ZERO: Jurnal Sains, Matematika dan Terapan
Journal title : ZERO: Jurnal Sains, Matematika dan Terapan Subject : Mathematics, Statistic, Computer Sciences, & Application Language : English ISSN : 2580-5754 (online), 2580-569X (print) Publication : Triannual (three times per year) DOI : Prefix 10.30829 by Crossref Publisher : Department of Mathematics, UIN Sumatera Utara Medan Cit. Analysis : SINTA 2, DOAJ, Garuda, Google Scholar Information : 📞 +6285270009767 ✉️ zero_journal@uinsu.ac.id ZERO: Jurnal Sains, Matematika dan Terapan is a blind reviewed academic journal, established in 2017 as part of the State Islamic University of North Sumatra Medan, dedicated to the publication of scholarly articles in various Sciences, mathematics, and its applications, by which exchanges of ideas as research findings and contemporary issues are facilitated. ZERO: Jurnal Sains, Matematika dan Terapan welcomes contributions of articles in such fields as Mathematics, Statistics, Computer Science, Information System, and its applications. According to the ISSN Decree, ZERO: Jurnal Sains, Matematika dan Terapan has been published on a triannual basis.
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).
Rainfall Risk Modelling for Rice Farming Using Continuous Hidden Markov Models
Climate change has increased rainfall variability and unpredictability, significantly impacted agricultural productivity, and raised the risk of crop failure, particularly in rain-fed rice farming systems. This study models rainfall data from Tabanan, Bali, using a continuous-time Hidden Markov Model (HMM) to identify latent weather states and assess the associated risk of rice crop failure. The model assumes four hidden states, each generating rainfall observations following a Gamma distribution. Simulation results produced Mean Absolute Percentage Error (MAPE) values below 5% for training and testing sets, indicating strong model performance in replicating rainfall patterns. Risk analysis compared simulated rainfall with rice crop water requirements across three planting periods. The second planting period (July–October) exhibited the highest risk at 3.75%. Compared to other predictive models, HMM offers superior capability in capturing temporal rainfall structure and identifying critical transition phases, making it highly suitable for agricultural risk assessment and climate-adaptive planning.
Copyright (c) 2025 David Vijanarco Martal, Berlian Setiawaty, Retno Budiarti http://creativecommons.org/licenses/by-sa/4.0
Optimizing Electric Vehicle Charging Station Placement in Banyumas Using Graph Domination Theory
This study applies graph theory to optimize the placement of electric vehicle (EV) charging stations in Banyumas Regency, Indonesia, using real geospatial data from 27 sub-districts. Each sub-district is modeled as a vertex, with edges defined by a 10 km coverage radius. The domination number is employed to identify the minimum number of charging stations required to ensure full spatial coverage. Unlike prior EV infrastructure studies in Indonesia that primarily rely on demand-based heuristics or clustering methods, this research explicitly guarantees coverage through graph domination theory. To enhance robustness, the domination-based solution is compared with a Set Covering Problem solved using Ant Colony Optimization (ACO). Both approaches consistently identify six strategic locations, achieving 100% coverage while reducing infrastructure requirements by approximately 78% compared to a one-station-per-sub-district strategy. The results provide practical guidance for policymakers and urban planners by supporting cost-efficient, scalable, and equitable EV charging infrastructure deployment in regions with early-stage EV adoption.
Copyright (c) 2025 Yogo Dwi Prasetyo, Hari Sumardi http://creativecommons.org/licenses/by-sa/4.0
AMBATIG: Android Application for Generating Batik Motifs Using Frieze Symmetry Group Transformations
Indonesian batik is known for its diverse motifs, yet many artisans still design patterns manually, which limits variation and innovation. This study develops Ambatig, an Android application that helps artisans create batik motifs more efficiently. The application was implemented in Android Studio and follows a waterfall development model. Ambatig transforms a user drawn base cell into structured repetitions by composing distance preserving transformations, namely translation, vertical reflection, horizontal reflection, half turn rotation of 180 degrees, and glide reflection. These operations are configured according to the seven frieze types introduced in the study. Functional black box testing confirmed stable performance with all scenarios passing. Compared with previous motif generation approaches, Ambatig introduces a parameterized on device frieze transformation pipeline that produces all seven symmetry families from a single base motif. Real time preview and export features support creative exploration while maintaining mathematical coherence in digital batik design.
