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Perguruan tinggi tiap tahunnya memerlukan proses evaluasi mutu bersadarkan standar Badan Akreditasi Nasional-Perguruan Tinggi (BAN-PT). Sehingga perguruan tinggi harus mengetahui kondisi mahasiswanya guna mengevaluasi dan mempertahankan Angka Edukasi Efesiensi (AEE) perguruan tinggi tersebut. Salah satu standar yang telah ditetapkan oleh BAN-PT adalah kualitas mahasiswa lulusan yang dapat dilihat dari IPK, ketepatan menyelesaikan studi, skripsi, jalur penerimaan dan lainnya. Seleksi Nasional Masuk Perguruan Tinggi (SNMPTN) dan Seleksi Bersama Masuk Perguruan Tinggi (SBMPTN) merupakan bagian dari jalur penerimaan yang ada di Universitas Lampung (UNILA). Tujuan dari penelitian ini adalah memberikan informasi mengenai klasifikasi kualitas mahasiswa lulusan UNILA berdasarkan jalur penerimaan SNMPTN dan SBMPTN dengan teknik data mining memanfaatkan software rapidminer dalam penerapan Algoritma C4.5 dan menggunakan metode penelitian Cross Industry Standard Process for Data Mining (CRIPS-DM). Data yang digunakan dalam penelitian ini merupakan data mahasiswa lulusan jalur penerimaan SBMPTN dan SNMPTN pada tahun 2013-2017. Hasil dari penelitian ini yaitu mahasiswa lulusan dengan kualitas kategori kelas I sebanyak 46% pada jalur penerimaan SBMPTN, 28% jalur penerimaan SNMPTN dan kategori kelas II sebanyak 11% jalur penerimaan SBMPTN dan 15% jalur penerimaan SNMPTN. Hasil pemodelan decision tree mendapatkan nilai akurasi sebesar 97,46% dengan nilai error sebesar 0,98% serta nilai Area Under Curve (AUC) sebesar 0,973 dengan nilai error 0,014 yang tergolong ke dalam excellent classification.  Kata kunci : data mining, Algoritma C4.5, CRISP-DM, kualitas mahasiswa, jalur penerimaan  Universities need a quality evaluation process based on the standards of the National Accreditation Agency for Higher Education (BAN-PT) every year. Therefore, it is necessary for universities knowing the students to evaluate and to maintain the University's Education Efficiency Number (AEE). One of the standards that has been determined by BAN-PT is the quality of students that can be seen from the GPA, the accuracy of completing studies, thesis, path admission, and others. The National Selection for Higher Education (SNMPTN) and Joint Selection for Higher Education (SBMPTN) are part of the path admission at the University of Lampung (UNILA). The purpose of this study is to provide information about the quality of students based on SNMPTN and SBMPTN admission with data mining techniques using RapidMiner software in the application of the C4.5 algorithm and using the research method of the Cross-Industry Standard Process for Data Mining (CRIPS-DM). The data used in this study is the data of students with SBMPTN and SNMPTN path admission in 2013 until 2017. The results of this study were students who has graduated of class I quality was 46% from the SBMPTN path admission, 28% of the SNMPTN path admission, and class II category was 11% from the SBMPTN path admission, and 15% from the SNMPTN path admission. The results of accuracy obtained in decision tree modeling got an accuracy value of 97.46% with an error value of 0.98% and the value of Area Under Curve (AUC) of 0.973 with an error value of 0.014 which is classified into a excellent classification.  Keywords: data mining, C4.5 algorithm, CRISP-DM, student quality, path admission.

ANALISA KLASIFIKASI KUALITAS MAHASISWA LULUSAN BERDASARKAN JALUR PENERIMAAN MENGGUNAKAN ALGORITMA C4.5 (STUDI KASUS: UNIVERSITAS LAMPUNG)