inna Suma Inna http://creativecommons.org/licenses/by-sa/4.0
Modeling Competing Risks in Leukemia Patients: A Survival Analysis using the Cause-Specific Hazard Approach
Leukemia is a hematological malignancy with high mortality, where deaths may arise from leukemia or other causes, leading to competing risks. This study aims to model event-specific mortality by integrating the Extended Cox model with time-dependent covariates into the Cause-Specific Hazard (CSH) framework. Secondary data from 130 leukemia patients at RSUD dr. H. Koesnadi Bondowoso (2022–2024) were analyzed, with survival time as the response and two competing events. Parameter estimation was conducted using maximum partial likelihood within a competing risks structure, optimized via Newton–Raphson iteration. The results show that chronic disease significantly increases the hazard of leukemia-related death (HR = 4.60; 95% CI: 1,46-14,44), while sex (HR = 0.30; 95% CI: 0,12-0,73) and body mass index (HR = 0.82; 95% CI: 0,73-0,91) significantly influence mortality due to other causes. These findings demonstrate that the CSH framework provides more accurate and clinically interpretable estimates by distinguishing event-specific risks. This approach supports improved risk stratification and evidence-based clinical decision-making in leukemia management.
Copyright (c) 2026 Mohamad Fatekurohman http://creativecommons.org/licenses/by-sa/4.0
Adaptive Portfolio Optimization Using MVF with Machine Learning Forecasting and Regime Switching: Evidence from LQ45 Stocks
This study proposes an adaptive portfolio optimization framework that integrates Random Forest(RF)-based return forecasting into a Mean-Variance-Forecast Error (MVF) model, augmented by a Hidden Markov Model (HMM) for market regime identification. Using weekly historical return data from 40 LQ45-listed stocks spanning January 2014 to January 2025, the framework dynamically adjusts portfolio allocations in response to bull and bear market conditions detected by a two-state HMM. The primary methodological contribution lies in addressing the static limitation of conventional MVF under shifting market regimes. Out-of-sample evaluation over a 138-week test period demonstrates that regime-switching MVF achieves Sharpe ratios above 1.30, substantially lower maximum drawdowns than the MVF-only portfolio, and cumulative returns of 291.96%. Bootstrap-validated 95% confidence intervals confirm the statistical robustness of these improvements. Nevertheless, portfolio turnover remains high during active reallocations. These findings indicate that combining machine-learning-based predictive modelling with adaptive, regime-driven allocation enhances portfolio stability, mitigates extreme losses, and improves risk-return efficiency under dynamic emerging-market conditions.
Copyright (c) 2026 Fadly Ramdhani http://creativecommons.org/licenses/by-sa/4.0
Five-Species Classification of Granivorous Bird Pests in Rice Fields using EfficientNet-B0 Transfer Learning
Bird pests significantly threaten rice production in Indonesia, particularly during the generative stage, yet most existing studies focus on general bird detection without distinguishing pest species. This study developed a species-aware classification model to identify five classes (four granivorous pest species and one non-pest class) in rice-field environments. A lightweight convolutional neural network based on EfficientNet-B0 with transfer learning was trained on 2,999 granivorous and 635 non-pest images, which were filtered through duplicate removal, quality screening, and class balancing to produce a curated dataset of 2,112 images. The dataset was split into 70% training and 30% validation sets, and five-fold cross-validation was conducted within the training subset during model development to assess robustness. The model achieved a validation accuracy of 83.38%, with an average cross-validation accuracy of 84.7% ± 1.52% and a macro-average AUC of 0.9724, indicating strong class discrimination. Most misclassifications occurred among morphologically similar Lonchura species rather than random class confusion. An additional 18 in situ field images were used for preliminary external evaluation under natural field conditions, where prediction confidence decreased due to environmental complexity. Overall, the results demonstrate that a lightweight EfficientNet-B0 backbone can effectively distinguish visually similar granivorous pest species from non-pest birds while maintaining computational efficiency suitable for precision agriculture.
