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All Journal Mechatronics, Electrical Power, and Vehicular Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Abdimas Jurnal Riset Fisika Edukasi dan Sains ALCHEMY Jurnal Penelitian Kimia Journal of Applied Engineering and Technological Science (JAETS) Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT)
Miftahul Anwar
Electrical Engineering Faculty Of Engineering Sebelas Maret University
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences

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Enhancing the Readability of Academic Data for Machine Learning through Preprocessing Techniques Anna Mayyah Soraya; Faisal Rahutomo; Miftahul Anwar
Journal of Electrical, Electronic, Information, and Communication Technology Vol 8, No 1 (2026): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.8.1.103104

Abstract

Academic data plays a central role in supporting decision-making in educational institutions. However, the successful implementation of machine learning to analyze and make predictions based on academic data highly depends on the quality and readability of the data. To fully harness the potential of machine learning, careful preprocessing of academic data is essential. This research aims to design and implement preprocessing techniques, that is imputation, winsorizing, and dropping data on academic data. To handle missing values, the Multivariate Imputation by Chained Equation method is used with three different algorithms, linear regression, random forest, and KNN, and then the accuracy of these three algorithms in predicting missing values is compared. Additionally, winsorizing method is applied to outliers and data duplication is addressed by dropping duplicate data. Based on the testing results through evaluation metrics, these preprocessing techniques can improve model accuracy by 0.037 for MAE, 0.11 for RMSE, and 0.006 for MSE. The processed data allows the model to function more optimally and produce more reliable results.
Co-Authors Achmad Bahrudin Agus Ramelan Ahmad E. Esmaio Alfian Ma’arif Anna Mayyah Soraya Athaya Cantia Putri Berlian Shanaza Andiany Chico Hermanu B.A. Faisal Rahutomo Ferdiansyah Ashil Farisi Feri Adrianto Feri Adriyanto Hari Maghfiroh Hasan M.S. Atia Irwan Iftadi Irwan Iftadi Joko Hariyono Joko Slamet Saputro Jusuf Abimas Pratama Meiyanto Eko Sulistyo Mohamed Milad Baiek Muhammad Hamka Ibrahim Muhammad Nizam Putra Maulana Yusuf Rino Herwangga Sifaus Wulaning Arsri Stephanus Hanurjaya Subuh Pramono Teguh Endah S Teguh Endah Saraswati