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MICE Implementation to Handle Missing Values in Rain Potential Prediction Using Support Vector Machine Algorithm Putri, Aina Latifa Riyana; Surarso, Bayu; SRRM, Titi Udjiani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16699

Abstract

Support Vector Machine (SVM) is a machine learning algorithm used for classification. SVM has several advantages such as the ability to handle high-dimensional data, effective in handling nonlinear data through kernel functions, and resistance to overfitting through soft margins. However, SVM has weaknesses, especially when handling missing values in data. The use of SVM must consider the missing values strategy chosen. Missing values in data mining is a serious problem for researchers because it causes many problems such as loss of efficiency, complications in data handling and analysis, and the occurrence of bias due to differences between missing data and complete data. To overcome the above problems, this research focuses on understanding the characteristics of missing values and handling them using the Multiple Imputation by Chained Equations (MICE) technique. In this study, we utilized secondary data experiments that contain missing values from the Meteorological, Climatological, and Geophysical Agency (called BMKG) related to predictions of potential rain, especially in DKI Jakarta. Identification of types or patterns of missing values, exploration of the relationship between missing values and other variables, incorporation of the MICE method to handle missing values, and the Support Vector Machine Algorithm for classification will be carried out to produce a more reliable and accurate prediction model for rain potential. It shows that the imputation method with the MICE gives better results than other techniques (such as Complete Case Analysis, Imputation Method Mean, Median, Mode, and K-Nearest neighbor), namely an accuracy of 89% testing data when applying the Support Vector Machine algorithm for classification.
Characterization and Cartesian Product of Smarandache Semigroups (S-semigroups) Fadhilah, Laila Karimatul; Suryoto; Nikken Prima Puspita; Titi Udjiani
Integra: Journal of Integrated Mathematics and Computer Science Vol. 3 No. 1 (2026): March
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20263149

Abstract

Let (S, *) be a semigroup. A semigroup S is called a Smarandache semigroup (or S-semigroup) if it contains a proper subset A ⊂ S such that (A, *) forms a group under the same binary operation defined on S. In general, not every semigroup admits a proper subset that is a group; hence, not all semigroups are S-semigroups. In this paper, several structural conditions related to Smarandache semigroups are investigated. In particular, we study the role of idempotent and completely regular elements in the structure of S-semigroups. These conditions provide a characterization of S-semigroups. Furthermore, this study investigates whether the Cartesian product of two or more S-semigroups is again an S-semigroup.