Copyright (c) 2026 Rahmalia Syahputri http://creativecommons.org/licenses/by-sa/4.0
A Comparison of Generalized Pareto and Weibull Distributions for Modeling Megathrust Seismic Hazard in Indonesia
Mapping extreme earthquake risks in Indonesia's megathrust zones is critical for disaster mitigation. This study compares the Generalized Pareto (GPD) and Weibull distributions in modeling extreme earthquake magnitude probabilities. We analyzed 74,514 events (Mw>5.5) across 16 megathrust segments from 1973 to 2024. This data representing a comprehensive large-scale analysis at the national level. To ensure statistical independence of observations, a declustering algorithm was applied to isolate mainshocks. Parameters were estimated using Maximum Likelihood Estimation (MLE) via Nelder-Mead optimization, and evaluated using PDF/CDF plots, AIC, and Cramer-von Mises statistics. Results indicate a significant disparity: the Weibull distribution failed to fit any of the segments, whereas the GPD proved suitable for all 16. Consistently lower AIC and test statistics confirm the GPD's superior accuracy in representing extreme magnitude patterns. Furthermore, return periods and corresponding return levels were calculated to explicitly quantify future extreme seismic hazards.
Copyright (c) 2026 Moch Taufik Hakiki http://creativecommons.org/licenses/by-sa/4.0
Exact Formulas for the Energy, Laplacian Energy, and Signless Laplacian Energy of Line Graphs of Lintang Graphs
This study establishes exact formulas for the energy, Laplacian energy, and signless Laplacian energy of the lintang graph Ln and its line graph L(Ln). An algebraic spectral framework is employed to construct the associated matrices and determine their eigenvalues explicitly. In contrast to general results on line graph energies introduced by Ivan Gutman and subsequent studies, this work focuses on a specific two-hub graph structure for which explicit spectral formulas have not been previously reported. The results show that E(Ln) = 2 sqrt(2n), LE(L1) = QE(L1) = 10 sqrt(3), LE(Ln) = QE(Ln) = 4(n^2 - n + 2)/(n + 2) for n >= 2, and E(L(Ln)) = LE(L(Ln)) = QE(L(Ln)) = 4n - 4. These findings reveal a transition from sublinear to linear growth under the line graph transformation. The results contribute to spectral graph theory and are relevant to chemical graph theory and network analysis.
Copyright (c) 2026 Fransiskus Fran, Meliana Pasaribu, Helmi Helmi, Raventino Raventino http://creativecommons.org/licenses/by-sa/4.0
Clean Water Pipeline Network Optimization using Minimum Spanning Tree Algorithms in Argopuro Housing, Jember
Infrastructure development requires cost-efficient planning to ensure sustainable urban services. This study optimized the clean water pipeline network in Argopuro Housing, Jember, using Minimum Spanning Tree algorithms. The system was modeled as a weighted undirected graph representing junctions and pipe segments. Four algorithms Prim’s, Kruskal’s, Sollin’s, and Borůvka’s were applied to determine the optimal configuration and evaluate computational performance. All algorithms produced the same optimal pipeline length of approximately 31.00 km, representing a 40.24% reduction from the initial network. Execution time differed, with Prim’s algorithm being the fastest (0.0008 s), followed by Kruskal’s (0.0012 s), Borůvka’s (0.0067 s), and Sollin’s (0.2476 s). These results demonstrate that MST-based optimization effectively improves network efficiency. However, the study was limited to a single dataset and pipeline length as the main criterion. Future research should incorporate additional constraints and larger network scenarios for broader applicability.
Copyright (c) 2026 Agustina Pradjaningsih, Audy Putri Lestari, Firda Fadri, Apriani Soepardi http://creativecommons.org/licenses/by-sa/4.0
IDX30 Portfolio Construction using K-Means Clustering with MAD Risk Optimization and Sortino Ratio Evaluation
A stock portfolio plays an important role in managing risk and achieving optimal returns in volatile markets. This study proposes an integrated framework that combines K-Means Clustering, Mean Absolute Deviation (MAD), and the Sortino Ratio. The main contribution lies in linking clustering-based asset selection with downside risk optimization and evaluation, enabling portfolio construction that accounts for asset similarity, risk measurement, and investor-oriented performance assessment. This approach addresses the limitation of previous studies that apply these methods separately by providing a more structured basis for downside risk-adjusted portfolio selection. Using daily IDX30 stock data from June 2024 to June 2025, samples were selected based on index consistency. The results indicate that a portfolio of ANTM and INDF achieved the highest Sortino Ratio of 0.2179. These findings suggest that combining high-return stocks with moderate volatility, supported by clustering and downside risk optimization, can improve downside risk-adjusted performance, providing practical guidance for investors.
