Integrasi Analisis Butir Soal dan Klasifikasi Kognitif Untuk Personalisasi Rekomendasi Remedial Adaptif Menggunakan K-Nearest Neighbor
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Karimah Agustin
*
Universitas Nusa Putra, Sukabumi,
Indonesia
- Alun Sujjada Universitas Nusa Putra, Sukabumi, Indonesia
- Muhammad Ikhsan Thohir Universitas Nusa Putra, Sukabumi, Indonesia (*) Corresponding Author
Abstract
In many secondary schools, classroom assessments still use tests that are written by hand, which makes it hard for teachers to ensure each question matches the right level of thinking from Bloom's taxonomy or to check if the questions are statistically reliable. This study tackles that issue by creating a web-based testing system for a madrasah tsanawiyah, which automatically sorts multiple-choice questions, checks their quality using psychometric methods, and offers custom learning suggestions to help students improve. Cognitive-level tags (C1 to C6) are assigned by matching keywords that describe actions, and a kNearest Neighbor classifier uses TF-IDF features to sort items into groups that show more or less complex thinking. Item quality is checked using Classical Test Theory module automatically computes the difficulty index and discrimination index of each item from students answer patterns, and materials that help improve learning are chosen based on each student's weak areas through content-based filtering. The system was developed using the Waterfall model and was tested on 600 items taken from three different subjects. The classifier achieved 86.67% accuracy when using k=11, which was verified through 10- fold cross-validation showing 81.83% ± 5.19%, and all 23 black-box test cases were successfully passed. The results show that using automatic item analysis along with adaptive remediation is possible to implement in assessment practices at the madrasah level. The main contribution of this study is the integration of automatic cognitive classification, CTT-based psychometric analysis, and content-based filtering remedial recommendation into a single real-time examination platform, a combination that has not been found in previous similar studies, which generally address only one of these three aspects separately.
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References
K. R. dan T. R. I. Kementerian Pendidikan, “Peraturan Menteri Pendidikan, Kebudayaan, Riset, dan Teknologi Nomor 21 Tahun 2022 tentang Standar Penilaian Pendidikan pada Pendidikan Anak Usia Dini, Jenjang Pendidikan Dasar, dan Jenjang Pendidikan Menengah,” Jakarta, 2022.
Suyit Ratno et al., “Analisis Penerapan Taksonomi Bloom dalam Pembelajaran Siswa Kelas VI SD Negeri 105293 Medan Estate,” Sinar Dunia: Jurnal Riset Sosial Humaniora dan Ilmu Pendidikan, vol. 3, no. 4, pp. 127–136, Dec. 2024, doi: 10.58192/sidu.v3i4.2726.
Pusat Kurikulum dan Pembelajaran, “Panduan Pembelajaran dan Asesmen Kurikulum Merdeka,” Jakarta, 2022.
B. R. Yandra, F. Meliasanti, and D. Huri, “Tinjauan Tipe HOTS dan LOTS dalam Soal ASTS serta ASAS Bahasa Indonesia Tahun 2022-2024,” Jurnal Ilmiah FONEMA, vol. 7, no. 1, pp. 61–77, Aug. 2024, doi: 10.25139/fn.v7i1.8633.
J. Cressa and M. Mukhlis, “Level Kognitif Taksonomi Bloom pada Soal Mata Pelajaran Bahasa Indonesia,” J-LELC: Journal of Language Education, Linguistics, and Culture, vol. 3, no. 1, pp. 55–62, Feb. 2023, doi: 10.25299/j-lelc.2023.12094.
F. Y. Hermanto, A. D. Anggraeni, and S. Sutirman, “Analisis Butir Soal dengan Classical Test Theory untuk Mengukur Kemampuan Kognitif Siswa,” Efisiensi : Kajian Ilmu Administrasi, vol. 20, no. 2, pp. 159–170, Jan. 2025, doi: 10.21831/efisiensi.v20i2.80225.
Mu’ah Sharoh Suprihatin and Fina Fakhriyah, “Analisis Butir Soal Materi Rantai Makanan Meliputi Validitas, Reliabilitas, Tingkat Kesukaran, dan Daya Beda,” Jurnal Yudistira : Publikasi Riset Ilmu Pendidikan dan Bahasa, vol. 4, no. 1, pp. 77–91, Jan. 2026, doi: 10.61132/yudistira.v4i1.2615.
A. N. Izzah, S. S. L. Azizah, R. Ekawati, and I. S. Rufiana, “Eksplorasi Analisis Butir Soal Bahasa Indonesia di Kelas II Sekolah Dasar Berdasarkan Taksonomi Bloom,” Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI), vol. 5, no. 2, pp. 1031–1043, Apr. 2025, doi: 10.53299/jppi.v5i2.1064.
Kementerian Agama Republik Indonesia, “Keputusan Menteri Agama Republik Indonesia Nomor 450 Tahun 2024 tentang Pedoman Implementasi Kurikulum pada Raudhatul Athfal, Madrasah Ibtidaiyah, Madrasah Tsanawiyah, Madrasah Aliyah, dan Madrasah Aliyah Kejuruan,” Jakarta, 2024.
