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All Journal Mandalika Mathematics and Educations Journal Journal of Educational Studies
Dhirga Tandi Teppa
Institut Teknologi Bacharuddin Jusuf Habibie
Humanities Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Evaluating Swarm-Genetics for VRPTW: Robustness Across Seeds and Fleet Efficiency On Solomon Benchmarks Aprizal Resky; Zaitun Zaitun; Dhirga Tandi Teppa
Mandalika Mathematics and Educations Journal Vol 7 No 4 (2025): Desember
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i4.10213

Abstract

The Vehicle Routing Problem with Time Windows (VRPTW) is a challenging NP-hard problem in logistics optimization. This study evaluates a Swarm-Genetics algorithm, a hybrid method combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with swarm regeneration and adaptive parameter control. The algorithm was tested on 57 Solomon benchmark instances (C, R, RC) under three random seeds to assess robustness. Results show that the algorithm is robust across seeds, producing stable outcomes with minimal variation. It frequently preserves fleet efficiency, often matching the Best Known Solutions (BKS) in vehicle count, particularly for clustered instances. However, routing distances remain less competitive, with average gaps of about 10% for clustered, 12–13% for random, and over 20% for mixed cases. Convergence analysis further indicates rapid early improvements but stagnation in complex distributions. Overall, Swarm-Genetics provides a robust and fleet-efficient framework, though further enhancements are needed to improve distance quality.
SMOTE-Enhanced SVM for Imbalanced Nutritional Status Classification in MBG Marwan Sam; Ahmad Husain; Dhirga Tandi Teppa
Journal of Educational Studies Vol. 4 No. 2 (2026): July
Publisher : Lembaga Bale Literasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58218/jes.v4i2.2803

Abstract

The increasing prevalence of imbalanced data in public health, particularly in nutritional surveillance among school-aged children, poses a significant challenge in accurately identifying cases of malnutrition, where the majority class (well-nourished children) often overshadows minority cases, leading to biased predictive outcomes. This issue is especially critical within the Free Nutritious Food (MBG) Program, where early detection of at-risk children is essential for effective intervention. Therefore, this study aims to develop a more reliable predictive model that can address class imbalance and improve the detection of malnutrition. To achieve this, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset by generating synthetic samples of the minority class, followed by classification using a Support Vector Machine (SVM). The model was evaluated using various train-test split ratios and assessed through multiple performance metrics, including accuracy, sensitivity, specificity, Cohen’s kappa, and AUC. The findings reveal that conventional SVM models trained on imbalanced data fail to detect malnutrition cases, resulting in zero sensitivity despite high accuracy. In contrast, the SMOTE-enhanced SVM significantly improves detection performance, achieving sensitivity above 90% and accuracy between 95% and 97%, with the optimal model obtained at an 70:30 split ratio. In conclusion, integrating SMOTE with SVM effectively overcomes class imbalance and provides a robust predictive framework for early identification of malnutrition in school-aged children.
Co-Authors Ahmad Husain Marwan Sam Resky, Aprizal Zaitun Zaitun