Copyright (c) 2026 Rifki Pebriyandi, Evy Sulistianingsih, Hendra Perdana, Wirda Andani, Shantika Martha http://creativecommons.org/licenses/by-sa/4.0
Psychometric Validation of a Multidimensional Learning Readiness Instrument Following Indonesia’s Free Nutritious Meal Program
This study aimed to validate a multidimensional learning readiness instrument within the context of Indonesia's Free Nutritious Meal Program (MBG) using Confirmatory Factor Analysis (CFA). The instrument measured five dimensions: School Participation, Learning Concentration, Learning Energy, Psychological Readiness, and Academic Readiness. Data were collected from 366 students and analyzed using the Robust Maximum Likelihood estimator in R. One indicator (A3) was removed due to low factor loading, resulting in a revised 19-indicator measurement model. The final CFA model demonstrated acceptable goodness-of-fit indices (chi-square = 294.356, df = 142, chi-square/df = 2.07, CFI = 0.944, TLI = 0.933, RMSEA = 0.054, SRMR = 0.042). Convergent validity, discriminant validity, and composite reliability generally supported the psychometric adequacy of the instrument. The findings suggest that the proposed instrument may serve as a useful multidimensional measurement tool for evaluating students' learning readiness within school feeding contexts.
Copyright (c) 2026 Darwis Darwis, Sudirman Sudirman, A Rahim http://creativecommons.org/licenses/by-sa/4.0
Sector-Adjusted R-vine Copula (RVMS) Extension of CAPM for Portfolio Risk Optimization
This study evaluated the R-vine Market Sector (RVMS) model, a sector-adjusted extension of the Capital Asset Pricing Model (CAPM), for portfolio risk optimization in the Indonesian stock market using 978 daily observations from 2021 to 2025. Returns were modeled using ARMA–GARCH and R-vine copulas, and portfolios were optimized under a minimum tail-risk criterion with rolling-window backtesting. The results indicated asymmetric and tail dependence, with sectoral effects contributing substantially to portfolio risk. RVMS reduced expected tail losses by approximately 12–16% relative to CAPM at standard confidence levels, although both models showed limited performance under extreme tail conditions. Economically, RVMS provided modest improvements in risk-adjusted performance and lower drawdowns, despite higher turnover. Overall, incorporating sectoral dependence improved portfolio risk modeling, although the benefits remained moderate and context-dependent.
Copyright (c) 2026 Wa Ode Intan Fully Nadya, Retno Budiarti, I Gusti Putu Purnaba http://creativecommons.org/licenses/by-sa/4.0
Clustering Indonesian Traditional Foods by Nutritional Profiles using the K-Means Algorithm for Health Policy
Indonesia has a wide variety of traditional foods; however, systematic mapping of their nutritional composition remains limited. This study aims to cluster Indonesian traditional foods based on nutritional profiles using the K-Means algorithm to support health policy development. The analysis focuses on calories, protein, fat, and carbohydrates. A quantitative approach was applied, including data selection, normalization, and determination of optimal number of clusters using the Elbow Method. The results show that four clusters (k = 4) were obtained. Cluster 3 contains foods high in calories and protein, Cluster 2 is dominated by carbohydrates, Cluster 0 shows moderate nutritional values, and Cluster 1 represents low-energy foods. Clustering quality was evaluated using the Silhouette Coefficient (0.45) and Davies–Bouldin Index (0.94), indicating moderate and acceptable clustering performance. PCA visualization retained 88.77% of total data variance. Clusters inform policy via dietary grouping, guiding interventions; limited to macronutrients, excluding micronutrients and portion variability.
Copyright (c) 2026 Irene Devi Damayanti http://creativecommons.org/licenses/by-sa/4.0
Numerical Simulation of Tidal Hydrodynamics in the Mahakam Estuary using MacCormack and Lax-Wendroff Schemes
Tidal hydrodynamics in the Mahakam Estuary are influenced by intricate tidal propagation and river–estuary interactions, necessitating precise numerical modeling. This research analyzed the MacCormack and Lax–Wendroff methods for addressing the one-dimensional Saint–Venant equations influenced by semi-diurnal tides, with an average depth of 10 m and a river flow rate of 3000 m³/s. Simulations were performed with uniform discretization under chosen CFL conditions and confirmed against analytical tidal propagation traits using RMSE, MAE, correlation coefficient, and wave amplitude retention. Both models effectively replicated flood–ebb cycles and tidal damping with similar precision. The MacCormack scheme generated more refined solutions with reduced numerical oscillations, while the Lax–Wendroff scheme maintained sharper gradients but exhibited slight dispersive errors. In general, both methods proved to be dependable and numerically consistent for tidal simulations in the Mahakam Estuary.