Copyright (c) 2025 Dinda Kartika, Fevi Rahmawati Suwanto, Nurul Maulida Surbakti http://creativecommons.org/licenses/by-sa/4.0
Determinants of Fishermen and Fish Farmers Exchange Rate: A Panel Data Regression Approach
This study examines the impact of key economic variables on the exchange rate of fishermen and fish farmers using monthly panel data by subsector/province for the period 2022–2024 sourced from BPS. The price index received by farmers (Y) is explicitly defined as the dependent variable. A panel data regression model was employed using three independent variables: Farmers’ Paid Price Index ( ), the Fishermen and Fish Farmers Exchange Rate ( ), and the Fishermen’s Business Exchange Rate ( ). The results showed that both and had a significant positive effect (α < 0.05) on Y, with exhibiting a stronger influence than . These findings suggest that policies aimed at controlling input prices and stabilizing exchange rates can effectively improve the welfare of fishermen and fish farmers. Furthermore, the regression model developed in this study provides a practical analytical framework for supporting data-driven policy decisions related to price dynamics and welfare enhancement in the fisheries sector.
Copyright (c) 2025 Vitri Aprilla Handayani, Sabarinsyah Sabarinsyah, Adamsyam Arrafi, Hery Sunarsono, Andini Setyo Anggraeni http://creativecommons.org/licenses/by-sa/4.0
Modeling Monthly Rainfall Data Using the Alpha Power Transformed X-Lindley Distribution in the Toba Lake Region
Modeling rainfall is crucial for hydrological studies and climate adaptation, especially in regions with complex topography such as the Toba Lake area, North Sumatra. Classical probability distributions often struggle to represent skewness, heavy tails, and variability observed in tropical rainfall. This study explores APTXL distribution as a flexible two-parameter model. Through the alpha power transformation, APTXL extends the X-Lindley distribution by introducing an additional shape parameter, allowing better accommodation of asymmetrical and extreme values while maintaining analytical tractability. Statistical properties are derived, and parameters are estimated using maximum likelihood. The model is applied to a long-term dataset from 13 meteorological stations, covering 408 monthly observations per station. Comparative analysis against Gamma, Lognormal, and Generalized Extreme Value distributions using multiple goodness-of-fit criteria indicates that APTXL provides consistently improved performance. These results suggest APTXL as a practical tool for rainfall modeling and water-resource applications in climate-sensitive regions.
Copyright (c) 2025 Mohamad Khoirun Najib, Sri Nurdiati, Elis Khatizah, Aulia Rizki Firdawanti, Hendri Irwandi, Mirza Farhan Azhari, David Vijanarco Martal, Nicholas Abisha http://creativecommons.org/licenses/by-sa/4.0
Implications of Age-Based Clustering for Survival and Relapse-Free Analysis in METABRIC Breast Cancer
Cox proportional hazards models are widely used for breast cancer survival analysis, but their validity is often limited by violations of the proportional hazard assumption. Machine learning techniques offer potential ways to improve model robustness, yet their combined use remains underexplored. This study aims to compare the proportional hazard assumptions fulfilment and the discriminatory ability of the models before and after age-based clustering. K-medoids was selected for its robustness to outliers. The results demonstrate that clustering significantly improved adherence to the proportional hazards assumption and increased the concordance index, indicating better predictive performance. Number of variables satisfying the assumption increased from 3 in the global model to 5–6 across clusters. Tumor size and positive lymph nodes consistently had a significant effect on all clusters for both survival time and relapse-free time. These findings suggest that age-based clustering can enhance the robustness and predictive performance of Cox models.
Copyright (c) 2025 Alif Azhari, Mauliddin Mauliddin http://creativecommons.org/licenses/by-sa/4.0
Ensemble Bagging Support Vector Machine–Kernel Discriminant Analysis Model for Stunting Potential Classification
Considering the maternal knowledge, economic status, and maternal nutritional status, the current study created an optimal risk assessment model to detect childhood stunting risk. At the same time, these variables are unbalanced and interrelated in non-linear fashion. Then, to these ends, an Ensemble Bagging model consisting of Support Vector Machine and Kernel Discriminant was trained by voting on the aggregation of the majority of 100 bootstrapped samples, which countered variance and overfitting reducing, hence improving generalization. The primary data were sourced from the mothers of toddlers in the Wajak District. The model predicators were 3 out of the primary ones accounting for the stunting risk. The model also recorded an accuracy of 95%, sensitivity level of 80%, as well as a 100% specificity score. Non-linear relationships were detected and the variance was also reduced, supporting the study to place itself in the realms of novelty by being the first research to fuse the Ensemble Bagging with Kernel methods for Detected stunting risk, The model, hence, fits best as a decision Support System for detecting stunting risk at an early stage.
