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All Journal EKSAKTA: Journal of Sciences and Data Analysis MATEMATIKA CAUCHY: Jurnal Matematika Murni dan Aplikasi Indonesian Journal of Mathematics and Natural Sciences Al-Jabar : Jurnal Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Matematika UNAND Majalah Ilmiah Matematika dan Statistika (MIMS) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Journal of Fundamental Mathematics and Applications (JFMA)
Titi Udjiani SRRM
Departemen Matematika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Engineering Materials Science & Nanotechnology Mathematics

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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.
Co-Authors Aina Latifa Riyana Putri Bagas, Rizky Muhammad Bayu Surarso Etna Vianita Harjito - Harjito Harjito Ikhtiyar, Zakaria Bani Irawanto, B Juwanda, Farikhin Muhammad Rafid Fadil Nikken Prima Puspita Nurhayati, Neri Puspita, N P R., IDA M. Redemtus Heru Tjahjana Rizky Muhammad Bagas Shelly Sholatan Kamilah Solikhin Zaki Sunarsih Sunarsih Suryoto Suryoto Suryoto, S susilo hariyanto Susilo Hariyanto Usman, Carissa Devina Vianita, Etna Yuri C Sagala Zaenurrohman, Zaenurrohman