Copyright (c) 2026 Hadi Hermansyah, Emil Azmanajaya, Indriasri Raming, Faisal Faisal, Fahruddin Fahruddin http://creativecommons.org/licenses/by-sa/4.0
Development of a Tourism Awareness Index and Its Determinants among Tourism Workers
Tourism worker awareness is essential for service quality and sustainable tourism development, yet a validated composite measure remains lacking. This study develops the Tourism Awareness Index (TAI) based on the Sapta Pesona framework using survey data from 868 tourism workers in Malang Regency, Indonesia. Six dimensions showed satisfactory internal consistency (Cronbach's alpha = 0.65-0.81), while one dimension had alpha slightly above 0.60. Factor analysis with principal component extraction identified a three-factor structure explaining 74.2% of total variance, supported by a KMO value of 0.91 and a significant Bartlett's test. Ordered logistic regression revealed that workers aged 45 years and above (OR = 1.42, p = 0.042) and those with at least a high school education (OR = 1.60, p = 0.004) were more likely to achieve higher TAI levels, while lack of foreign language proficiency reduced this likelihood (OR = 0.57, p = 0.003). The TAI offers a statistically validated tool and may support workforce development and tourism policy.
Copyright (c) 2026 Sarni Maniar Berliana, Krismanti Tri Wahyuni, Victoria Pena Valenzuela, Joshua Meneses, Wahyuni Andriana Sofa, Maria A. Hasiholan Siallagan, Ditto Satrio Wicaksono http://creativecommons.org/licenses/by-sa/4.0
Dual-Fairness Nurse Scheduling via the Double Direct Progressive Filling Algorithm under Qualification and Contract Constraints
Nurse scheduling requires balancing workload distribution while satisfying qualification and employment contract constraints. This study implements a hybrid scheduling framework integrating Goal Programming (GP), the Double Direct Progressive Filling Algorithm (DDPFA), and the CP-SAT solver to generate feasible nurse schedules under actual and workforce-reduction scenarios in inpatient and emergency departments. Performance is evaluated using four indicators: inter-shift fairness, inter-nurse fairness, soft-constraint compliance, and computation time. The results show that the proposed approach achieves lower standard deviation values (0.15–0.42), satisfies all soft constraints, and generates feasible schedules in under 3 seconds. Compared with the evaluated manual scheduling and goal programming approaches, the framework produced more balanced workload allocation across shifts and nurses under the evaluated scenarios. These findings suggest that the proposed framework may provide a practical approach for fairness-oriented and cost aware workforce planning under the evaluated hospital conditions.
Copyright (c) 2026 Tita Putri Redytadevi, Toni Bakhtiar, Jaharuddin Jaharuddin http://creativecommons.org/licenses/by-sa/4.0
Delay Tolerance and Recovery Control in a Synchronized Electric Bus Network using Max-Plus Algebra
This study analyzed delay tolerance, normalization time, and recovery control in the two-terminal Trans Gadjah Mada Electric Bus network using max-plus algebra. An analytical-computational approach was applied by representing the nominal route times as a weighted directed graph and transforming them into a max-plus linear system. The max-plus eigenvalue, critical arcs, component-wise delay thresholds, excess delays, and recovery horizons were then computed. The nominal synchronized period was 21 minutes. The Terminal 1 and Terminal 2 local routes had delay thresholds of 2 and 3 minutes, respectively, whereas both interterminal corridors were critical arcs with zero delay tolerance. Thus, any positive delay on a critical corridor directly disturbed synchronization. Service-operational delays changed route weights, while departure-time delays shifted terminal event times. The resulting threshold and excess-delay measures provided the mathematical basis for a simple recovery-mode switching rule, whose parameters remain illustrative and require empirical calibration before operational deployment.
