Predictive Analysis of Students’ Academic Readiness for the Academic Competency Test Using an Integrated K-Means and C4.5 Approach

Sri Atiqah  Elvidamayanti; Agung  Ramadhanu; Billy  Hendrik

doi:10.35134/komtekinfo.v13i3.709

Authors

Sri Atiqah Elvidamayanti Universitas Putra Indonesia YPTK Padang
Agung Ramadhanu Universitas Putra Indonesia YPTK Padang https://orcid.org/0000-0001-7543-6659
Billy Hendrik Universitas Putra Indonesia YPTK Padang https://orcid.org/0000-0001-7543-6659

DOI:

https://doi.org/10.35134/komtekinfo.v13i3.709

Keywords:

Academic Readiness, K-Means Clustering, C4.5 Decision Tree, Educational Data Mining, Academic Ability Test (TKA)

Abstract

The identification of students' academic readiness for the Academic Ability Test (Tes Kemampuan Akademik/TKA) is still carried out conventionally and subjectively, causing academic interventions to be frequently delayed and poorly targeted. In fact, the academic data already available in schools holds considerable potential for early, objective, data-driven detection of student readiness patterns. This study aims to build a predictive-analysis model of students' academic readiness for the TKA by integrating the K-Means and C4.5 methods. K-Means is applied to group students according to the similarity of their academic characteristics, after which the resulting clusters are used as class labels to build a decision-tree classification model using C4.5. The dataset was obtained from State Senior High School (SMAN 1) Kamang Magek, comprising 82 students with 11 predictor variables covering academic aspects (average compulsory and elective subject scores, score trend, score stability, attendance) and non-academic aspects (learning motivation, parental support, learning intensity, mental condition). The resulting model achieved an accuracy of 93.90% with a weighted F1-score of 0.93, with K-Means identifying three readiness groups (Ready, Fairly Ready, Not Ready) consisting of 45, 20, and 17 students, respectively. Learning motivation proved to be the most dominant factor in the C4.5 decision tree, followed by score trend, score stability, and attendance. This study contributes an interpretable early-detection model of academic readiness that can be operationalized as a basis for designing data-driven academic intervention programs in senior high schools.

References

J. Juriyah, T. Fahrudin, and J. Jalaludin, “ANALISIS KESIAPAN IMPLEMENTASI KEBIJAKAN TES KEMAMPUAN AKADEMIK (TKA) 2025 OLEH KEMENDIKBUD,” vol. 5, pp. 5708–5721, 2025.

G. C. Rorimpandey, F. T. G. Mantik, A. Hasibuan, I. Engineering, E. Faculty, and U. N. Manado, “IMPLEMENTATION OF THE FORWARD CHAINING METHOD FOR DETECTING IMPLEMENTASI METODE FORWARD CHAINING UNTUK DETEKSI KESIAPAN,” vol. 5, no. 4, pp. 758–765, 2024.

K. Ario, T. Wibowo, U. A. Surakarta, U. Kusuma, and H. Surakarta, “Optimisasi Kesuksesan Akademis : Studi Sistem Deteksi Dini untuk Mengidentifikasi Potensi Dropout Mahasiswa Tingkat Akhir dengan Protokol Intervensi Cepat pada Perguruan Tinggi,” vol. 2, pp. 57–63, 2024.

S. Firmasari, S. Sumarwati, Y. Gloriani, S. Firmasari, and T. Erawati, “Relationship between Literacy and Numeracy for Junior High,” vol. 15, pp. 6048–6054, 2023, doi: 10.35445/alishlah.v15i4.3096.

V. Armalina, W. H. Pamungkas, I. Budi, and Sufiani, “KAJIAN SISTEMATIS PARAMETER LEARNING ANALYTICS SEBAGAI DASAR PENGEMBANGAN FRAMEWORK EVALUASI LEARNING MANAGEMENT SYSTEM,” pp. 400–408, 2025.

S. A. A. Kharis and A. H. A. Zili, “Tantangan dan peluang educational data mining dan learning analytics pada pendidikan jarak jauh di indonesia,” vol. 1, no. 2, pp. 218–225, 2024.

M. A. Bui and A. Bahtiar, “IMPLEMENTASI METODE ALGORITMA K-MEANS CLUSTERING UNTUK MENGELOMPOKKAN TRANSAKSI PENJUALAN BARANG DI TOKO ARINO,” vol. 8, no. 2, pp. 1451–1456, 2024.

