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All Journal JUITA : Jurnal Informatika Journal of Applied Data Sciences
Syafrial Fachri Pane
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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NS-SVM: Bolstering Chicken Egg Harvesting Prediction with Normalization and Standardization Aji Gautama Putrada; Nur Alamsyah; Muhamad Nurkamal Fauzan; Syafrial Fachri Pane
JUITA: Jurnal Informatika JUITA Vol. 11 No. 1, May 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i1.15140

Abstract

Breeding chickens and chicken eggs are poignant, and recent studies have applied computer science to optimize this field, including chicken egg harvesting prediction. However, existing research does not emphasize the importance of data transformation to obtain optimum chicken egg harvesting prediction. This paper proposes the normalization and standardization-bolstered support vector machine (NS-SVM) method, namely normalization, and standardization, to improve the prediction of chicken egg harvest using SVM. First, we obtain the chicken egg dataset from Africa using Kaggle. The problem and solution become urgent, whereas chicken egg production can ease businesspeople to invest in chicken eggs. We adopt the normalization and standardization method from previous research. However, the notation is to differentiate the method from legacy SVM. The dataset has up to 13 features. Then we apply standard pre- processing such as label encoding and random oversampling. We also review the dataset feature using the Pearson correlation coefficient (PCC). We use two SVM kernels: radial basis function (RBF) and the 2nd-degree polynomial. Then we again apply the same model but by applying normalization and standardization. We use cross- validation with ???? = ???????? to measure the Accuracy of the compared models. The results show that normalization and standardization positively affect the prediction model of the two SVM kernels. The model with the highest performance is NS-SVM with a 2nd-degree kernel, namely ???????????????????????????????? = ????. ????????????. At the same time, the model with the lowest performance is SVM with RBF, namely???????????????????????????????? = ????. ????????????. In addition, the results of ROC AUC analysis show that the performance of our model on the imbalanced dataset with a moderate degree is ???????????? = ????.???????????? to ????.????????????.
Hybrid Multi-Objective Metaheuristic Machine Learning for Optimizing Pandemic Growth Prediction Adiwijaya Adiwijaya; Syafrial Fachri Pane; Mahmud Dwi Sulistiyo; Alfian Akbar Gozali
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.981

Abstract

Pandemic and epidemic events underscore the challenges of balancing health protection, economic resilience, and mobility sustainability. Addressing these multidimensional trade-offs requires adaptive and data-driven decision-support tools. This study proposes a hybrid framework that integrates machine learning with multi-objective optimization to support evidence-based policymaking in outbreak scenarios. Six key indicators—confirmed cases, disease-related mortality, recovery count, exchange rate, stock index, and workplace mobility—were predicted using eight regression models. Among these, the XGBoost Regressor consistently achieved the highest predictive accuracy, outperforming other approaches in capturing complex temporal and socioeconomic dynamics. To enhance interpretability, we developed SHAPPI, a novel method that combines Shapley Additive Explanations (SHAP) with Permutation Importance (PI). SHAPPI generates stable and meaningful feature rankings, with immunization coverage and transit station activity identified as the most influential factors in all domains. These importance scores were subsequently embedded into the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to construct Pareto-optimal solutions. The optimization results demonstrate transparent trade-offs among health outcomes, economic fluctuations, and mobility changes, allowing policymakers to systematically evaluate competing priorities and design balanced intervention strategies. The findings confirm that the proposed framework successfully balances predictive performance, interpretability, and optimization, while providing a practical decision-support tool for epidemic management. Its generalizable design allows adaptation to diverse geographic and epidemiological contexts. In general, this research highlights the potential of hybrid machine learning and metaheuristic approaches to improve preparedness and policymaking in future health and socioeconomic crises.
Co-Authors Adiwijaya Aji Gautama Putrada Alfian Akbar Gozali Mahmud Dwi Sulistiyo Muhamad Nurkamal Fauzan Nur Alamsyah