Copyright (c) 2026 Marcellinus Andy Rudhito, Dewa Putu Wiadnyana Putra http://creativecommons.org/licenses/by-sa/4.0
Modelling Leptospirosis Transmission with Waning Immunity and Stage-Structured Infection: Local stability and Vaccination Analysis
Leptospirosis remains a complex zoonotic threat. This study develops a stage-structured mathematical model of leptospirosis transmission incorporating vaccine-induced immunity and two-stage treatment. The basic reproduction number , derived via the Next-Generation Matrix method, serves as a threshold where indicates disease elimination, whereas indicates persistence, with equilibrium stability analyzed using the Routh-Hurwitz criterion. To evaluate controls, we explicitly contrast two parameter regimes: a disease-free scenario under high interventions and reduced contact rates resulting in , and an endemic scenario with low interventions and a high contact rate yielding Vaccination and awareness alone cannot eliminate the disease; reducing human-vector contact rates through environmental control is strictly required. Combining 80% vaccination and 40% awareness with these environmental measures reduces transmission potential by 88%, providing quantifiable public health policies.
Copyright (c) 2026 Dinta Ardelia Wahyudi http://creativecommons.org/licenses/by-sa/4.0
Measuring Tail Risk in the Farmer Price Index Difference using POT-EVT
Farmer welfare suffers when the Farmer Price Received Index does not keep up with the Farmer Price Paid Index, and common ratio-based measures such as the Farmers' Exchange Rate do not capture sharp drops well. This study measures the extreme downside risk of the Index Difference (ID), the difference between the two indices. Because agricultural price data are heavy-tailed and not normally distributed, a Peaks-over-Threshold (POT) model based on the Generalized Pareto Distribution is fitted to the loss variable Loss = -ID, using monthly Indonesian data from January 2006 to December 2023 (216 observations). Risk is measured by Value at Risk (VaR) and Expected Shortfall (ES). At the 95th-percentile threshold, 11 exceedances give a slightly negative, near-zero shape parameter, and the 99% VaR and ES for ID are about -6.44 and -6.81. Applying POT-EVT to a farmer welfare indicator is a new contribution, and the estimates are exploratory, intended to illustrate the potential of ID-based early warning and insurance-trigger design rather than to support immediate application. With only 11 exceedances, however, these uses remain suggested rather than demonstrated, and the tail estimates should be read with caution.
Copyright (c) 2026 Silvina Rosita Yulianti http://creativecommons.org/licenses/by-sa/4.0
Evaluating the Success of Bank Jago Digital Banking Application using an Integrated DeLone and McLean and TAM Model
The rapid growth of digital banking in Indonesia has increased the need to evaluate the success of digital banking applications from both information system and user acceptance perspectives. Despite its widespread adoption, Bank Jago users continue to report issues related to system performance and service quality. This study evaluates the success of the Bank Jago application by integrating the DeLone and McLean Information Systems Success Model with the Technology Acceptance Model (TAM). A quantitative survey of 100 Bank Jago users in Pekanbaru, recruited through non-probability accidental sampling, was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4; this geographic and sampling scope bounds the generalizability of the findings. Seven of the nine hypotheses were supported; Information Quality did not significantly affect User Satisfaction, and Perceived Usefulness did not significantly affect Behavioral Intention to Use. The model explained a substantial proportion of variance in User Satisfaction (R2 = 0.752), and the strongest reported standardized relationship was User Satisfaction on Net Benefit (Beta = 0.747), which is a single-predictor path and should be interpreted with that structural prominence in mind. These findings support the integrated DeLone and McLean and TAM framework in the digital banking context and indicate that Bank Jago should prioritize system quality, service quality, and application usability to enhance user satisfaction and the benefits perceived by users.