Copyright (c) 2025 Alfiyah Hanun Nasywa, Solimun Solimun, Achmad Efendi, Adji Achmad Rinaldo Fernandes, Celia Sianipar, Fachira Haneinanda Junianto http://creativecommons.org/licenses/by-sa/4.0
Determinants of Dengue Hemorrhagic Fever in Aceh: A Panel Regression Approach
Dengue Hemorrhagic Fever (DHF) exhibits substantial variation across districts and over time in Aceh Province, making it suitable for analysis within a panel data framework. This study models district-level DHF incidence using applied econometric techniques based on non-spatial panel data regression, employing a balanced panel dataset of 23 districts/cities observed from 2020 to 2022. The Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM) are estimated and formally compared using the Chow test, Hausman test, and Lagrange Multiplier test, with results consistently indicating that the Fixed Effect Model is the most appropriate specification due to the presence of unobserved, time-invariant district-specific effects. Diagnostic testing identifies heteroskedasticity in the error structure; therefore, the selected FEM is re-estimated using White cross-section robust standard errors to ensure reliable statistical inference. Empirical results show that population density is positively and statistically significantly associated with DHF cases, while the number of health workers is negatively and significantly associated, whereas rainfall, number of hospitals, sanitation coverage, and poverty level do not exhibit statistically significant effects in the final robust specification. The selected model explains approximately 86% of the within-district variation in DHF incidence, demonstrating the importance of appropriate model specification and robust variance estimation in panel data regression applied to epidemiological outcomes, while emphasizing that the estimated relationships represent statistical associations rather than causal effects.
Copyright (c) 2025 Fitra Muliani, Fazrina Saumi, Amelia Amelia, Rizki Amalia http://creativecommons.org/licenses/by-sa/4.0
Comparative Performance of Statistical and LSTM Based Arbitrage in the Indonesian Stock Market
This study compares the performance of traditional statistical arbitrage and Long Short-Term Memory (LSTM)–based deep arbitrage strategies in generating returns and risk-adjusted performance in the Indonesian stock market. A quantitative approach is employed using long-only trading simulations on daily closing prices of blue-chip financial sector stocks listed on the Indonesia Stock Exchange from April 2015 to April 2025. Stock pairs are selected based on correlation and cointegration criteria, while spread volatility is modeled using a GARCH (1,1) framework. To ensure a genuine out-of-sample evaluation, the sample is divided into an in-sample period from April 2015 to August 2021 for model training and parameter optimization, and an out-of-sample period from September 2021 to April 2025 for performance assessment. Strategy performance is evaluated using portfolio return and Sharpe ratio. The empirical results show that both strategies are feasible in the Indonesian market; however, the LSTM-based deep arbitrage strategy significantly outperforms the traditional statistical arbitrage approach, achieving a higher out-of-sample portfolio return (735% versus 482%) and a superior Sharpe ratio (1.67 versus 0.69). These findings indicate that deep learning–based arbitrage can provide substantial improvements in both return and risk-adjusted performance under long-only trading constraints in an emerging market context.
Copyright (c) 2025 Yunita Yunita, Indwiarti Indwiarti, Deni Saepudin http://creativecommons.org/licenses/by-sa/4.0
Integrating Triple-Bottom-Line Goals and Uncertainty in Aggregate Production Planning Using Fuzzy Goal Programming
This study develops a Sustainable Aggregate Production Planning (SAPP) model based on Fuzzy Goal Programming (FGP) that integrates economic, environmental, and social objectives under uncertainty. Conventional aggregate production planning primarily focuses on cost minimization, often resulting in excessive overtime, high emissions, and workforce instability. To address these limitations, the proposed model simultaneously considers total cost, carbon emissions, energy consumption, waste generation, workforce stability, and worker satisfaction within a unified fuzzy optimization framework. From a mathematical perspective, the main contribution of this study lies in the explicit formulation of a max–min FGP structure using aspiration-based linear membership functions for all sustainability objectives, enabling a balanced compromise solution without relying on deviation-variable-based goal programming commonly adopted in existing SAPP models. The resulting formulation is a linear mixed-integer optimization model that preserves tractability while accommodating conflicting sustainability goals. Numerical experiments are conducted using illustrative demand and operational data adapted from a reference study, solely for mathematical calibration and validation of the proposed model rather than empirical inference. The results indicate a global satisfaction level of λ = 0.67, representing a balanced max–min compromise among economic, environmental, and social objectives. Compared to the baseline scenario, the optimized plan achieves notable improvements in cost efficiency and waste reduction while keeping emissions, energy consumption, and workforce-related indicators within predefined fuzzy tolerance limits. Overall, the proposed SAPP–FGP model provides a transparent and flexible decision-support framework for sustainability-oriented production planning, offering clear insights into trade-offs among competing objectives and contributing to the applied mathematical literature on multi-objective production planning under uncertainty.