L. Harmaji, A. S. Asaury, M. Wijana, and M. E. Habiby, “Segmentasi Kinerja Akademik Mahasiswa,” vol. 7, no. 2, pp. 73–81, 2024.

N. F. Aini F, D. M. Midyanti, and R. Hidayati, “PENERAPAN METODE C4.5 UNTUK KLASIFIKASI POTENSI AKADEMIK SISWA (Studi kasus: SMP DDI Sultan Syarif Abdurachman Pontianak),” vol. 11, no. 01, 2023.

A. Aziz, Setiawan, and E. Wahyudi, “Implementasi algoritma c4.5 dalam pengambilan keputusan pemberian kredit di pt abadi sejahtera,” 2024.

B. K. Paju, D. Y. A. Fallo, and S. E. Mowata, “Analisis Algoritma Klasifikasi dalam Pembelajaran Sistem Informasi Geografis di Pendidikan Informatika,” J. Kridatama Sains dan Teknol., vol. 7, no. 01, pp. 480–488, 2025, doi: 10.53863/kst.v7i01.1676.

D. Marcelina, Terttiaav, and Ini, “Hybrid clustering and supervised learning model for digital MSME segmentation,” vol. 14, no. 1, pp. 86–96, 2025.

C. Pan, “Construction of Risk Prediction Models for Enterprise Finance Sharing Operations Using K ‑ Means and C4 . 5 Algorithms,” Int. J. Comput. Intell. Syst., vol. 3, 2024, doi: 10.1007/s44196-024-00608-3.

H. A. Maulana, N. Fitriyah, D. El Baradei, F. Ardiyanto, and M. Arifin, “Analisis Data Pendidikan Menggunakan Metode Data Mining dengan Algoritma Decision Tree dan K-Means Clustering,” vol. 6, no. 1, 2026.

A. Dharma, E. B. Wijaya, D. Heyneker, and J. Vanness, “Comparison of the K-Means Algorithm and C4.5 Against Sales Data,” vol. 7, no. 2, pp. 741–751, 2023.

Khaidir, Reyna Buryani Al’Safinat, A. Muhammad Fuad Fadhlurrahman, By Tri Agung Nusantara Kr. J. Tompong, and Jessica Crisfin Lapendy, “Klasifikasi Tingkat Kesegaran Sayur Bayam Berdasarkan Tekstur Daunnya Berbasis Citra Digital,” J. Deep Learn. Comput. Vis. Digit. Image Process., vol. 2022, pp. 88–99, 2024, doi: 10.61255/decoding.v2i2.549.

C. Agusniar, S. M. Ulfa, and H. Rhomadhona, “Utilizing K-Means Clustering for Grouping Student Achievement Data to Evaluate Learning Activeness,” vol. 2, no. 3, pp. 56–59, 2025.

W. Wirdawati, S. Yulihartati, and A. Ramadhanu, “Implementasi Euclidean Distance dan Segmentasi K-Means Clustering Pada Identifikasi Citra Jeruk Nipis,” vol. 3, no. 2, pp. 72–79, 2024.

R. Nabilatulrahmah, A. Triana, T. Ispahan, S. Fitriani, K. D. Tania, and A. Rifai, “ANALISIS RISIKO AKADEMIK SISWA DENGAN METODE K-MEANS,” vol. 9, no. 4, pp. 6766–6773, 2025.

R. Budiarni, L. E. Putri, and R. Irsa, “Implementasi Algoritma C4.5 dalam Menentukan Prediksi Pendaftaran Ulang Mahasiswa Baru di STT Payakumbuh,” vol. 5, no. 2, pp. 283–289, 2025, [Online]. Available: https://doi.org/10.55382/jurnalpustakaai.v5i2.1154

Hijrah, Maulidar, and Adria, “Analisis RapidMiner Dan Weka Dalam MemprediksiKualitas Kinerja Karyawan Menggunakan MetodeAlgoritma C4.5,” Http://Jurnal.Mdp.Ac.Id, vol. 9, no. 2, pp. 1655–1665, 2022.

R. Fardiani, C. Responden, and K. Kunci Pendidikan, “Penerapan Algoritma C4.5 Untuk Prediksi Penerima Beasiswa Siswa Berprestasi Di Mi Al-Ishlah Ciganitri,” J. Inform. Univ. Muhammadiyah Tangerang P, vol. 8, no. 4, pp. 2722–2713, 2024.

F. Yenila, H. Marfalino, and S. Defit, “Model Analisis Machine Learning dengan Pendekatan Deep Learning dalam Penentuan Kolektabilitas,” vol. 12, no. 2, pp. 403–414, 2023.