Copyright (c) 2026 Dessy Elvyana, Zarnelly Zarnelly, Tengku Khairil Ahsyar, Nesdi Evrilyan Rozanda, Angraini Angraini http://creativecommons.org/licenses/by-sa/4.0
Analysis of Online Marketplace User Satisfaction using SERVQUAL, Quality Function Deployment, and Mixed Integer Linear Programming
This study analyzes and optimizes user satisfaction in online marketplaces by integrating SERVQUAL, the Kano Model, Quality Function Deployment (QFD), and Mixed Integer Linear Programming (MILP). Data were collected from 167 Shopee users, with 107 valid responses analyzed. SERVQUAL measured service quality dimensions, the Kano Model derived satisfaction and dissatisfaction parameters, QFD translated user requirements into internal service measures, and MILP selected optimal improvement alternatives under an IDR 90,000,000 budget constraint. The results show that assurance had the highest SERVQUAL weight (0.207), followed by tangibility (0.201), reliability (0.200), empathy (0.198), and responsiveness (0.195). The Pearson correlation-based HoQ approach selected customer service training, automatic delivery update features, and feedback completion incentives, with a total cost of IDR 85,000,000 and a deviation value of 0.191. These findings provide scenario-based decision support to improve the quality of marketplace services.
Copyright (c) 2026 Nayla Nur Alifah http://creativecommons.org/licenses/by-sa/4.0
A Mathematical Model for Conservation Strategies of Rare Plants in North Sumatra
This study develops a nonlinear mathematical model for the conservation of rare plant populations in North Sumatra by incorporating interactions among rare plants, human populations, population pressure, and fire- and toxicant-control activities. The model is formulated as a system of four nonlinear ordinary differential equations and analyzed using positivity, boundedness, equilibrium, and stability theory. The analysis establishes four equilibrium points, including a biologically relevant coexistence equilibrium. Conditions for the existence of this equilibrium are derived, while its local and global asymptotic stability are established using the Routh–Hurwitz criterion and Lyapunov stability theory, respectively. Numerical simulations using illustrative parameter values are performed to support the analytical results and demonstrate the qualitative behavior of the system. The results provide analytical conditions under which rare plant populations can persist despite human pressure and environmental disturbances. This study is theoretical in nature and is not calibrated using ecological field data; therefore, the numerical results are intended to illustrate model dynamics rather than provide site-specific conservation predictions. The proposed framework contributes to the mathematical understanding of conservation dynamics and may serve as a foundation for future data-driven conservation studies.
Copyright (c) 2026 Notalia Pardamean Sihombing, Esther Sorta Mauli Nababan, Sutarman Sutarman http://creativecommons.org/licenses/by-sa/4.0
Rule-Based IoT Decision Support System for Real-Time Chili Plant Growth Monitoring
This study proposes a rule-based IoT decision support system for real-time chili plant growth monitoring. Its main contribution is an integrated framework for chili cultivation that combines plant growth indicators and environmental sensor data within a single transparent IF-THEN rule structure an integration not commonly addressed in prior chili-monitoring systems. The system integrates eight parameters stem height, branches, leaves, fruits, soil moisture, temperature, water volume, and soil pH collected from 100 plant samples and 704 repeated environmental sensor records over a single 14-week growing season. Classification is formalized as a maximum-severity decision over a knowledge base of eight rules: each rule carries a severity level, and a record receives the label of the most severe triggered rule (Less Optimal > Fairly Good > Optimal). Thresholds were set through a two-step calibration that combines established agronomic ranges for Capsicum annuum with empirical refinement against observed plant responses. The logic was validated against 100 expert-labeled records using a confusion matrix, achieving 96% overall accuracy (95% CI: 90.2–98.4%), a macro-average F1-score of 93.4%, and per-class F1-scores between 88.9% and 98.0%. Classification results show that 75% of records fell into the Less Optimal category, driven primarily by elevated temperature (mean 34.09°C) and acidic soil pH (mean 5.38). The system delivers interpretable, actionable decision support without complex machine learning infrastructure; its main limitation is that thresholds were calibrated on a single site and season, so external multi-site validation is required before broader deployment.
Copyright (c) 2026 Ertina Sabarita Barus http://creativecommons.org/licenses/by-sa/4.0
Interregional Poverty Linkages and Human-Capital Correlates in the Special Region of Yogyakarta: Evidence from a Spatial Panel Model
Poverty remains unevenly distributed across the Special Region of Yogyakarta (DI Yogyakarta), suggesting that local conditions and interregional linkages may shape poverty outcomes. This study examined poverty rates across five regencies/city administrative units in DI Yogyakarta during 2021-2025 while accounting for spatial dependence. A balanced panel was analyzed by comparing common, fixed, and random effects models, followed by spatial diagnostics and estimation of a spatial autoregressive fixed-effects model. The average poverty rate declined from 13.28% in 2021 to 10.61% in 2025. Under model-based inference, the spatial lag coefficient was positive (rho = 0.629), indicating spatial association in poverty rates across neighbouring regions. The spatial impact decomposition showed that expected years of schooling had negative direct, indirect, and total effects on poverty, whereas the effects of mean years of schooling, literacy rate, life expectancy at birth, and stunting prevalence were weaker. The findings are interpreted as exploratory, context-specific associations.