Copyright (c) 2025 Nabila Zakia Indra http://creativecommons.org/licenses/by-sa/4.0
Adaptive Learning in Higher Education: A Stochastic Modeling Approach Revealing Absorbing States and Dominant Adaptive Parameters
This study evaluates the effectiveness of adaptive learning methods using an integrated stochastic modeling framework. Empirical analysis based on real data from 30 students, and stochastic simulation-based analysis (DTMC, Monte Carlo, Sobol, and HBM) used for modeling, probabilistic validation, and parameter sensitivity exploration. A Discrete-Time Markov Chain was applied to model transitions in learner ability, and Monte Carlo simulation was used to validate the probabilistic behavior. The Sobol sensitivity method identified the dominant parameters, while Hierarchical Bayesian Modeling accounted for inter-student variability. The findings show consistent upward transitions, with no students regressing to lower ability states and those in the High category remaining in an absorbing state. Sensitivity analysis indicates that adaptivity level (α) has the strongest influence on performance improvement, followed by difficulty ratio (λ) and feedback frequency (β). The Bayesian model explains more than 70% of the variance in learning gains. Overall, the study concludes that stochastic modeling provides a robust framework for evaluating adaptive learning systems and demonstrates that well-designed adaptive mechanisms significantly enhance student performance, with engagement measured through system interaction logs.
Copyright (c) 2025 Ruth Mayasari Simanjuntak http://creativecommons.org/licenses/by-sa/4.0
Spatial Grouping of Tornado-Relevant Wind Regimes Areas in Indonesia to Enhance Disaster Risk Mitigation Capacity
Tornadoes are major weather hazards in Indonesia, where wind variability is important for assessing disaster risk and supporting energy planning. This study conducts a short-term (one-year) analysis by identify similarities in regional wind speed patterns using a time-series clustering approach, treating monthly average wind speeds in 2024 as proxies for tornado-relevant wind regimes rather than direct tornado occurrence data. Agglomerative hierarchical clustering is integrated with three distance measures—Dynamic Time Warping (DTW), Autocorrelation Function (ACF), and Short Time Series (STS)—and optimized using Brain Storm Optimization (BSO) to determine optimal distance weighting and cluster numbers. The results indicate that DTW provides the best performance, yielding a two-cluster solution with a Silhouette Coefficient of 0.5292. The first cluster exhibits relatively stable wind patterns, while the second shows higher temporal variability. This framework provides a data-driven basis for region-specific wind energy planning and tornado-adaptive infrastructure considerations in Indonesia.
Copyright (c) 2025 Sela Naren Ardelita, A'yunin Sofro http://creativecommons.org/licenses/by-sa/4.0
Evaluating Double Resampling through Simulation and Application in Semiparametric Truncated Spline for Waste Economic Value
This study developed a double resampling procedure as a method for estimating standard error in truncated spline semiparametric path modeling, given that standard error cannot be obtained analytically. Primary data were collected from 100 respondents through a Likert scale questionnaire and analyzed using a path structure involving Facility and Infrastructure Quality and Waste Bank Participation have a significantly positive effect on 3R-Based Waste Management Practices and the Waste Economic Value . The modeling process involved selecting knots, estimating spline functions, and evaluating double resampling performance through simulation studies. The results showed that and had a significant positive effect on and , while the relationship between and was negative before and after the 21% threshold. The simulation study shows that the Jackknife-Bootstrap method produces lower standard errors and bias, while the Bootstrap-Jackknife method is more stable at very small sample size. These findings confirm the effectiveness of double resampling.
