Graduation Prediction System On Students Using C4.5 Algorithm

Kurniawan, Donny; Anggrawan, Anthony; Hairani, Hairani

doi:10.30812/matrik.v19i2.685

Cited by 13 publications

(11 citation statements)

References 4 publications

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“…Some previous studies in education have used data mining algorithms with a classification tree as their base learner. Kurniawan, Anggrawan, and Hairani (2020) proposed a graduation prediction system for undergraduate students of Bumigora University using a classification tree algorithm. However, their study was limited to a small dataset and one type of classifier.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Undergraduate Student’s Study Completion Status Using MissForest Imputation in Random Forest and XGBoost Models

Nirmala

Wijayanto

Notodiputro

2022

ComTech

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The number of higher education graduates in Indonesia is calculated based on their completion status. However, many undergraduate students have reached the maximum length of study, but their completion status is unknown. This condition becomes a problem in calculating the actual number of graduates as it is used as an indicator of higher education evaluation and other policy references. Therefore, the unknown completion status of the students who have reached the maximum length of study must be predicted. The research compared the performance of Random Forest and Extreme Gradient Boosting (XGBoost) classification models in predicting the unknown completion status. The research used a dataset containing 13.377 undergraduate students’ profiles from the Higher Education Database (PDDikti), Ministry of Education, Culture, Research, and Technology. The dataset was incomplete, and the proportion of missing data was 20,9% of the total data. Because missing data might lead to prediction bias, the research also used MissForest imputation to overcome the missing data in the classification modelling and compared it to Mean/Mode and Median/Mode imputation. The results show that MissForest outperforms the other two imputations in both classifiers but requires the longest computation time. Furthermore, the XGBoost model with MissForest is significantly superior to the Random Forest model with MissForest. Hence, the best model chosen to predict the completion status is XGBoost with MissForest imputation.

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Section: Introductionmentioning

confidence: 99%

Prediction of Undergraduate Student’s Study Completion Status Using MissForest Imputation in Random Forest and XGBoost Models

Nirmala

Wijayanto

Notodiputro

2022

ComTech

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“…Purnamasari et al (2019) also use C4.5 algorithms to explore how academic performance can impact the final graduation time of students. Kurniawan et al (2020) develop a graduation prediction system based on C4.5 algorithms. Meiriza et al (2020) leverage naive Bbayes classifier to analyse the influence of demographic features and academic performance on college graduation.…”

Section: Methods For Predicting Difficulty In Graduationmentioning

confidence: 99%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

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“…Universitas butuh mengetahui sikap mahasiswa, berikutnya faktor-faktor yang bisa menimbulkan kegagalan mahasiswa bisa ditinjau dengan lebih baik [14], [15]. Oleh karena itu, dengan kemampuan akademik yang rendah, usia masuk universitas, indeks prestasi ataupun faktor-faktor lainnya, bisa digunakan untuk menentukan tingkat kelulusan yang sesuai dengan masa studi.…”

Section: Pendahuluanunclassified

Perancangan Sistem Prediksi Kelulusan Mahasiswa Universitas Dian Nuswantoro Menggunakan Unified Modelling Language

Zeniarja

Salam²,

Ma’ruf³

2022

Restikom

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Mahasiswa ialah salah satu tiang yang sangat berarti dalam siklus hidup suatu Universitas. Jumlah kelulusan suatu Universitas sering kali mempunyai perbandingan yang kecil bila dibanding dengan jumlah mahasiswa yang diperoleh pada tahun akademik yang serupa. Tingkatan kelulusan mahasiswa yang kecil ini bisa disebabkan oleh sebagian aspek, seperti banyaknya aktivitas kemahasiswaan yang diiringi oleh aspek ekonomi, serta aspek – aspek lainnya. Perihal ini membuat sesuatu Universitas wajib mempunyai desain ataupun metode yang bisa memperhitungkan apakah mahasiswa itu bisa lulus dengan durasi yang sesuai. Salah satu faktor yang mendukung keberhasilan di dalam Univeritas adalah mahasiswa yang lulus dengan durasi yang sesuai. Semakin banyak mahasiswa yang lulus dengan durasi yang sesuai (dalam hal ini untuk sarjana yaitu <= 8 semester), harus berbanding yang sama atau lebih tinggi terhadap jumlah mahasiswa yang masuk pada suatu Universitas. Jika jumlah mahasiswa yang tidak lulus dengan durasi yang sesuai lebih tinggi, maka dapat menyebabkan lonjakan peningkatan jumlah data akademis dari semua mahasiswa yang masih terdaftar sehingga akan mempengaruhi citra dan reputasi dari Universitas yang nantinya dapat mengancam nilai akreditasi Universitas tersebut. Untuk mengatasi hal tersebut, maka diperlukan sistem yang dapat memprediksi kelulusan mahasiswa. Objek Penelitian ini dilakukan pada mahasiswa Universitas Dian Nuswantoro. Perancangan sistem prediksi menggunakan diagram Unified Modelling Language (UML). Diharapkan sistem prediksi kelulusan mahasiswa ini dapat berjalan optimal sehingga dapat memprediksi dan mengantisipasi secara dini profil kelulusan mahasiswa Universitas Dian Nuswantoro yang tidak sesuai meskipun di tengah wabah pandemi Covid-19.

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Graduation Prediction System On Students Using C4.5 Algorithm

Cited by 13 publications

References 4 publications

Prediction of Undergraduate Student’s Study Completion Status Using MissForest Imputation in Random Forest and XGBoost Models

Prediction of Undergraduate Student’s Study Completion Status Using MissForest Imputation in Random Forest and XGBoost Models

Educational Anomaly Analytics: Features, Methods, and Challenges

Perancangan Sistem Prediksi Kelulusan Mahasiswa Universitas Dian Nuswantoro Menggunakan Unified Modelling Language

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