Penggunaan Metode Rough Set untuk Menentukan Tingkat Kesiapan Siswa dalam Menghadapi ANBK di SMP Negeri 2 Kuala
DOI:
https://doi.org/10.62951/repeater.v3i3.619Keywords:
ANBK, Data Mining, Rosetta, Rough Set, Student ReadinessAbstract
The Computer-Based National Assessment (ANBK) is an essential instrument designed to comprehensively measure student competence, including literacy, numeracy, and character aspects. However, in practice, many students still face various challenges during preparation, such as cognitive limitations, psychological readiness, and technical barriers, which affect their overall readiness to participate in ANBK. This study aims to analyze the readiness level of students at SMP Negeri 2 Kuala by employing the Rough Set method. The variables examined include digital literacy, subject matter understanding, psychological readiness, and school facility support. Data were collected from 250 ninth-grade students through structured questionnaires and subsequently processed using the Rosetta software to perform attribute reduction and generate decision rules. The findings indicate that digital literacy, subject matter understanding, and psychological readiness are the most influential variables in determining student readiness, while facility support serves only as a complementary factor. The extraction process generated seven decision rules with an accuracy level of 100%, which effectively classified students into three readiness categories: highly ready, ready, and less ready. These results confirm that the Rough Set method is highly effective for identifying dominant factors and producing decision rules that can guide schools in developing targeted strategies to enhance student readiness for ANBK.
Downloads
References
Alamsyah, R., & Allwine. (2023). Penerapan metode rough set pada tingkat kepuasan kostumer terhadap kualitas pelayanan lapangan futsal. Jurnal Bisantara Informatika (JBI), 7(1), 1–14.
Andal EdD, E. Z., Panergayo, A. A. E., & Almanza, M. R. G. (2020). Exploring the Online Learning Self-efficacy of Teacher Education Students at the Laguna State Polytechnic University: Basis for Transition to Flexible Learning System. Universal Journal of Educational Research, 8(12), 6598–6608. https://doi.org/10.13189/ujer.2020.081224
Andini, y., tata hardinata, j., pranayama purba, y., & studi sistem informasi stikom tunas bangsa jljend sudirman blok no, p. A. (2022). Penerapan data mining terhadap tata letak buku di perpustakaan sintong bingei pematangsiantar menggunakan metode apriori: vol. Xi (issue 1). Http://ejournal.stmik time.ac.id
M. Arif Rahman. (2020). Penerapan Metode Rough Set Dalam Memprediksi Penjualan Perumahan (Studi Kasus Di Pt. Anugerah Pasadena Pekanbaru). Fakultas Teknik Ilmu Komputer, 14(2), 324–355.
Manik, M. (2022). Kesiapan siswa dalam menghadapi Asesmen Nasional berbasis komputer. Jurnal Pendidikan Asatiza: Jurnal Pendidikan, 3(1), 1–10. https://ejournal.stai-tbh.ac.id/index.php/asatiza
Masrizal, M., & Munandar, M. H. (2020). Identifikasi Siswa Bermasalah Dengan Menggunakan Metode Rough Set (Studi Di Sma N 4 Pariaman). Jurnal Informatika, 7(2), 90–99. https://doi.org/10.36987/informatika.v7i2.1342
Muzani, M., Martanto, M., Dikananda, A. R., & Rifai, A. (2025). Alghoritma Backpropagation Neural Network Dengan Mengoptimasi Particle Swarm Optimization Untuk Memprediksi Saham Bank Bca. Jurnal Informatika Dan Teknik Elektro Terapan, 13(2), 331–338. https://doi.org/10.23960/jitet.v13i2.6208
Nisa, C., Humaira, M. A., & Efendi, I. (2023). Analisis Pelaksanaan Asesmen Nasional Berbasis Komputer (Anbk) Di SDN 01 Pasirmuncang. Karimah Tauhid, 2(5), 2119–2127. https://ojs.unida.ac.id/karimahtauhid/article/view/10385%0Ahttps://ojs.unida.ac.id/karimahtauhid/article/download/10385/4220
Nur Khormarudin, A., Kevin Synagogue Panjaitan, O. C., Septianingsih, A., Faisal, M., Utami, W. S., Novia Ningsi, L., Satria Tambunan, H., Zai, C., & Komputer, T. (2022). Teknik Data Mining: Algoritma K-Means Clustering. Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika Dan Statistika, 1(2), 116–123.
Sahid, D. S. S., Efendi, R., Putra, E. H., & Wahyudi, M. (2020). Categorizing attributes in identifying learning style using rough set theory. International Journal of Advanced Computer Science and Applications, 11(1), 292–298. https://doi.org/10.14569/ijacsa.2020.0110136
Samaray, S. (2022). Implementasi Algoritma Rough Set dengan Software Rosetta untuk Prediksi Hasil Belajar. Jurnal Eksplora Informatika, 11(1), 57–66. https://doi.org/10.30864/eksplora.v11i1.498
Setiyowati, H., Suryati, E., & Rina, R. (2022). Analisis Pelaksanaan Asesmen Nasional Berbasis Komputer (ANBK) Di Madrasah Ibtidayah Negeri 9 Hulu Sungai Utara. Al-Madrasah: Jurnal Pendidikan Madrasah Ibtidaiyah, 6(3), 803. https://doi.org/10.35931/am.v6i3.1086
Sinaga, d. M., windarto, a. P., tambunan, h. S., & damanik, i. S. (2022). Data mining menggunakan metode asosiasi apriori untuk merekomendasi pola obat pada puskesmas. Journal of information system research (josh), 3(2), 143–149. Https://doi.org/10.47065/josh.v3i2.1237
Utari, S. (2022). Penerapan Algoritma Rought Set Untuk Memprediksi Jumlah Permintaan Produk. Bulletin of Artificial Intelligence, 1(1), 1–7. https://doi.org/10.62866/buai.v1i1.1
Yusup Susanto, A. (2020). Prediksi Untuk Menentukan Kesiapan Siswa SMP Menghadapi Ujian Nasional Metode Data Mining Decision Tree (Doctoral dissertation, Universitas Muhammadiyah Ponorogo).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Repeater : Publikasi Teknik Informatika dan Jaringan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