Copyright (c) 2026 safaat yulianto, atika nurani ambarwati, virgania sari http://creativecommons.org/licenses/by-sa/4.0
A Comparative study of Average-Based FTSMC, GBM, and LSTM for JCI Forecasting
Stocks are high-risk investment instruments, so a model is needed to assist investors in decision-making. The Jakarta Composite Index (JCI) is a key indicator that measures the performance of all stocks on the Indonesian stock exchange. This study compares the performance of the Average-Based FTSMC, GBM, and LSTM models in predicting JCI movements. The research data consists of daily stock index values from June 1, 2024, to June 29, 2026. The models were compared using the evaluation metrics MAPE, RMSE, and MAE. The results show that the Average-Based FTSMC model provides the highest accuracy (MAPE: 0.925%, RMSE: 86.753, and MAE: 65.162). These findings indicate that the fuzzy time series approach is more adaptive to JCI fluctuations than both classical stochastic models and deep learning models. This study is limited to historical data using a single input variable; therefore, generalizing the results to market conditions requires further investigation.
Copyright (c) 2026 Syifa Aulia http://creativecommons.org/licenses/by-sa/4.0
Hybrid Fuzzy Logic and Geometric Analysis Model to Assess Land-use Conversion Governance in North Sumatra
Land-use conversion in North Sumatra has increased significantly due to urban expansion, infrastructure development, and economic growth, creating challenges for sustainable land governance. This study proposes a Hybrid Fuzzy Logic and Geometric Analysis Model that evaluates governance effectiveness and spatial transformation within a single, consistently defined framework, and applies it as a case study to Hamparan Perak District. Five governance indicators were assessed using a Mamdani Fuzzy Inference System spatial planning compliance, policy implementation effectiveness, institutional coordination, environmental sustainability, and regulatory enforcement intensity while land conversion and its spatial pattern were measured separately on the geometric side so that no variable is counted in both components. The fuzzy evaluation produced a governance-effectiveness score of 0.63, categorised as moderately effective, with a 95% bootstrap confidence interval of 0.59–0.67 and close agreement with expert reference scores (RMSE = 0.046, r = 0.91). Geometric analysis showed the Spatial Distribution Index rising from 0.48 in 2019 to 0.72 in 2024, indicating clustered conversion around transportation corridors and urban growth centres. Governance and spatial components were combined using weights of 0.60 and 0.40 derived through the Analytic Hierarchy Process, yielding a Hybrid Governance Index of 0.65 that reproduces cleanly from the reported inputs. For this study area, the results suggest that land-use conversion reflects both urban development pressure and governance capacity; wider validation would be needed before generalising beyond the case. The model provides a reproducible decision-support framework for sustainable land-use planning.
Copyright (c) 2026 Ruth Riah Ate http://creativecommons.org/licenses/by-sa/4.0
Hydraulic Diagnosis of Urban Flood Mechanisms using HEC-RAS: A Case Study of the Bontang River, Indonesia
Flooding along the Bontang River in Gunung Telihan Village, Bontang City, recurs almost annually, yet its causes had not been diagnosed mechanistically. This study applied one dimensional steady-flow HEC-RAS 4.1 modeling to a 1,936 m reach, using primary topographic data and design discharges (Q2, Q10, Q20) from the Nakayasu Synthetic Unit Hydrograph. Unlike prior single mechanism studies, the analysis resolved four co occurring flood mechanisms: bank-asymmetry overflow, right bank levee overflow, undersized sluice-gate backwater, and a downstream constriction. Although nominal channel capacity (13.83 m3/s) marginally exceeded Q2 (11.88 m3/s), the low right bank reduced the effective overflow threshold below Q2. A 95% confidence interval on design discharge, computed using Kite's asymptotic method, indicated the downstream constriction could approach overflow at the upper bound of Q10 uncertainty. An independent tidal sensitivity check showed the tidal range effect at the confluence (2.2 m) exceeded the sluice-gate backwater step alone (0.42-0.68 m). A planned condition simulation combining retention ponds, levee reinforcement, and gate augmentation achieved safe status at all critical points, indicating site differentiated intervention is a promising, land feasible direction pending detailed design verification.
