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All Journal SAINSMAT Indonesian Journal of Human Nutrition Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jambura Geoscience Review Mattawang: Jurnal Pengabdian Masyarakat Panrannuangku Jurnal Pengabdian Masyarakat ARRUS Journal of Mathematics and Applied Science Jurnal Klinik dan Riset Kesehatan Unnes Journal of Mathematics SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam VARIANSI: Journal of Statistics and Its Application on Teaching and Research Journal of Management, Economic, and Accounting
Ruliana Ruliana, Ruliana
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Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Health Professions Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Journal : ARRUS Journal of Mathematics and Applied Science

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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.
Cluster Analysis Using Ensemble ROCK Method in District/City Grouping in South Sulawesi Province based on People's Welfare Indicators Hidayat, Taufiq; Ruliana, Ruliana; Rais, Zulkifli; Botto-Tobar, Miguel
ARRUS Journal of Mathematics and Applied Science Vol. 3 No. 1 (2023)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience1761

Abstract

Cluster analysis is a data mining technique used to group data based on the similarity of attributes of object data. One of the problems that are often encountered in cluster analysis is data with a mixed categorical and numerical scale. The clustering stage for mixed data using the ensemble ROCK (Robust Clustering using links) method is carried out by combining clustering outputs from categorical and numeric scale data. The method used for categorical data is the ROCK method and the method used for numerical data is the Hierarchical Agglomerative method. The best clustering method is determined based on the criteria for the ratio between the standard deviations within the group (SW) and the smallest standard deviation between groups (SB). Based on 24 observation objects in the regencies and cities of the Province of South Sulawesi, the ROCK ensemble method with a value of 0.1 produces three clusters with a ratio value of 2,27 x10-16 based on the combination of the output results of the ROCK method and the Hierarchical Agglomerative method
Co-Authors Adiyanty, Tri Amelia Ahmar, Ansari Saleh Aidid, Muhammad Kasim Anggraeny Putri, Nur Arief Agoestanto Aswi, Aswi Aswi, Aswi Botto-Tobar, Miguel Dian Handayani Fahmuddin S, Muhammad Harahap, Muhammad Ikhsan Hasnita, Hasnita Ida Ayu Putu Sri Widnyani Irwan Irwan Maddatuang Maddatuang Magfirah Septiana Mangkona, Andi Ilham Azhar Mar'ah, Zakiyah Marni Marni, Marni Muhammad Arif Tiro, Muhammad Arif Mustika M, Sitti Sri Nasrul Nasrul Nurmalita, Shafira Nusrang, Muhammad Putri, Fildzah Karunia Putriaji Hendikawati Rais, Zulkifli Rosmini Maru Samichah Samichah Sembiring, Frinny Sitti Masyitah Meliyana R. Sudarmin Sudarmin Susiyanti, Susiyanti Suwardi Annas Syifa Mustika Taufiq Hidayat Yulianingrum, Chintya Zakiyah Mar'ah Zulkifli Rais