Competition in the higher education, especially private higher education (PTS) in the digital era, is becoming increasingly tough. In order to achieve the number of prospective new students, various methods are used so that the target for admitting the number of new students can be achieved in each new academic year. Providing a scholarship program is one way to attract the prospective new students. The awarding of a scholarship program must consider various possibilities such as the seriousness or commitment of the prospective new student. Refusal to grant scholarship programs can occur and become an obstacle for achieving the target. The prediction model through machine learning using some variables such as high school’s name, high school “category”, province or area of high school located, focus of specialization in high school, high school’s grade, type of parents income, and selected major of study in higher education. All of those variables will provides the probability values that will become an indicator that can be used to prioritize requests for scholarship program applications by taking into account the factors of acceptance or rejection from prospective students. Currently there is no measurement with accuracy of acceptance or rejection from prospective students. The purpose of this research is to build and compare machine learning models such as Logistic Regression, Artificial Neural Networks, Support Vector Machines, Decision Trees, Naïve Bayes, and K Nearest Neighbors so that a machine learning model is obtained that has the best predictions for awarding scholarship programs. The result of this research is that the Logistic Regression model has the highest model average accuracy value (62,05%) from training data compared to others. The highest accuracy of Logistic Regression model (62,29%) achieved based on the testing data. The highest AUC value (0,818) generated by Logistic Regression model which means the model is able to do the classification categorized “Good Classification” compare to other models.Persaingan di dalam dunia pendidikan tinggi secara khusus Perguruan Tinggi Swasta (PTS) terutama di era digital menjadi semakin ketat. Dalam memperebutkan jumlah calon mahasiswa baru yang tersedia, berbagai cara dilakukan agar target penerimaan jumlah mahasiswa baru dapat tercapai. Pemberian program beasiswa adalah salah satu cara menjaring calon mahasiswa baru. Pemberian program beasiswa harus mempertimbangkan berbagai kemungkinan seperti keseriusan atau komitmen sedangkan penolakan pemberian program beasiswa dapat juga terjadi dan menjadi kendala pada akhir suatu periode Penerimaan Mahasiswa Baru (PMB). Model prediksi melalui pembelajaran mesin dengan beberapa atribut seperti asal sekolah SMA, “Kategori Sekolah” SMA, provinsi atau daerah asal SMA, jurusan saat SMA yang diambil, nilai akademik SMA, jenis pekerjaan orang tua, dan pilihan program studi atau jurusan yang akan diambil saat nanti berkuliah pada akhirnya dapat memberikan suatu indikator nilai peluang atau kemungkinan penerimaan atau penolakan program beasiswa dari seorang calon mahasiswa baru. Saat ini belum ada usaha untuk memprediksi secara sistematis terhadap penerimaan / penolakan program beasiswa. Tujuan penelitian ini adalah membangun dan membandingkan model pembelajaran mesin seperti Logistic Regression, Artificial Neural Network, Support Vector Machine, Decision Tree, Naïve Bayes, dan K Nearest Neighbors sehingga didapatkan satu model pembelajaran mesin yang memiliki prediksi yang terbaik terhadap pemberian program beasiswa. Dari hasil penelitian maka model Logistic Regression memiliki nilai akurasi rata-rata tertinggi (62,05%) saat melakukan pembelajaran model dengan data latihan dibandingkan dengan model lainnya. Akurasi model Logistic Regression memiliki nilai tertinggi terhadap data uji sebesar (62,29%) dan juga memiliki nilai AUC (0.818) yang berarti bahwa model dapat melakukan pengklasifikasian dengan baik terhadap kelompok pengambilan keputusan dibandingkan dengan model lainnya
