Sistem Prediksi Ketepatan Kelulusan Mahasiswa Berdasarkan Data Akademik Dan Non Akademik Menggunakan Metode K-Means (Studi Kasus : Universitas Catur Insan Cendekia)

Fernanda, Mochamad Rizky Alvin; Sokibi, Petrus; Fahrudin, Rifqi

doi:10.51920/jd.v11i1.182

Cited by 2 publications

(4 citation statements)

References 3 publications

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“…Dalam penelitian ini penulis akan membuatkan prediksi tingkat kelulusan mahasiswa menggunakan data mining dengan metode K-means pada program studi informatika Universitas Muhammadiyah Makassar yang akan mempercepat prediksi kelulusan mahasiswa agar mahasiswa dapat membuat strategi atau merencanakan sesuatu untuk dapat melihat apa yang harus dilakukan suatu mahasiswa agar ke depannya bisa lulus tepat waktu dan memperkirakan tidak terjadinya Drop Out (DO) pada suatu mahasiswa. [3].…”

Section: Pada Program Studi Informatika Universitasunclassified

Prediksi Tingkat Kelulusan Menggunakan K-Means Pada Program Studi Informatika Unismuh Makassar

Irhamna Rachman,

Mujadilah,

Wahyuni

et al. 2023

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Predicting timely graduation brings numerous benefits not only to students but also to the university itself. Creating a graduation prediction model assists students and academic advisors in fostering a positive environment that encourages on-time graduation by developing a predictive model for graduation rates using the K-means data mining method in the Informatics study program at Universitas Muhammadiyah Makassar. This method is used to cluster students based on attributes such as total credits taken, semester Grade Point Average (GPA), and overall Cumulative Grade Point Average (CGPA). The clustering aims to identify patterns and characteristics of student graduation. Data from several semesters is collected and preprocessed, including data normalization and transformation. The research steps involve data preprocessing, cluster labeling, distance calculation to cluster centers, and result analysis. The analysis shows that the K-means method can generate student clusters with varying graduation rate patterns. The formed clusters can be interpreted as groups of students with potential for timely graduation or groups needing more attention to achieve on-time graduation. Empirical validation is performed by comparing K-means prediction results with actual graduation data. Accuracy measurement involves calculating the percentage of similarity between predictions and actual data. Empirical validation results demonstrate the accuracy level, which can serve as a benchmark for assessing the performance of this prediction model. This study aims to provide deeper insights into factors influencing student graduation and potentially support decision-making at the academic level. Keywords: Graduation Prediction, Data Mining, K-Means, Analysis, Clustering, Empirical Validation.

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Section: Pada Program Studi Informatika Universitasunclassified

Prediksi Tingkat Kelulusan Menggunakan K-Means Pada Program Studi Informatika Unismuh Makassar

Irhamna Rachman,

Mujadilah,

Wahyuni

et al. 2023

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“…Prediksi merupakan suatu kegiatan yang dilakukan secara sistematis dan bertujuan untuk memperkirakan suatu hal yang mungkin terjadi di masa depan. Proses prediksi dapat diketahui berdasarkan informasi di masa lalu, sehingga ditemukan perbedaan antara sesuatu yang telah terjadi dan hasil yang diharapkan dapat diminimalisir [3]. Hubungan antara masa studi mahasiswa dengan kelulusan dapat diprediksi menggunakan data riwayat akademik mahasiswa di masa lalu [1].…”

Section: Pendahuluanunclassified

“…Penelitian selanjutnya yang dilaporkan oleh [7] penggunaan data induk mahasiswa sebagai prediktor ketepatan waktu lulus dan algoritma CART untuk pengklasifikasiannya diperoleh nilai accuracy: 63.19%, specificity: 54.48% dan sensitivity: 69.94%. Sedangkan penelitian [3] menggunakan data akademik dan non akademik dalam memprediksi ketepatan kelulusan mahasiswa menggunakan metode K-Means memperoleh nilai dengan tingkat akurasi mencapai 90% benar. Selanjutnya penelitian untuk mencari model prediksi ketepatan masa studi mahasiswa [8] dengan menerapkan fitur Forward Selection pada algoritma Naïve Bayes menghasilkan nilai akurasi yang cukup tinggi yaitu 92,94%.…”

Section: Pendahuluanunclassified

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Implementasi Algoritma Naïve Bayes Menggunakan Feature Forward Selection dan SMOTE Untuk Memprediksi Ketepatan Masa Studi Mahasiswa Sarjana

Kurniadi

Nuraeni

Lestari

2022

JSC

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The punctuality of students in completing their studies is an important aspect of the study program. Because there are still students who have not been able to complete their studies on time. The purpose of this study is to determine the factors that influence students in completing their studies by extracting student academic data to obtain a classification model that can be used to predict the accuracy of the study period. The classification method for predicting the accuracy of the student's study period uses the Naive Bayes algorithm using the Feature Forward Selection and SMOTE. The method for data processing in this study uses CRISP-DM. The results of this study are in the form of a classification model to predict the accuracy of the study period of students who obtain a fairly high accuracy value of 87.13%, a recall value of 83.82%, and a precision value of 89.76%, and an AUC value of 0.92. included in the category of Excellent Classification. The use of SMOTE has succeeded in handling Imbalanced Class on the data, and the application of Feature Forward Selection resulted in 5 factors that most influence the accuracy of the student's study period, namely the attributes of Gender, School Category, Year of Entry, Study Program and Grade Point Average for the third semester. The prediction model generated using the Naïve Bayes algorithm, Feature Forward Selection, and SMOTE is expected to help study programs to find out earlier the possibility of students completing their studies on time or not on time.

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Sistem Prediksi Ketepatan Kelulusan Mahasiswa Berdasarkan Data Akademik Dan Non Akademik Menggunakan Metode K-Means (Studi Kasus : Universitas Catur Insan Cendekia)

Cited by 2 publications

References 3 publications

Prediksi Tingkat Kelulusan Menggunakan K-Means Pada Program Studi Informatika Unismuh Makassar

Prediksi Tingkat Kelulusan Menggunakan K-Means Pada Program Studi Informatika Unismuh Makassar

Implementasi Algoritma Naïve Bayes Menggunakan Feature Forward Selection dan SMOTE Untuk Memprediksi Ketepatan Masa Studi Mahasiswa Sarjana

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