H. Lahza, T. G. Smith, and H. Khosravi, “Beyond item analysis: Connecting student behaviour and performance using e‐assessment logs,” British Journal of Educational Technology, vol. 54, no. 1, pp. 335–354, Jan. 2023, doi: 10.1111/bjet.13270.
N. I. Rosyida, S. W. Nugraha, U. Agustin, C. Rochman, and A. Y. Nuryantini, “OPTIMALISASI PROGRAM REMEDIAL BERDASARKAN INSTRUMEN AKREDITASI SATUAN PENDIDIKAN DALAM PEMBELAJARAN DIFERENSIASI KURIKULUM MERDEKA,” Jurnal Evaluasi Pendidikan, vol. 14, no. 1, pp. 12–16, Oct. 2023, doi: 10.21009/jep.v14i1.36763.
M. R. Arrohman, N. Khoiriyah, A. Fawaida, and D. L. Fithri, “ANALISIS KLASIFIKASI KELULUSAN SISWA MENGGUNAKAN METODE KNN (K-NEAREST NEIGHBOR) RANDOM FOREST PADA DATASET ‘STUDENTS PERFORMANCE.’” Jurnal Mahasiswa Sistem Informasi (JMSI), vol. 6, no. 2, pp. 299-305, 2025, doi: 10.24127/jmsi.v6i2.9102.
V. Alvian, D. Hidayatullah, A. Nilogiri, H. Azizah, and A. Faruq, “Klasifikasi Siswa Berprestasi Menggunakan Metode K-Nearest Neighbor (KNN) Pada SMA Negeri 2 Situbondo Classification Of Achieving Students Using K-Nearest Neighbor (KNN) Method At SMA Negeri 2 Situbondo,” Jurnal Smart Teknologi, vol. 3, no. 6, 2022. https://jurnal.unmuhjember.ac.id/index.php/JST/article/view/8484/0
M. R. Kurniawan, O. N. Pratiwi, and F. Hamami, “KLASIFIKASI SOAL MENGGUNAKAN MULTI-LABEL PROBLEM TRANSFORMATION DENGAN METODE RANDOM FOREST DAN K-NEAREST NEIGHBOR,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 1, pp. 367–380, Jan. 2025, doi: 10.29100/jipi.v10i1.5910.
B. P. Adam, C. Suhartono, and D. Firmansyah, “Klasifikasi Teks Soal Ujian Bahasa Indonesia Berdasarkan Taksonomi Bloom,” Jurnal Informatika, vol. 9, no. 1, p. `15-26, 2022.
R. Ardiansyah, M. Ari Bianto, and B. D. Saputra, “Sistem Rekomendasi Buku Perpustakaan Sekolah menggunakan Metode Content-Based Filtering,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 4, no. 2, pp. 510–518, Oct. 2023, doi: 10.37859/coscitech.v4i2.5131.
F. D. Astuti and W. Andriyani, “Pengembangan Sistem Rekomendasi Pembimbing Tugas Akhir Menggunakan Teknik Content Based Filtering,” JIKO (Jurnal Informatika dan Komputer), vol. 9, no. 2, p. 474, Jun. 2025, doi: 10.26798/jiko.v9i2.1599.
N. Jannah, “ANALISIS PENGEMBANGAN SISTEM INFORMASI BATIK MADURA MENGGUNAKAN METODE WATERFALL DAN BLACK BOX TESTING,” JURNAL SATYA INFORMATIKA, vol. 8, no. 01, pp. 64–81, Jun. 2023, doi: 10.59134/jsk.v8i01.239.
Tim Penyusun Kurikulum MTs Al-Hapidhiyah, “Kurikulum Operasional Madrasah MTs Al-Hapidhiyah Tahun Ajaran 2025/2026,” 2025.
I. Listiowarni and N. Puspa Dewi, “Pemanfaatan Klasifikasi Soal Biologi Cognitive Domain Bloom’s Taxonomy Menggunakan KNN Chi-Square Sebagai Penyusunan Naskah Soal,” Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, vol. 11, no. 2, pp. 186–197, Nov. 2020, doi: 10.31849/digitalzone.v11i2.4798.
S. Nurjanah et al., “Psychometric quality of multiple-choice tests under classical test theory (CTT): AnBuso, Iteman, and R,” Jurnal Penelitian dan Evaluasi Pendidikan, vol. 28, no. 2, pp. 161–172, Dec. 2024, doi: 10.21831/pep.v28i2.71542.
R. SHEPTIAN, I. SARIFAH, and R. RIYADI, “CLASSICAL TEST THEORY ANALYSIS USING ANATES: A STUDY OF MATHEMATICS READINESS TEST FOR ELEMENTARY SCHOOL STUDENTS,” SCIENCE : Jurnal Inovasi Pendidikan Matematika dan IPA, vol. 5, no. 1, pp. 20–29, Feb. 2025, doi: 10.51878/science.v5i1.3863.
N. F. Munazhif, G. J. Yanris, and M. N. S. Hasibuan, “Implementation of the K-Nearest Neighbor (kNN) Method to Determine Outstanding Student Classes,” SinkrOn, vol. 8, no. 2, pp. 719–732, Apr. 2023, doi: 10.33395/sinkron.v8i2.12227.
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