Copyright (c) 2026 Viva Oktaviani http://creativecommons.org/licenses/by-sa/4.0
Comparing Volatility and Neural Network Models for Forecasting Bitcoin Prices in Indonesian Rupiah
As Indonesia emerges among Southeast Asia's largest cryptocurrency markets, forecasting Bitcoin priced in rupiah (BTC/IDR) has gained practical relevance, yet most research focuses on BTC/USD and overlooks domestic macroeconomic conditions. This study tests whether augmenting an Exponential GARCH with Exogenous Variables (eGARCH-X) model with a Nonlinear Autoregressive network with eXogenous inputs (NARX) improves forecasts of weekly BTC/IDR log returns, using IHSG, USD/IDR, gold price, and BI rate as exogenous inputs. Using 437 weekly observations from January 2018 to June 2026, the hybrid was benchmarked against eGARCH-X, NARX, and a restricted eGARCH without exogenous terms across three splits, evaluated with RMSE, MAE, directional accuracy, and the Diebold­­­­ Mariano test, with each network comparison replicated over ten random initializations. The eGARCH identified a well-determined conditional variance process: volatility was highly persistent (0.976), responded asymmetrically to the sign of innovations (0.038, p = 0.010), and displayed heavy tails, with standardized residuals passing all diagnostics. No model differed significantly from the eGARCH-X benchmark, directional accuracy was indistinguishable from chance throughout (43.7–55.5%), and the exogenous regressors were insignificant both inSeaksample and out-of-sample. Weekly BTC/IDR returns thus appear tractable in their variance but close to unforecastable in their conditional mean, locating the practical value of these models in volatility estimation rather than directional prediction.
Copyright (c) 2026 Raihan Akbar, I Wayan Mangku, Bib Paruhum Silalahi http://creativecommons.org/licenses/by-sa/4.0
Comparative Analysis of Classical and Time-Varying Lee–Carter Models for Mortality Forecasting in Indonesia and Japan
Reliable mortality forecasting is essential for demographic planning and actuarial decision-making. This study compared the forecasting performance of the Classical and Time-Varying Lee–Carter models using World Population Prospects (WPP) 2024 mortality data for Indonesia and Japan (1970–2023). Both models were estimated using 1970–2015 data and evaluated on 2016–2023 through out-of-sample validation based on MAE, RMSE, and MAPE. For Indonesia, the Time-Varying model yielded a slightly lower MAPE (7.262%) than the Classical model (8.086%), whereas the Classical model produced slightly lower forecasting errors for Japan. Overall, the differences between the models were modest. The findings suggest that, in this study, forecasting performance was associated more closely with population-specific mortality characteristics than with model complexity alone.
Copyright (c) 2026 syipa arista widya, Ismail Husein http://creativecommons.org/licenses/by-sa/4.0
Comparing Adaptive Kernels in Geographically and Temporally Weighted Regression for Stunting in East Nusa Tenggara, 2021–2023
Stunting remains a major public health challenge in East Nusa Tenggara, Indonesia. This study compares Gaussian, Bisquare, and Exponential adaptive kernels in Geographically and Temporally Weighted Regression (GTWR) to model spatiotemporal variation in stunting determinants. District-level panel data from 22 districts/municipalities during 2021-2023 were used, yielding 66 space-time observations. Models were evaluated using R², RMSE, spatial-temporal parameter tests, and a spatial-only GWR baseline. The Bisquare adaptive GTWR produced the strongest in-sample fit (R² = 98.58%; RMSE = 0.7647), improving over the Bisquare GWR baseline (R² = 46.98%; RMSE = 4.6703), although this high fit should be interpreted cautiously. GRDP per capita, poverty rate, and maternal education showed significant spatial-temporal variability, while the sanitation and clean water access variable was not significant (F₃ = 1.0938; p = 0.4771). GTWR may serve as an exploratory tool for identifying local stunting patterns.
Copyright (c) 2026 Danang Ariyanto, Henny Pramoedyo, Atiek Iriany, Isnani Darti http://creativecommons.org/licenses/by-sa/4.0