Model Prediksi dengan Pembelajaran Mesin dalam Pemberian Program Beasiswa kepada Calon Mahasiswa Baru Program S1 di Perguruan Tinggi Swasta.

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

Comparative Analysis of Classification Performance for U.S. College Enrollment Predictive Modeling Using Four Machine Learning Algorithms (Artificial Neural Network, Decision Tree, Support Vector Machine, Logistic Regression)

Every year, the national high school graduation rate is declining and impacting the number of students applying to colleges. Moreover, the majority of students are applying to more than one college. This makes a lot of colleges to be highly competitive in student recruitment for enrollment and thus, the necessity for institutions to anticipate uncertainties related to budgets expected from student enrollment has increased. Hence enrollment management has become a pivotal sector in higher education institutions. Data and analytics are now a crucial part of enhancing enrollment management. Through big data analytics-driven solutions, institutions expect to improve enrollment by identifying students who are most likely to enroll in college. Machine learning can unlock significant value for colleges by allocating resources effectively to improve enrollment and budgeting. Therefore, a machine learning method is a vital tool for analyzing a large amount of data, and predictive analytics using this method has become a high demand in higher education. Yet higher education is still in the early stages of utilizing machine learning for enrollment management. In this study, I applied four machine learning algorithms to seven years of data on 108,798 students, each with 50 associated features, admitted to a 4-year, non-profit university in Midwest urban area to predict students\u27 college enrollment decisions. By treating the question of whether students offered admission will accept it as a binary classification problem, I implemented four machine learning algorithm classifiers and then evaluate the performance of these algorithms using the metrics of accuracy, sensitivity, specificity, precision, F-score, and area under the ROC and PR curves. The results from this study will indicate the best-performed prediction modeling of students’ college enrollment decisions. This research will expand the case and knowledge of utilizing machine learning methods in the higher education sector, focused on the U.S. College enrollment management field. Moreover, it will expand the knowledge of how the machine learning prediction model can be pragmatically used to support institutions in setting up student enrollment management strategies

Predictive Models of Student College Commitment Decisions Using Machine Learning

Every year, academic institutions invest considerable effort and substantial resources to influence, predict and understand the decision-making choices of applicants who have been offered admission. In this study, we applied several supervised machine learning techniques to four years of data on 11,001 students, each with 35 associated features, admitted to a small liberal arts college in California to predict student college commitment decisions. By treating the question of whether a student offered admission will accept it as a binary classification problem, we implemented a number of different classifiers and then evaluated the performance of these algorithms using the metrics of accuracy, precision, recall, F-measure and area under the receiver operator curve. The results from this study indicate that the logistic regression classifier performed best in modeling the student college commitment decision problem, i.e., predicting whether a student will accept an admission offer, with an AUC score of 79.6%. The significance of this research is that it demonstrates that many institutions could use machine learning algorithms to improve the accuracy of their estimates of entering class sizes, thus allowing more optimal allocation of resources and better control over net tuition revenue

Data-driven maintenance of military systems:Potential and challenges

The success of military missions is largely dependent on the reliability and availability of the systems that are used. In modern warfare, data is considered as an important weapon, both in offence and defence. However, collection and analysis of the proper data can also play a crucial role in reducing the number of system failures, and thus increase the system availability and military performance considerably. In this chapter, the concept of data-driven maintenance will be introduced. First, the various maturity levels, ranging from detection of failures and automated diagnostics to advanced condition monitoring and predictive maintenance are introduced. Then, the different types of data and associated decisions are discussed. And finally, six practical cases from the Dutch MoD will be used to demonstrate the benefits of this concept and discuss the challenges that are encountered in applying this in military practice

Towards a data-driven military: a multi-disciplinary perspective

Towards a data-driven military. A multi-disciplinary perspective assesses the use of data and information on modern conflict from different scientific and methodological disciplines, aiming to generate valuable contributions to the ongoing discourse on data, the military and modern warfare. Military Systems and Technology approaches the theme empirically by researching how data can enhance the utility of military materiel and subsequently accelerate the decision-making process. War Studies take a multidisciplinary approach to the evolution of warfare, while Military Management Studies take a holistic organisational and procedural approach. Based on their scientific protocols and research methods, the three domains put forward different research questions and perspectives, providing the unique character of this book

Sustenabilitatea educației doctorale în economie și afaceri

Volumul ”Sustenabilitatea educației doctorale în economie și afaceri” valorifică ideile și cercetările doctoranzilor de la Universitatea “Alexandru Ioan Cuza” din Iași, școala doctorală de economie și administrarea afacerilor. Lucrările au fost prezentate, prin postere sau în plen, în conferința finală a proiectului SESYR, finanțat prin programul european Jean Monnet. Structurarea volumului în patru subcapitole generice are ca scop valorificarea domeniilor considerate prin filosofia proiectului:managementul proiectelor, antreprenoriat si angajabilitate pentru tinerii cercetători. O colecție de 24 de articole având 35 de autori, oferă un mediu de dezbatere științifică provocatoare pentru publicul cititor din domeniul economic. Focalizarea subiectelor din articolele prezente pe motivațiile de cercetare ale doctoranzilor și postdoctoranzilor face ca acest volum să reprezinte un debut publicistic pentru unii autori iar pentru alții, o consolidare a vocației. Diseminarea pasiunilor în astfel de contexte consolidează colaborarea și deschiderea spre noi subiecte investigative. Volumul este destinat studenților, cercetătorilor și profesorilor și îl propunem ca reper bibliografic pentru dezvoltarea altor idei de cercetare și inovare în arealul nostru tematic