KLASIFIKASI KELULUSAN SISWA SMA ISLAM Al AZHAR 5 CIREBON   MENGGUNAKAN METODE DECISION TREE

Muhammad Yusuf; Rudi Kurniawan; Bani Nurhakim; Ade Irma Purnamasari

Authors

Muhammad Yusuf STMIK IKMI Cirebon, Indonesia
Rudi Kurniawan STMIK IKMI Cirebon, Indonesia
Bani Nurhakim STMIK IKMI Cirebon, Indonesia
Ade Irma Purnamasari STMIK IKMI Cirebon, Indonesia

Keywords:

Data Mining, Decision Tree, Klasifikasi, Kelulusan Siswa, RapidMiner

Abstract

Penentuan kelulusan siswa merupakan salah satu proses penting dalam dunia pendidikan yang memerlukan ketelitian, objektivitas, dan efisiensi. Di lingkungan sekolah, proses evaluasi kelulusan sering kali masih dilakukan secara manual sehingga membutuhkan waktu yang lama serta berpotensi menimbulkan subjektivitas dalam pengambilan keputusan. Oleh karena itu, diperlukan suatu sistem berbasis data yang mampu membantu pihak sekolah dalam menentukan kelulusan siswa secara lebih objektif dan cepat. Penelitian ini bertujuan untuk membangun model klasifikasi kelulusan siswa di SMA Islam Al Azhar 5 Cirebon tahun ajaran 2024–2025 dengan menggunakan metode Decision Tree dalam bidang Data Mining. Data yang digunakan berjumlah 615 data siswa periode 2023–2024 yang diperoleh dari SMA Islam Al Azhar 5 Cirebon, dengan variabel nilai mata pelajaran seperti PAI, PKN, Bahasa Indonesia, Matematika, Bahasa Inggris, PJOK, Seni, serta beberapa mata pelajaran lainnya, termasuk nilai rata-rata sebagai atribut pendukung. Proses pengolahan dan pemodelan data dilakukan menggunakan perangkat lunak RapidMiner dengan pembagian data training sebesar 80% dan data testing sebesar 20%. Hasil penelitian menunjukkan bahwa model Decision Tree mampu menghasilkan tingkat akurasi sebesar 100% pada data training dan data testing dengan nilai error 0%. Untuk memastikan performa model dan menghindari kemungkinan overfitting, dilakukan pengujian menggunakan teknik K-Fold Cross Validation sebanyak 10 fold yang menghasilkan akurasi sebesar 99,35% dengan classification error sebesar 0,65%, weighted mean recall sebesar 99,66%, dan weighted mean precision sebesar 95,25%. Hasil tersebut menunjukkan bahwa model Decision Tree memiliki tingkat akurasi yang sangat tinggi dan berpotensi untuk diterapkan sebagai sistem pendukung keputusan dalam proses evaluasi kelulusan siswa di sekolah.

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