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All Journal Jambura Journal of Mathematics Jurnal Varian Variansi : Journal of Statistics and Its Application on Teaching and Research Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Panrannuangku Jurnal Pengabdian Masyarakat ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology SAINSMAT: Journal of Applied Sciences, Mathematics, and Its Education Seminar Nasional Pengabdian Kepada Masyarakat VARIANSI: Journal of Statistics and Its Application on Teaching and Research Seminar Nasional Hasil Penelitian LP2M UNM ARRUS Jurnal Pengabdian Kepada Masyarakat Abdi Kimia : Jurnal Pengabdian Masyarakat SMART: Jurnal Pengabdian Kepada Masyarakat
Zulkifli Rais
Department of Statistics, Universitas Negeri Makassar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Journal : ARRUS Journal of Mathematics and Applied Science

 1 
Applied of the Self-Organizing Maps (SOM) Method for Clustering Educational Equity in South Sulawesi Gunawan, Andi Restu; Sudarmin, Sudarmin; Rais, Zulkifli
ARRUS Journal of Mathematics and Applied Science Vol. 4 No. 1 (2024)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience2607

Abstract

This research aims to group regencies/cities based on education indicators and identify the characteristics of each group formed based on education indicators. The method used in this research is Self self-organizing map (SOM). SOM is an artificial neural network that requires no assumptions and a method that produces a representation of the input space from low-dimensional training samples. The data used in this research are 9 variables regarding pure enrollment rates, gross enrollment rates, and student-to-teacher ratios at each level of education in 24 districts/cities in South Sulawesi in 2020-2021 which come from BPS publications. Based on the results obtained, 4 clusters were formed, each of which had its characteristics. The clusters formed include Cluster 1 consisting of 7 regencies/cities, cluster 2 consisting of 10 regencies/cities, cluster 3 consisting of 4 regencies/cities, and Cluster 4 consisting of 2 regencies. Based on the results of cluster validation using the Dunn index, 4 optimal clusters were obtained with a value of 0.42.
Implementation of the Support Vector Regression (SVR) Method in Inflation Prediction in Makassar City Ruliana, Ruliana; Rais, Zulkifli; Marni, Marni; Ahmar, Ansari Saleh
ARRUS Journal of Mathematics and Applied Science Vol. 4 No. 1 (2024)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience2608

Abstract

Inflation is an important economic indicator, the growth rate is always kept low and stable. One step to deal with the possibility of a high inflation rate is to know the picture of the inflation rate in the future by making predictions. Prediction is a method used to determine a value or need in the next period. Support Vector Regression (SVR) is a development of the Support Vector Machine (SVM) method which is used for regression cases which can handle non-linear data cases. The problem that often occurs when using the SVR method is determining optimal model parameters. One way to determine the best parameters for the SVR method is to use Grid Search Optimization. The stages of the SVR method include data normalization, dividing training data and testing data, using the Radial Basis Function kernel, selecting the best parameters using Grid Search Optimization, and making predictions using the best model obtained with parameters γ = 10, ∁ = 100, and ε. = 0.1 with k = 5. The prediction results obtained were then evaluated by looking at the RMSE value, the RMSE value obtained was 0.029, which means the model's ability to follow the data pattern well and the prediction results made were very good.
Co-Authors A. Bida Purnamasari Ahmar, Ansari Saleh Annas, Suwardi Asmar Asmar, Asmar Asmi, Nia Nurul Aswi, Aswi Fahmuddin, Muh. Ferigo Taufani Tri Hakiki Fithriyah Azzahrah Gunawan, Andi Restu Hardianti Hafid Irwan Irwan Irwan Irwan Irwan Irwan Isnaeni R Jiran Julita Karunia Rahayu Ayu Lili Maghfirah Rahma Sudirman Marni Marni, Marni Mar’ah, Zakiyah Misveria Villa Waru Muh. Hizbul Zainul Muttaqim Muhammad Arif Tiro Muhammad Fahmuddin Muhammad Fahmuddin Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Refaldy Nurdin Nurdin Nurhamidah Mursyidin Nurtiwi Nurtiwi Nurul Madany Nusrang, Muhammad Rahmat Hesha Safei Rezky Novita Said Riska Aprianti Rizal Bakri Ruliana Ruliana Ruliana Ruliana, Ruliana Ruliana, R. Rusdianto S. Sudarmin, S. Sitti Masyitah Meliyana R. Sudarmin Sudarmin Sudarmin Sudarmin Surahman Nur, Surahman Suwardi Annas Uca Sideng Yusal, Muh Sri Yusniar Rasjid Yusniar Rasjid, Yusniar Zakiyah Mar'ah