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All Journal ITEj (Information Technology Engineering Journals) Jurnal Ilmiah KOMPUTASI Jurnal Mirai Management JOIV : International Journal on Informatics Visualization JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Sisfokom (Sistem Informasi dan Komputer) JURTEKSI Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknik Informatika (JUTIF) eProceedings of Engineering The Indonesian Journal of Computer Science International Journal of Computer Technology and Science
Oktariani Nurul Pratiwi
Telkom University
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering

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Journal : The Indonesian Journal of Computer Science

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Application of SMOTE Method on Topic Based Question Classification Using Naïve Bayes Algorithm Orvalamarva; Oktariani Nurul Pratiwi; Faqih Hamami
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4179

Abstract

In today's digital era, the utilization of technology in education is essential to support the learning process. This research discusses the classification of junior high school mathematics questions using the Naïve Bayes method. The use of an automated system in question classification helps reduce time and effort in grouping questions based on topics. The Naïve Bayes method was chosen because of its simplicity and ability to process data. The results showed that Naïve Bayes with SMOTE and Math symbols achieved 69% accuracy, while without SMOTE, the accuracy was lower. Cross-validation showed that the classification without symbols attained an accuracy of 89.35%, slightly superior to the classification using symbols, which was 88.79%. This result indicates that Naïve Bayes with SMOTE is more effective. Although the difference in accuracy with or without symbols is slight 0.56%, the performance is relatively equivalent, with an accuracy of 89%.
Co-Authors Adnyana, I Gusti Ngurah Bagus Putra Agustiana, Nathifa Alyasyifa, Salma Anindya Prameswari Putri Djakaria Anshari, Faishal Mufied Al Ariandi, Rama Asriana Asriana, Asriana Bagaskara, Kumara Ris Panji Cahyani, Pradita Chika Enggar Puspita Chrisdiyanti, Ivania Nonita Deden Witarsyah Deden Witarsyah Djakaria, Anindya Prameswari Putri Eflin Trinova Limbong Fa'rifah, Riska Yanu Fa'rifah, Riska Yanua Fakhrurroja, Hanif Faqih Hamami Fazrin, Fadhilah Firzania, Heidea Yulia Gosal, Yose Thimothy Hadwirianto, Muhammad Raihan Hanid, Wihda Sifwi Hilman Dwi Anggana Hwang, Jun-seok I B K Widiartha Iqbal Yulizar Mukti Irfan Darmawan Kamil, Andhika Ihsan Kardila, Yuni Kasparov, Michael Christensen Bonar Kurniawan, Muhammad Rayhan Mardika, Jody Muhaimin, Luthfi Ahmad Muharman Lubis Mustada, Fitrina Annisa Nisa, Amarila Zahratun Novanza, Alvin Renaldy Nur Ichsan Utama Nuraliza, Hilda Orvalamarva Pramudia, Rudi Guna Pramuwidya, Shinta Rachmadita Andreswari Rukmana, Putri Utami Saleh Hasan Badjrie Sutoyo, Edi Talisman, Bryan Ronald Yoon, Hyoen-yeong Yulio Ferdinand