Learning Difficulty Levels Prediction of Elementary School Student Mathematics Using Machine Learning Model
DOI:
https://doi.org/10.56873/jitu.8.1.5906Keywords:
Mathematics Learning, KNN (K-Nearest Neighbors) Model, Difficulty Level Prediction, Educational InterventionAbstract
Difficulty learning mathematics in elementary school students is a significant problem and requires serious attention. This study aims to predict the difficulty level in elementary school students learning mathematics using a machine learning model, namely KNN. Exam scores, assignments, quizzes, and characteristics of students' difficulty level in learning mathematics were used as data in this study. A study used the KNN model to divide students into three categories of difficulty in learning mathematics: easy, moderate, and challenging. The results showed that the KNN model can accurately predict student’s difficulty levels in mathematics. Thus, applying this model can help teachers provide appropriate and effective interventions to students experiencing difficulties. Using machine learning technology, especially the KNN model, we found an accuracy of 95%. In addition, we can still accurately predict the difficulty level of elementary school students' mathematics learning. This study uses anonymous student data, the distribution of assignments, quizzes, and exam score ranges, and characteristics of mathematics learning difficulty levels. There are three prediction classes: high, medium, and low.
References
[1] Ernawati et al., Problematika Pembelajaran Matematika. Yayasan Penerbit Muhammad Zaini, 2021.
[2] Isrok’atun and A. Rosmala, Model-Model Pembelajaran Matematika. Bumi Aksara, 2021.
[3] Z. Ardi et al., “Exploring the elementary students learning difficulties risks on mathematics based on students mathematic anxiety, mathematics self-efficacy and value beliefs using rasch measurement,” J. Phys. Conf. Ser., vol. 1157, no. 3, p. 032095, Feb. 2019, doi: 10.1088/1742-6596/1157/3/032095.
[4] M. Yusuf Setia Wardana and Aries Tika Damayani, “Persepsi Siswa terhadap Pembelajaran Pecahan di Sekolah Dasar,” ResearchGate, Oct. 2024, doi: 10.31980/mosharafa.v6i3.333.
[5] S. Susanti et al., Desain Media Pembelajaran SD/MI. Yayasan Penerbit Muhammad Zaini, 2022.
[6] J. L. Harvey and S. A. P. Kumar, “A Practical Model for Educators to Predict Student Performance in K-12 Education using Machine Learning,” in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Dec. 2019, pp. 3004–3011. doi: 10.1109/SSCI44817.2019.9003147.
[7] G. S. Sidik and A. A. Wakih, “Kesulitan Belajar Matematik Siswa Sekolah Dasar pada Operasi Hitung Bilangan Bulat,” Nat. J. Kaji. Dan Penelit. Pendidik. Dan Pembelajaran, vol. 4, no. 1, pp. 461–470, 2019, doi: 10.35568/naturalistic.v4i1.633.
[8] M. Anggriyani, M. Mahsup, S. Sirajuddin, and S. Syaharuddin, “Pembelajaran Estafet Learning dengan Kemampuan Numerik untuk Meningkatkan Motivasi dan Hasil Belajar Siswa,” Semin. Nas. Paedagoria, vol. 4, no. 1, pp. 443–452, Aug. 2024.
[9] A. H. Anna Zili, “Prediksi Kelulusan Siswa pada Mata Pelajaran Matematika menggunakan Educational Data Mining,” J. Ris. Pembelajaran Mat. Sekol., vol. 7, no. 1, Mar. 2023, doi: 10.21009/jrpms.071.03.
[10] A. Patrisyah, R. Buaton, and J. N. Sitompul, “Klasifikasi Tingkat Pemahaman Siswa pada Pelajaran Matematika di MTSS PAB 5 Klambir Lima,” Saturnus J. Teknol. Dan Sist. Inf., vol. 2, no. 4, pp. 146–156, Aug. 2024, doi: 10.61132/saturnus.v2i4.345.
[11] C. Chazar and M. H. Rafsanjani, “Penerapan Teachable Machine Pada Klasifikasi Machine Learning Untuk Identifikasi Bibit Tanaman,” Pros. Semin. Nas. Inov. Dan Adopsi Teknol. INOTEK, vol. 2, no. 1, pp. 32–40, May 2022, doi: 10.35969/inotek.v2i1.207.
[12] A. K. Sari, A. Irsyad, D. N. Aini, Islamiyah, and S. E. Ginting, “Analisis Sentimen Twitter Menggunakan Machine Learning untuk Identifikasi Konten Negatif,” Adopsi Teknol. Dan Sist. Inf. ATASI, vol. 3, no. 1, Art. no. 1, Jun. 2024, doi: 10.30872/atasi.v3i1.1373.
[13] Rismayani, S. R. D. Rachman, S. Wahyuni, Asmanurhidayani, J. Y. Mambu, and M. Pineng, “Implementation Artificial Neural Network on Identification System of Neurological Disorder,” in Intelligent Communication Technologies and Virtual Mobile Networks, G. Rajakumar, K.-L. Du, and Á. Rocha, Eds., Singapore: Springer Nature, 2023, pp. 619–629. doi: 10.1007/978-981-99-1767-9_45.
[14] R. Rismayani, M. Pineng, and H. Herlinda, “Using Artificial Neural Network for System Education Eye Disease Recognition Web-Based,” J. Biomim. Biomater. Biomed. Eng., vol. 55, pp. 262–274, 2022, doi: 10.4028/p-7z9xpt.
[15] N. H. Sutanto, E. Utami, and R. Rismayani, “Systematic Literature Review untuk Identifikasi Metode Evaluasi Website Layanan Pendidikan di Indonesia,” J. Ilm. IT CIDA, vol. 7, no. 1, Art. no. 1, Jun. 2021, doi: 10.55635/jic.v7i1.133.
[16] R. Aprilian, R. Habibi, and M. Y. H. Setyawan, Algoritma KNN dalam memprediksi cuaca untuk menentukan tanaman yang cocok sesuai musim. Kreatif, 2020.
[17] H. Sunandar, “Penggunaan Model Klaster K-Means Dan Klasifikasi KNN Untuk Identifikasi Pengetahuan Matematika Mahasiswa,” KAKIFIKOM Kumpul. Artik. Karya Ilm. Fak. Ilmu Komput., pp. 75–85, Apr. 2024.
[18] D. Kartikasari and I. K. D. Nuryana, “Analisis Prediksi Pengalaman Pengguna Aplikasi MELISA menggunakan Metode SVM dan KNN,” J. Emerg. Inf. Syst. Bus. Intell. JEISBI, vol. 4, no. 4, pp. 72–78, Sep. 2023.
[19] T. Telutci and R. Harman, “Penerapan Data Mining untuk Memprediksi Prestasi Siswa Sekolah Dasar Menggunakan Algoritma C4.5,” Comput. Based Inf. Syst. J., vol. 12, no. 1, Art. no. 1, Mar. 2024, doi: 10.33884/cbis.v12i1.8207.
[20] S. D. Ariyanto and L. A. Wulandhari, “Prediksi Kinerja Calon Mahasiswa Berdasarkan Nilai Seleksi Masuk Menggunakan Pendekatan Machine Learning,” J. Ilm. Komputasi, vol. 23, no. 2, Art. no. 2, Jun. 2024, doi: 10.32409/jikstik.23.2.3589.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Information Technology and Its Utilization
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The proposed policy for journals that offer open access
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
