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All Journal Journal of Applied Science, Engineering, Technology, and Education Mattawang: Jurnal Pengabdian Masyarakat Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology VARIANSI: Journal of Statistics and Its Application on Teaching and Research Abdi Kimia : Jurnal Pengabdian Masyarakat
Rais, Zulkifli
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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 Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Journal : ARRUS Journal of Mathematics and Applied Science

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K-Means Cluster Analysis for Grouping Districts in South Sulawesi Province Based on Village Potential Azrahwati; Nusrang, Muhammad; Aidid, Muhammad Kasim; Rais, Zulkifli
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience739

Abstract

Cluster analysis is an analysis in multivariable statistics that is used to group objects that have the same characteristics. One of the methods in cluster analysis used to group relatively large amounts of data is the K-Means method. In this study, the K-Means method was applied to classify sub-districts in South Sulawesi Province based on village potential. The variables used are the number of: Elementary School/Equivalent degree, Junior High School/Equivalent degree, Senior High School/Vocational School/Equivalent degree, Community Health Center/Pustu, Families without electricity, Villages/Urbans according to market presence, Villages/Towns that are passed by public transportation and Villages/Kelurahan that have lighting main road. The results of this study are that 3 groups are formed where the first cluster consists of 107 sub-districts that have high village potential, the second cluster consists of 16 sub-districts that have medium village potential and the third cluster consists of 184 sub-districts that have low village potential.
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.
Co-Authors Ahmar, Ansari Saleh Aidid, Muhammad Kasim Amira, Husnul Asmar Asmar, Asmar Azrahwati Fahmuddin S, Muhammad Fahmuddin, Muh. Indrayasaro Mar’ah, Zakiyah Muhammad Refaldy Nusrang, Muhammad Purnamasari, A. Bida Rizal Bakri Ruliana Ruliana Ruliana, Ruliana Rusdianto Saida, Saida Sitti Masyitah Meliyana R. Sudarmin Sudarmin Sudarmin Suwardi Annas Syahputra, Akbar Triutomo, Agung Yuniar, Eka Citra Yusniar Rasjid, Yusniar