Copyright (c) 2025 Kamelia Hidayat, Adji Achmad Rinaldo Fernandes, Atiek Iriany, Moh. Zhafran Hidayatulloh http://creativecommons.org/licenses/by-sa/4.0
Modeling and Forecasting Volatility through EGARCH-X and EGARCH-CJ Models
This study compares the performance of EGARCH-X and EGARCH-CJ models in forecasting financial market volatility using daily TOPIX data (2004–2011). Model parameters were estimated using an efficient Bayesian MCMC framework. The results indicate that the EGARCH-CJ model, which decomposes volatility into continuous and jump components, provides a superior in-sample fit. More importantly, in out-of-sample forecasting, the EGARCH-CJ model demonstrates significantly better accuracy for medium- and long-term horizons (e.g., MSE reductions up to 30% at the 5-day horizon, with significant Diebold-Mariano statistics). In contrast, the standard EGARCH model remains more effective for short-term forecasts. These findings underscore the importance of explicitly modeling jump dynamics for medium-term risk management in the Japanese stock market, offering valuable insights for financial modelers and risk managers.
Copyright (c) 2025 Didit Budi Nugroho, Benita Dwitya Putri, Bambang Susanto http://creativecommons.org/licenses/by-sa/4.0
Sentiment Analysis of Indonesia’s Free Nutritious Meal Program on Platform X (Formerly Twitter) Using IndoBERT
Public sentiment toward government programs is increasingly expressed through social media, necessitating robust quantitative evaluation methods. This study examines public sentiment toward Indonesia’s Free Nutritious Meal (Makan Bergizi Gratis/MBG) program using 7,958 manually annotated Indonesian-language posts from platform X (January–August 2025), consisting of 3,752 positive, 848 negative, and 3,358 neutral tweets. Sentiment classification was conducted using IndoBERT-base-P2 and compared with a Support Vector Machine (SVM) baseline with TF-IDF features, employing class-weighted learning to address data imbalance. Model performance was evaluated using accuracy and macro F1-score, followed by paired-sample statistical testing. IndoBERT-base-P2 achieved 92% accuracy and a macro F1-score of 0.90, outperforming SVM (86% accuracy, macro F1 = 0.83). Paired t-test results indicate that this improvement is statistically significant (p < 0.05), confirming the robustness of transformer-based modeling. This study contributes methodologically by integrating contextual language modeling, imbalance-aware optimization, and inferential statistical validation within a unified sentiment analysis framework, demonstrating the quantitative advantage of transformer-based approaches for Indonesian social media policy analysis.
Copyright (c) 2025 Adiba Zahriyah Muhabbab, Bunyamin Bunyamin, Hasmawati Hasmawati http://creativecommons.org/licenses/by-sa/4.0
A Modified Frequency-Based FLRG Fuzzy Time Series Model for National Rice Production Forecasting
Accurate predictions of national rice production are crucial for food sustainability, yet data fluctuations pose a major challenge. This study aims to improve forecasting accuracy by developing a modified Fuzzy Time Series (FTS) model that simplifies the Fuzzy Logical Relationship Group (FLRG) by retaining only the logical relationships with the highest frequency of occurrence. Monthly Indonesian rice production data from January 2018 to March 2025 were used to test this model. To assess the effectiveness of this modification, the model’s performance was compared with Chen’s conventional FTS models of orders 1 to 3 through MAD, RMSE, and MAPE. Results indicate that the modified third-order FLRG achieved the best accuracy (MAD = 196,410; RMSE = 271,774; MAPE = 5.46%), while reducing FLRG complexity by 10.84%. This demonstrates that FLRG simplification effectively captures longer seasonal dependencies while reducing computational complexity. Nevertheless, the model’s sensitivity to sudden structural changes underscores the need for adaptive or probabilistic FLRG enhancement, with hybrid mechanisms as a potential complement. Overall, the proposed approach provides an efficient decision-support tool for maintaining food supply stability and guiding data-driven agricultural policy in Indonesia.
