JURNAL ILMIAH MATEMATIKA DAN TERAPAN

On H-Irregularity Strengths of Square Chain Graphs

2026-08-01T02:44:28+00:00

This paper investigates three types of graph labeling schemes, namely H-irregular vertex, edge, and total labeling. These labeling frameworks are examined in the context of square chain graphs SC_n for n>=1. Furthermore, by establishing matching lower and upper bounds, the exact values of the vertex, edge, and total SC_m-irregularity strengths of SC_n are determined for all m with 1<=m<=n.

Higher-order SEM-PLS Modeling of School Readiness Among Indonesian Senior High School Students

2026-08-01T02:46:05+00:00

Improving the quality of secondary education is a strategic priority in Indonesia due to its impact on human wellbeing. Secondary education faces challenges in improving the quality of learning and students' psychological readiness. School preparation is widely acknowledged as a multifaceted concept that highlights the significance of social-emotional engagement, self-regulation, and cognitive-motivational engagement. Therefore, this study aims to examine the construct of school readiness in Indonesia senior high school students using higher-order SEM-PLS. Results indicate that behavioral regulation is the strongest predictor of academic achievement (0.385) and functions as the central mechanism transmitting social-emotional engagement into academic outcomes. However, cognitive-motivational factors exert a competitive mediating effect, indicating that academic grades do not always improve among students who show strong cognitive-motivational engagement (-0.130). The results of this study highlight the important role of behavioral regulation in academic achievement and indicate that social-emotional engagement exerts the strongest overall influence on cognitive-motivational engagement, thereby supporting a multidimensional integrated model of school readiness.

Optimization of the Electricity Economics System (EES) in Supporting National Energy Policy Using a Tri-Level Programming Approach

2026-08-01T02:45:52+00:00

High electricity subsidies in the national budget (APBN) pose a challenge to national fiscal sustainability. This research aims to formulate an optimization model for the Electricity Economics System (EES) that hierarchically balances the interests of the government, consumers, and producers. The methodology employs a Tri-Level Programming approach with a nested optimization strategy. Simulation results demonstrate that the model reached a stable convergence point at the 41st iteration with a zero relative change value. The national subsidy allocation was significantly reduced from IDR 68.64 trillion to IDR 24.03 trillion. Thus, this model serves as a strategic decision-making instrument that effectively represents hierarchical interactions between energy actors to produce more efficient fiscal policy solutions and support the sustainability of the national power system.

Comparison of the Euler Method, Fourth-Order Runge-Kutta Method, and Runge-Kutta-Fehlberg Method in Solving an RLC System

2026-08-01T02:45:42+00:00

This article discusses a comparison of numerical methods in solving systems of differential equations in resistor-inductor-capacitor (RLC) circuits. The methods used include the Euler method, the fourth-order Runge-Kutta (RK4), and the Runge-Kutta-Fehlberg (RKF). The circuit model is expressed in the form of a second-order differential equation system which is then transformed into a first-order system to facilitate numerical solutions. The performance of each method is evaluated by comparing the numerical solution to the exact solution using the Root Mean Square Error (RMSE) error measure. The results showed that the Euler method had the lowest accuracy rate, while the RK4 method produced significantly more accurate results. The RKF method performed best with small errors.

BBQ Weather Prediction in Basel Using Ensemble Machine Learning

2026-08-01T02:45:32+00:00

Weather-dependent decision making, such as planning an outdoor barbecue (BBQ), benefits from short-term forecasts that are both accurate and honestly evaluated. This study addresses two overlooked risks in applied weather classification: label leakage from same-day rule-based targets, and validation-set overfitting caused by repeated model-selection decisions. Using the ECA&D Basel daily weather records (2000–2010), the original BBQ-weather label was found to be fully determined by same-day precipitation, so the task was reframed as next-day forecasting through one-day lag features and target shifting. Data were split chronologically into training, validation, and test sets (60:20:20) to preserve temporal independence. Five heterogeneous classifiers (CatBoost, LightGBM, RUSBoost, Nearest Centroid, SGDClassifier) were compared, tuned with a Genetic Algorithm, and combined through three ensemble strategies: Weighted Voting via Dirichlet-distributed random search, Stacking, and Greedy Ensemble Selection. Weighted Voting achieved the best validation F1-score (0.6841), with RUSBoost receiving the largest weight (0.6700). A 7-feature subset, selected via SHAP, native feature importance, and linear coefficients, was statistically indistinguishable from the full 22-feature model (McNemar test, = 0.8388) and was adopted as the final model for parsimony. On the held-out test set, the final model achieved F1 = 0.6776, ROC-AUC = 0.9076, and PR-AUC = 0.6961, with only a 0.0065 gap from validation performance, confirming strong generalization. These results demonstrate that rigorous chronological splitting and formal statistical testing can materially change both the interpretation and the trustworthiness of ensemble classification results in weather-dependent decision support.