Information Systems Students' Study Performance Prediction Using Data Mining Approach

Gunawan,; Hanes,; Catherine,

doi:10.1109/icic47613.2019.8985718

Cited by 11 publications

(10 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Decision Tree C4.5 method produces information that the graduation status of prospective teachers (on time and late) is based on the attributes of age, GPA, IPS 1, IPS 2, IPS 3, IPS 4, and gender. This information is in line with the results of previous studies that stated that age attributes (Arifin & Hadiana, 2019); GPA and gender jelnis (Gunawan et al, 2019;D. Y. Putri et al, 2018); and the student Semester Achievement Index (Fitrani, 2019) are attributes that affect the classification of a student's graduation status.…”

Section: Discussionsupporting

confidence: 91%

Prediction model of teacher candidate student graduation status: Decision Tree C4.5, Naive Bayes, and k-NN

Kartianom

Arpandi

Kassymova

et al. 2022

Ekspose J. Penelit. Huk. dan Pendidik.

View full text Add to dashboard Cite

This study aims to determine the prediction model of the graduation status of prospective teacher students at IAIN Bone in terms of attributes, accuracy levels, and differences in the level of accuracy produced in the attributes of decision tree C4.5, Naïve Bayes, and k-NN data mining algorithms. This research uses a quantitative approach by adopting the Data Mining method. This research was conducted at IAIN Bone. The data collection process in this study used documentation techniques in the form of data on alumni of the Tarbiyah Faculty of IAIN Bone. The data analysis used was a descriptive analysis using decision tree C4.5, Naive Bayes, and k-NN data mining algorithms assisted by the RapidMiner application. The results of this study show that (1) model prediction of the graduation status of prospective teacher students in IAIN Bone in terms of attributes generated in the Decision Tree C4.5 and Naïve Bayes data mining algorithms consist of gender, age, Semester 1 IP, Semester 2 IP, Semester 3 IP, Semester 4 IP, and GPA, while the attributes produced in k-NN data mining algorithm consists of gender, regional origin, number of siblings, age, IP Semester 1, IP Semester 2, IP Semester 3, IP Semester 4, and GPA; (2) model prediction of graduation status of iain bone teacher candidate students in terms of the accuracy rate generated in the Decision Tree C4.5 data mining algorithm of 93.90%, Naïve Bayes by 90.24%, and k-NN of 92.07%; and (3) there was no significant difference between the accuracy rate produced by decision tree's data mining algorithm. C4.5 and Naïve Bayes (p-value = 1.00); Decision Tree C4.5 and k-NN (p-value = 1.00); as well as Naïve Bayes and k-NN (p-value = 1.00) in predicting the graduation status of iain bone teacher candidate students.

show abstract

Section: Discussionsupporting

confidence: 91%

Prediction model of teacher candidate student graduation status: Decision Tree C4.5, Naive Bayes, and k-NN

Kartianom

Arpandi

Kassymova

et al. 2022

Ekspose J. Penelit. Huk. dan Pendidik.

View full text Add to dashboard Cite

show abstract

“…Di tiap dini tahun akademik, kerap terjalin tidak seimbangnya perbandingan mahasiswa baru yang masuk dengan mahasiswa yang lulus. Tidak hanya itu, lonjakan penerimaan mahasiswa baru kadangkala tidak balance dengan jumlah mahasiswa yang lulus dengan masa studi 4 tahun (8 semester) [5], [16], [17]. Bahkan, sekarang ada aturan baru dari pemerintah bahwa masa aktif mahasiswa S1 hanya 7 tahun.…”

Section: Pendahuluanunclassified

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

Zeniarja

Salam²,

Ma’ruf³

2022

Restikom

View full text Add to dashboard Cite

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.

show abstract

“…Reference Investigation about information Systems Students' Study Performance Prediction Using Data Mining Approach [16] Work about predicting Academic Performance of Students in the UAE Using Data Mining Techniques [19] Review of predicting student performance in higher education institutions using decision tree analysis [18] Research on prediction Model for Classifying Students Based on Performance using Machine Learning Techniques [2] Work about educational Data Mining & Students' Performance Prediction [1] Study about predicting Critical Courses Affecting Students' Performance: A Case Study [7] Investigation about tracking Student Performance in Introductory Programming by Means of Machine Learning [21] Table 3. Based on the inclusions and exclusions considered, the process for searching for the articles is shown in:…”

Section: Descriptionmentioning

confidence: 99%

“…Article TP01 One-R [21] TP02 C4.5 [8], [28], [23], [1], [10], [16], [ 34] TP03 Naive Bayes (NB) [2], [21], [19], [8], [28], [25], [ 5] , [12], [23], [5], [31], [32], [1 ], [33], [10], [36], [40] TP04 Bayes Networks [19], [5] TP05 Decision trees [35], [17], [33], [24], [31] TP06 Neural Networks (MLPs) [2], [21], [8], [28], [17], [32], [5], [31], [15], [33], [36], [6], [40] TP07 Vector Support Machine (SVM) [2], [13], [12], [15],…”

Section: Code Techniquementioning

confidence: 99%

See 1 more Smart Citation

Predicting Academic Performance through Data Mining: A Systematic Literature

Daza¹,

Guerra²,

Cervera³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

The main objective of this work is to make a systematic review of the literature on the prediction of the academic performance of university students by applying data mining techniques. For this purpose, an exhaustive search was carried out and after the analysis of the documentation collected, aspects such as: methodology, attributes, selection algorithms, techniques, tools, and metrics were considered, which served as the basis for the elaboration of this document. The results of the study showed that the most used methodology is KDD(database knowledge extraction), the most important attribute to achieve prediction is CGPA(academic performance), the most commonly used variable selection algorithm is InfoGain-AttributeEval, among the most efficient techniques are Naïve Bayes, Neural Networks (MLP) and Decision Tree (J48), the most used tools for the development of the models is the Weka software and finally the metrics necessary to determine the effectiveness of the model were Precision and Recall.

show abstract

Information Systems Students' Study Performance Prediction Using Data Mining Approach

Cited by 11 publications

References 25 publications

Prediction model of teacher candidate student graduation status: Decision Tree C4.5, Naive Bayes, and k-NN

Prediction model of teacher candidate student graduation status: Decision Tree C4.5, Naive Bayes, and k-NN

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

Predicting Academic Performance through Data Mining: A Systematic Literature

Contact Info

Product

Resources

About