Copyright (c) 2025 Windy Lestari, Adika Setia Brata, Suci Rahmawati, Dimas Jayantara http://creativecommons.org/licenses/by-sa/4.0
Comparative Analysis of SmartPLS, WarpPLS, and R Studio: Accuracy, Features, Usability, and Licensing
Partial Least Squares Structural Equation Modeling (PLS-SEM) is widely applied to analyze relationships among latent variables using different software tools. This study compared SmartPLS, WarpPLS, and R Studio from quantitative and qualitative perspectives. A synthetic dataset (N=100) with a simple reflective model (X1, X2→Y) was analyzed under equivalent settings, including reflective indicators and bootstrapping with 5,000 resamplings, to ensure structural equivalence and highlight algorithmic differences. Quantitative results showed consistent external loadings above 0.70, with small numerical deviations (overall MAD=0.039) and the largest variation in item Y4 (MaxDiff=0.087). Internal model estimates were stable, with minor differences in path coefficients (MaxDiff≤0.040) and larger variation in R² for R Studio (MaxDiff=0.098), reflecting differences in latent score calculations. Bootstrapping confirmed significance (T>1.96; p<0.05), though variability in T statistics was observed across software. Qualitatively, SmartPLS excelled in usability and visualization, WarpPLS in nonlinear analysis, and R Studio in flexibility and cost-effectiveness. Computationally, SmartPLS consumed the most memory, R Studio was moderate, and WarpPLS was most efficient, with all execution times under five seconds. These findings suggest that software choice should consider not only numerical accuracy but also usability, licensing, and computational efficiency to align with research objectives and user competencies.
Copyright (c) 2025 Fajar Hari Prasetyo, Dika Kurnia Edo Kisworo, Indra Bagus Mahardhika, Shoim Ghifari http://creativecommons.org/licenses/by-sa/4.0
Comparative Performance of Spatial Robust Small Area Estimation Methods: A Simulation Study
Estimating parameters for small areas often faces limitations due to insufficient sample sizes, resulting in low-precision estimates. The Small Area Estimation (SAE) approach is used to address this problem by utilizing auxiliary variables to improve estimation efficiency. This study evaluates four SAE methods, namely EBLUP, REBLUP, SEBLUP, and SREBLUP, through a simulation study based on a nested error model across 18 scenarios that combine two area sizes (16 and 64 areas), levels of outlier contamination in the error component, and degrees of spatial correlation in the area-level random effects. Each scenario is replicated 50 times. Model performance is evaluated using Relative Bias (RB) and Relative Root Mean Square Error (RRMSE). The results show that non-robust methods are sensitive to outliers, whereas robust methods produce more stable estimates. The SREBLUP method demonstrates the best performance under low to moderate spatial correlation. In addition, an ANOVA test is conducted to identify factors that significantly affect the response.
Copyright (c) 2025 Baiq Dian Arianingsih, Kusman Sadik, Indahwati Indahwati http://creativecommons.org/licenses/by-sa/4.0
Min–Max Fuzzy TOPSIS with Entropy Weighting for Strategic Location Multicriteria Decision Making
This study aimed to develop a robust, objective framework for strategic location Multicriteria Decision Making (MCDM) by effectively addressing criteria conflict and data uncertainty. The research methodology utilized a novel hybrid approach, integrating the Entropy method to determine objective criteria weights with a Min-Max Fuzzy TOPSIS model, a modification adopted specifically to improve the consistency and rationality of alternative ranking results. The model was applied to a case study concerning strategic location selection in Batubara Regency, evaluating five alternative locations based on six criteria. The major finding from the objective weighting process was that the Number of Students ( ) was the most influential criterion, receiving the highest weight of 0.294. Subsequent analysis using the modified Fuzzy TOPSIS revealed that Alternative (Madang Deras) achieved the highest performance index , securing the first rank. Numerical validation showed a significant improvement over existing approaches. When compared with the original Min-Max Fuzzy TOPSIS, the Performance Index of the best alternative ( ) increased from 0.588 to 0.673, representing a 14.46% improvement. Furthermore, when evaluated against a model using uniform weighting, the Performance Index increased from 0.449 to 0.673, reflecting a substantial 49.89% enhancement. These results demonstrate that entropy-based objective weighting meaningfully improves the discriminative power of the decision model and reduces bias. Overall, the proposed hybrid framework offers a more stable, accurate, and comprehensive approach for strategic location selection.
Copyright (c) 2025 Endah Nurfebriyanti, Parapat Gultom, Tulus Tulus http://creativecommons.org/licenses/by-sa/4.0
Weather-Driven Loss Modeling for Rice Farmers’ Losses Using Cobb–Douglas and VaR–ES
Weather variability poses significant risks to rice production, leading to potential income losses for farmers and increased uncertainty in agricultural planning. This study integrates a Cobb–Douglas production function with Value at Risk (VaR) and Expected Shortfall (ES) measures to assess weather-driven production losses in Aceh Besar using secondary data on rainfall, temperature, and wind speed from 2010 to 2023. Rice production is first modeled to estimate output sensitivity to climatic factors, after which production losses are derived from forecast-based outcomes. Several candidate parametric probability distributions are fitted to the loss data, and the most suitable distribution is selected based on goodness-of-fit ranking. The results indicate that weather variables significantly reduce rice output and that the production process exhibits decreasing returns to scale. The selected distribution yields a potential loss of IDR 774,352 and an expected loss of IDR 940,160 per hectare at the 95% confidence level. These findings provide a quantitative basis for weather-based agricultural risk assessment and support evidence-based risk mitigation strategies for farmers and policymakers.
