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INDONESIA
Zero : Jurnal Sains, Matematika, dan Terapan
Published by Universitas Islam Negeri Sumatera Utara
ISSN : 2580569X     EISSN : 25805754     DOI : 10.30829
Core Subject : Science, Education, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics
Arjuna Subject : -
Articles 282 Documents
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Adaptive Portfolio Optimization Using MVF with Machine Learning Forecasting and Regime Switching: Evidence from LQ45 Stocks Fadly Ramdhani; Deni Saepudin
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.29447

Abstract

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.
Delay Tolerance and Recovery Control in a Synchronized Electric Bus Network using Max-Plus Algebra Marcellinus Andy Rudhito; Dewa Putu Wiadnyana Putra
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.29891

Abstract

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.
Psychometric Validation of a Multidimensional Learning Readiness Instrument Following Indonesia’s Free Nutritious Meal Program Darwis Darwis; Sudirman Sudirman; A Rahim; Abdul Kadir Djailani
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.29549

Abstract

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.
Five-Species Classification of Granivorous Bird Pests in Rice Fields using EfficientNet-B0 Transfer Learning Taufik Taufik; Rahmalia Syahputri; Tri Susilowati; Rahmat Hanif Purnama
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.28457

Abstract

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.
Modelling Leptospirosis Transmission with Waning Immunity and Stage-Structured Infection: Local stability and Vaccination Analysis Dinta Ardelia Wahyudi; Budi Priyo Prawoto
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.30511

Abstract

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.
Sector-Adjusted R-vine Copula (RVMS) Extension of CAPM for Portfolio Risk Optimization Wa Ode Intan Fully Nadya; Retno Budiarti; I Gusti Putu Purnaba
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.28837

Abstract

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.
A Comparison of Generalized Pareto and Weibull Distributions for Modeling Megathrust Seismic Hazard in Indonesia Aimmatul Ummah Alfajriyah; Moch Taufik Hakiki; Muhammad Haekhal Aqila
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.28556

Abstract

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.
Multi-State Markov Net Premium Valuation for Chronic Illness Riders with Care Benefits in Indonesia Irma Fauziah; Hamida Hamida; Suma Inna
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.29524

Abstract

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.
Dual-Fairness Nurse Scheduling via the Double Direct Progressive Filling Algorithm under Qualification and Contract Constraints Tita Putri Redytadevi; Toni Bakhtiar; Jaharuddin Jaharuddin
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.29388

Abstract

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.
Clustering Indonesian Traditional Foods by Nutritional Profiles using the K-Means Algorithm for Health Policy Irene Devi Damayanti; Samuel Yacobus Padang; Isak Tandi; Melda Duma'
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i2.26756

Abstract

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.

Page 27 of 29 | Total Record : 282

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