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All Journal Journal of Applied Science, Engineering, Technology, and Education ARRUS Journal of Mathematics and Applied Science VARIANSI: Journal of Statistics and Its Application on Teaching and Research Abdi Kimia : Jurnal Pengabdian Masyarakat
Fahmuddin S, Muhammad
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Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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Journal : ARRUS Journal of Mathematics and Applied Science

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Classification Of Hypertension Using Methods Support Vector Machine Genetic Algorithm (SVM-GA) Fahmuddin S, Muhammad; Rais, Zulkifli; Yuniar, Eka Citra
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 1 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3976

Abstract

Support Vector Machine (SVM) is a machine learning method for classifying data that has been successfully used to solve problems in various fields. The risk minimization principle used can produce an SVM model with good generalization capabilities. The problem with the SVM method is the difficulty in determining the optimal SVM hyperparameters. This research uses Genetic Algorithm (GA) to optimize SVM hyperparameters. GA optimization on SVM is used to classify hypertension. From the result of classification analysis using GA, it shows good accuracy value performance, namely 100% compared to using only SVM.
Classification of Family Welfare Card Recipients in Makassar City Using Decision Tree Algorithms Rais, Zulkifli; Fahmuddin S, Muhammad; Musfira, Musfira
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4783

Abstract

This study aims to analyze the factors influencing the determination of recipients of the Family Welfare Card (KKS) program in Makassar City and evaluate the level of accuracy of the decision tree model in the classification process. The KKS program is a government effort to accelerate poverty alleviation, so it is important to ensure that the selection process for program recipients is carried out on target. The decision tree method is used in this study because of its ability to simplify the decision-making process through an easy-to-understand tree structure. This study utilizes KKS recipient data with various variables, such as income, number of dependents, employment status, asset ownership, and education level, to build a classification model. The results of the study indicate that the variable of the Head of Household's (KRT) Highest Education Level (X4) has the highest level of importance in determining KKS recipients, followed by the variable Number of Family Members (X1), and the variable Ownership of Residential Buildings (X5). The decision tree model that was built has an accuracy level of 84.21%, which states the model's ability to classify KKS recipients effectively. This study also provides insight into the description of factors influencing KKS receipts, which can be used as a basis for formulating more efficient and targeted policies.
Co-Authors Aidid, Muhammad Kasim Alam, Muhammad Nur Harfiana Abbas, Gusma Haryadi, Dhiya Izdihar Muh. Imam Shadiq Munawwarah Musfira, Musfira Mustika M, Sitti Sri nurismi, Nur ismi Nurliah, Muhammad Jabbar Taslim Nurul Fahmi Nusrang, Muhammad Rais, Zulkifli Ruliana Ruliana Ruliana, Ruliana Saida, Saida Sudding Suwardi Annas Triutomo, Agung Yuniar, Eka Citra