Copyright (c) 2025 Nurviana Nurviana, Amelia Amelia, Riezky Purnama Sari, Ulya Nabilla, Mawarni Mawarni, Masthura Masthura http://creativecommons.org/licenses/by-sa/4.0
Fuzzy-AHP for Teaching Quality Assessment and Student Performance Prediction in Mathematics Education Program
This study proposes a fuzzy–Analytic Hierarchy Process (Fuzzy-AHP) model to evaluate teaching quality and predict student academic performance in a Mathematics Education program, based on data collected from 100 undergraduate Mathematics Education students (n = 100). A structured Evaluation Index System (EIS) comprising six criteria and twenty-six sub-criteria was constructed, with criterion weights derived using AHP based on expert judgments and student responses represented as triangular fuzzy numbers. The model produces composite teaching quality scores through fuzzy aggregation and centroid defuzzification, identifying Integration and Relevance of Teaching as the most influential dimension. Predictive validation using Spearman correlation and linear regression confirms a significant positive relationship between teaching quality and academic performance (ρ = 0.46, p < .01), with instructional quality explaining 21% of performance variance. From an applied mathematics perspective, this study contributes a formally structured fuzzy-AHP modelling framework with empirical predictive validation, advancing teaching quality assessment beyond descriptive ranking toward evidence-based performance prediction.
Copyright (c) 2025 dame ifa sihombing http://creativecommons.org/licenses/by-sa/4.0
Classifying Alumni Satisfaction in Integrity Zone Universities Using CART: Evidence from Indonesian Higher Education
This study examines alumni satisfaction within the Integrity Zone framework in Indonesian higher education using survey data collected from 315 alumni out of a total population of 1,483 graduates. Alumni satisfaction was dichotomized into satisfied and dissatisfied groups and analyzed using the Classification and Regression Tree (CART) method to identify the most influential service related factors. The results indicate that employee sincerity, service timeliness, completeness of information, adequacy of facilities, and institutional transparency particularly the availability of anti-corruption information are the key determinants of alumni satisfaction. The CART model achieved an Area Under the Curve (AUC) value of 0.73, indicating fair discriminative ability according to standard classification guidelines. These findings provide practical insights for improving service quality and integrity-oriented governance in higher education institutions.
Copyright (c) 2025 Nurhasanah Nurhasanah, Eka Baqtini, Evi Ramadhani, Nany Salwa, Latifah Rahayu Siregar http://creativecommons.org/licenses/by-sa/4.0
Development of a Traffic Signal Green Time and Cycle Length Optimization Model Using NSGA-III
Optimization of delay, capacity, and emissions in signalized intersections brings in conflicting goals. But most literature studies have dealt with these individual goals or used two-objective optimization methods, and such approaches are not efficient in capturing these tradeoffs. To overcome this problem, this research utilizes Non-dominated Sorting Genetic Algorithm-III in designing a model to optimize delay, capacity, and emissions for a four-leg intersection. Convergence, hypervolume indicator, and spread methods are used to examine algorithm performance, and knee point solutions are used to identify a tradeoff solution. The obtained output shows that NSGA-III gives a smooth and evenly spread Pareto front with a hypervolume and a spread of which represents excellent convergence and diversification capabilities. Following the outcome of the experiment based on knee point identification, the solution with index 38 gives an optimal control setting of q₁ = 37.7 s, q₂ = 20 s, q₃ = 20 s, q₄ = 27.8 s, with cycle length of C = 121.42 s. With this setting, the average delay is reduced by 38%, the queue length by , and the degree of saturation is improved to 0.83, while capacity is reduced moderately and total emissions increased. In summary, this research work proves the NSGA III is efficient in identifying tradeoffs among delays, capacity and emission. The highlight of this research work is that the knee point gives the most balanced operational solution without excessively increasing cycle length.
