<p><em>Abstract</em></p><p><em>Tracking college </em><em>graduates</em><em> needs to be done to find out how their current job status is, especially the waiting time to get a job, as an indicator of the quality of college graduates. Information technology support such as data mining can be used as tools to generate knowledge. This study aims to predict the waiting time for alumni to get a job using a single decision tree algorithm (C4.5) and compare the decision tree algorithm (C4.5) with the forward selection feature. Data processing uses the C4.5 algorithm with the help of RapidMiner Studio software. The results show that the decision tree algorithm (C4.5) with the forward selection feature achieves the best performance with 80.37% accuracy, 79.56% precision, 81.34% recall, 80.40% f-measure and 0.914 AUC which includes into the excellent classification category. Thus, the C4.5 algorithm based on Forward Selection is proven to increase the level of accuracy, compared to a single decision tree (C4.5) algorithm, which is characterized by an increase in the accuracy value of 25.93%.</em></p><p><em>Keywords: Data Mining, Decision Tree, C4.5, Classification, </em><em>Graduates </em><em>Waiting Time</em></p><p> </p><p><em>Abstrak</em></p><p><em>P</em><em>elacakan </em><em>alumni perguruan tinggi </em><em>perlu dilakukan untuk mengetahui bagaimana status pekerjaan </em><em>mereka</em><em> saat ini</em><em>, khususnya waktu tunggu dalam mendapatkan pekerjaan, sebagai salah indikator kualitas lulusan perguruan tinggi</em><em>. </em><em>Dukungan teknologi informasi seperti </em><em>data mining </em><em>dapat digunakan sebagai tools </em><em>untuk menghasilkan suatu pengetahuan. Penelitian ini </em><em> </em><em>bertujuan untuk memprediksi waktu tunggu alumni mendapatkan pekerjaannya dengan menggunakan algoritma decision tree (C4.5) tunggal dan dibandingkan algoritma decision tree (C4.5) dengan fitur forward selection. </em><em>Pengolahan data menggunakan algoritma C4.5 dengan bantuan software RapidMiner Studio. </em><em>Hasilnya menunjukkan bahwa algoritma decision tree (C4.5) dengan fitur forward selection meraih performa terbaik dengan nilai accuracy 80,37%, precision 79,56%, recall 81,34%, f-measure 80,40% dan AUC 0.914 yang termasuk ke dalam kategori excellent classification.</em><em> </em><em>Dengan demikian,</em><em> algoritma C4.5 berbasis Forward Selection terbukti dapat meningkatkan tingkat akurasi, dibandingkan dengan algoritma decision tree (C4.5) tunggal, yang ditandai dengan peningkatan nilai akurasi sebesar 25,93%</em></p><p><strong><em>Kata </em></strong><strong><em>k</em></strong><strong><em>unci: </em></strong><em>Data Mining, Decision Tree, C4.5, Klasifikasi, Waktu Tunggu</em><em> Alumni</em><em> </em></p>
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