Yacob, Thoricka Adiiq (2026) Sistem Identifikasi Siswa Berisiko Pada SMP Negeri 18 Samarinda Dengan Algoritma Decision Tree. S1 Teknik Informatika thesis, STMIK Widya Cipta Dharma.
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Abstract
Identifikasi siswa berisiko secara dini menjadi tantangan besar di SMP Negeri 18 Samarinda karena prosesnya masih manual, tidak terstruktur, dan bergantung pada ingatan guru. Data absensi, nilai, dan pelanggaran yang tersebar menyebabkan intervensi terhadap siswa bermasalah seringkali terlambat dan tidak tepat sasaran. Penelitian ini bertujuan untuk merancang dan membangun sistem identifikasi siswa berisiko berbasis web yang dapat mengklasifikasikan siswa secara objektif menggunakan algoritma Decision Tree. Metode penelitian menggunakan model pengembangan Waterfall dengan tahapan analisis kebutuhan, perancangan (UML dan ERD), implementasi (Python, Streamlit, SQLite), dan pengujian. Variabel yang digunakan adalah frekuensi bolos, nilai rata-rata, dan jenis pelanggaran (dikonversi ke numerik). Model Decision Tree dilatih dengan 80 data siswa dan diuji dengan 70 data lainnya. Pengujian dilakukan melalui black-box testing dan perbandingan hasil klasifikasi sistem terhadap penilaian manual guru pada 30 sampel siswa. Hasil penelitian menunjukkan bahwa sistem berhasil mengklasifikasikan siswa ke dalam tiga kategori risiko (rendah, sedang, tinggi) dengan tingkat akurasi sebesar 86,7%. Nilai presisi rata-rata mencapai 86,0%, recall 86,0%, dan F1-score 86,0%. Pengujian fungsionalitas menunjukkan seluruh fitur (login, input, edit, hapus, dashboard, ekspor, dan manajemen user) berfungsi 100%. Sistem juga memberikan rekomendasi tindakan spesifik untuk setiap kategori risiko. Kesimpulannya, sistem ini efektif sebagai alat bantu bagi guru dan BK dalam melakukan deteksi dini, monitoring, dan intervensi siswa berisiko. =========================================================== Early identification of at-risk students is a major challenge at SMP Negeri 18 Samarinda because the process remains manual, unstructured, and reliant on teachers' memory. Scattered attendance, grade, and violation data often lead to delayed and misguided interventions for problematic students. This research aims to design and build a web-based at-risk student identification system that can classify students objectively using the Decision Tree algorithm. The research methodology employed the Waterfall development model, encompassing requirements analysis, design (UML and ERD), implementation (Python, Streamlit, SQLite), and testing. The variables used were absenteeism frequency, average grades, and violation types (converted to numeric values). The Decision Tree model was trained on 80 student records and tested on another 70. System evaluation was conducted through black-box testing and by comparing the system's classification results against manual teacher assessments on 30 student samples. The results indicate that the system successfully classifies students into three risk categories (low, medium, high) with an accuracy rate of 86.7%. The average precision, recall, and F1-score all reached 86.0%. Functional testing confirmed that all features (login, input, edit, delete, dashboard, export, and user management) are 100% operational. The system also provides specific action recommendations for each risk category. In conclusion, this system effectively serves as a decision support tool for teachers and counselors in conducting early detection, monitoring, and intervention for at-risk students, thereby contributing to improved education.
| Item Type: | Thesis (S1 Teknik Informatika) |
|---|---|
| Additional Information: | Pembimbing 1 : Wahyuni, S.Kom., M.Kom Pembimbing 2 : Ahmad Abul Khair, S.Kom., M.T |
| Uncontrolled Keywords: | identifikasi siswa berisiko, decision tree, data mining, prediksi, SMP |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Teknik Informatika |
| Depositing User: | Mr Thoricka Adiiq Yacob |
| Date Deposited: | 07 Aug 2026 08:38 |
| Last Modified: | 07 Aug 2026 08:38 |
| URI: | http://repository.wicida.ac.id/id/eprint/6463 |
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