Copyright (c) 2025 Meliana Pasaribu, Helmi Helmi, Adrah Adrah, Eka Nisrina Yaqin http://creativecommons.org/licenses/by-sa/4.0
Text Mining and News Sentiment Analysis of the PPRT (Domestic Worker Protection) Bill in Three Online News Media From 2004 to 2024
The Domestic Worker Protection Bill (RUU PPRT) has been a critical issue in Indonesia, yet its legislative process has stagnated for two decades, leading to intense public discourse. This study aims to analyze the sentiment and narrative dynamics of RUU PPRT news coverage in online media, as well as the media's role in shaping public opinion. Employing a Text Mining and Lexicon-Based Sentiment Analysis approach, enhanced with adaptations for the Indonesian lexicon, this research analyzes 387 news articles from three prominent online media outlets (Tempo, Kompas, and VOA News) published between 2004 and 2024. The findings reveal that positive sentiment dominates with 58.1%, followed by negative sentiment at 31.3%, and neutral sentiment at 10.6%. Tempo was identified as the most active media outlet covering this issue. These results indicate that the mass media plays a significant role in shaping the pattern of public discourse regarding the PPRT Bill, particularly through the dominance of positive sentiment in its reporting and confirm that lexicon-based sentiment analysis can systematically capture the dynamics of complex socio-political narratives.
Copyright (c) 2025 Dian Novita Kristiyani, Richard Gordon Mayopu http://creativecommons.org/licenses/by-sa/4.0
Uncertainty-Aware Kalman Filtering via Intrusive Polynomial Chaos for Disturbance Estimation
Robust control under parameter uncertainty requires reliable disturbance estimation. This paper proposes an uncertainty-aware method, namely Intrusive Polynomial Chaos-based Kalman Filter (IPC-KF) for systems with probabilistic parameters and measurement noise. The method is evaluated through two numerical case studies and compared with a nominal Kalman filter (KF). Results from 100 realizations, assessed using RMSE and mean variance, show that the IPC-KF achieves estimation accuracy comparable to the nominal KF. For the spring–mass–damper system, the RMSE difference is below , with both methods yielding the same mean variance of . For the F-16 aircraft model, identical RMSE values and a mean variance of are obtained. While IPC-KF captures parameter uncertainty via polynomial chaos, augmenting the state with disturbances does not necessarily improve estimation accuracy. Further studies are needed to assess uncertainty bounds and robustness.
Copyright (c) 2025 Heri Purnawan http://creativecommons.org/licenses/by-sa/4.0
Automated Social Media Advertising Poster Design Using a Multi-Agent AI Framework
This study proposes an automated system for social media advertising poster generation using a role-based multi-agent architecture implemented with CrewAI. The system decomposes the design task into three sequential agents responsible for text generation, visual asset recommendation, and grid-based layout optimization. A formal 12×12 discrete layout model is employed to represent spatial constraints, enabling consistent and structured poster composition. System performance was evaluated through user testing involving five respondents using a five-point Likert scale. The results show mean scores of 3.2 for content completeness, 3.6 for layout consistency, 3.8 for text relevance, and an overall performance mean of 3.4 (SD = 0.15), indicating satisfactory usability. From an applied mathematics perspective, this work contributes a computational layout formulation using grid discretization and rule-based optimization, as well as a quantitative evaluation of multi-agent coordination efficiency. The proposed framework demonstrates that agentic AI can effectively support structured visual content generation while maintaining user-controlled refinement.
Copyright (c) 2025 Christiana Naida Putri, Kemas Rahmat Saleh Wiharja http://creativecommons.org/licenses/by-sa/4.0
Technical Assessment of Neuro-Symbolic AI for Cultural and Fractal Analysis of Batik Motifs
This study presents a structured literature review of Neuro-Symbolic Artificial Intelligence (NSAI) approaches for extracting cultural semantics and fractal features from Batik motifs. A structured multi-database screening (2015–2025) yielded 69 peer-reviewed studies, which were synthesized thematically. The review identifies three key findings: existing vision-based models generally lack explicit mechanisms for encoding intangible cultural rules; hybrid neural–symbolic approaches demonstrate improved interpretability and compositional reasoning; and fractal-based descriptors show promise for representing culturally grounded motif structures. Based on these findings, this study proposes a conceptual NSAI framework that combines symbolic knowledge representations with fractal feature modeling, without empirical validation at this stage. The synthesis highlights potential applications in motif recognition, generative motif modeling, and computer-assisted cultural heritage preservation. Overall, NSAI offers a feasible and explainable conceptual framework for modeling Batik’s intangible cultural knowledge.
Copyright (c) 2025 Rahmat Tullah, Lilis Stianingsih http://creativecommons.org/licenses/by-sa/4.0