Application of C4.5 decision tree for scholarship evaluations

Wang, X.; Zhou, Chao; Xu, Xiaolong

doi:10.1016/j.procs.2019.04.027

Cited by 38 publications

(26 citation statements)

References 2 publications

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“…Metode C4.5 banyak digunakan pada penelitian di bidang pendidikan. Metode C4.5 untuk memprediksi pekerjaan lulusan perguruan tinggi serta menganalisa faktor-faktor yang mempengaruhi lulusan perguruan tinggi mendapatkan pekerjaan dan kualitas dari pekerjaan tersebut [5]. Algoritma C4.5 digunakan untuk memprediksi siswa dalam memperoleh bantuan danapendidikan [6].…”

Section: Pendahuluanunclassified

“…Memprediksi mahasiswa yang berpotensi non aktif menggunakan Data Mining dalam DecisionTree dan Algoritma C4.5 [9]. Mengatasi permasalahan pemberian beasiswa yang kurang adil dan tidak efisien dengan membuat sistem evaluasi beasiswa mahasiswa menggunakan pohon keputusan C4.5 [10]. Penerapan Metode C4.5 pada penelitian lainnya adalah sistem prediksi dan evaluasi prestasi akademik mahasiswa menggunakan Data Mining, di mana klasifikasi dilakukan untuk menemukan faktor-faktor yang mempengaruhi prestasi mahasiswa [11].…”

Section: Pendahuluanunclassified

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Prediksi Hasil Belajar Siswa Secara Daring pada Masa Pandemi COVID-19 Menggunakan Metode C4.5

2021

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Student learning in schools has changed since the Covid-19 pandemic. Student learning in normal conditions is carried out face-to-face and turns into online or online learning. The research was conducted to predict student learning outcomes during the COVID-19 pandemic so that the results of this study can be used as a reference in policymaking in schools. The C4.5 method was used in the study to classify the data for class XII of the Multimedia Department at SMKN 2 Padang Panjang and the classification results could predict student learning outcomes during the pandemic. Processed student value data were taken from 1 (one) subject as the research data sample. Analysis of the value of student learning outcomes using the C4.5 Method to obtain new knowledge from student learning outcomes data carried out during the COVID-19 pandemic. The data analyzed consisted of attributes of attendance, assignments, daily tests, and test scores which influenced the decision criteria for student learning outcomes in online learning. The learning outcome decision criteria consist of "Satisfactory" and "Not Satisfactory" which refer to the Minimum Completion Criteria. Tests conducted on the training data of learning outcomes show that the value of the Daily Test is the most influential attribute in decision making. Implementation of the results using the RapidMiner Studio 9.2.0 software and produces an accuracy of 83.33% of the test data testing with the rules of data analysis training results. The results of the C4.5 classification testing method in this study can be used to predict student learning outcomes. The test results with an accuracy of 83.33% can be recommended to help schools in making policies

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Section: Pendahuluanunclassified

Prediksi Hasil Belajar Siswa Secara Daring pada Masa Pandemi COVID-19 Menggunakan Metode C4.5

2021

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“…And the results are better using C4.5 [4]. The application of the C4.5 algorithm in evaluating scholarship granting to students has been conducted research [5]. In that study Wang et al have the objective to analyze the relationship between student performance and the scholarships provided and by using the C4.5 decision tree algorithm in the scholarship evaluation system it is hoped that the scholarships will be efficient and fair and can be realized.…”

Section: Related Workmentioning

confidence: 99%

Utilization of Data Mining to Predict Non-Performing Loan

Widiyono¹,

Isa²

2020

Adv. sci. technol. eng. syst. j.

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In the banking industry, the existence of problem loans is inevitable. NPL (Non-Performing Loan) will certainly have an impact on the reduction in the capital of a bank. One good step in reducing the risk of credit default or the emergence of non-performing loans is to take proper care of debtors who begin to experience payment constraints. The main obstacle experienced in bank management, especially in the credit sector, is being unable to identify or detect potential debtors early due to a large amount of data and manual processing. In this study, the debtor payment history is presented as data to predict the existence of problem loans. History payment can be used to predict bad loans. The technic of data mining in this experiment is a new method. The results of research conducted using Naïve Bayes, Decision Tree, K-NN, Rule Induction, Logistic Regression, Random Forest, Generalized Linear Model, and Gradient Boosted Trees as a comparison then choose the method that has the highest accuracy to be implemented in making additional modules on the core banking system. Random Forest is the model that has the highest accuracy of 96.55%.

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“…Teknik reduksi data yaitu memperoleh data volume kecil dari volume besar tetapi masih mempertahankan integritas data asli, Tujuan pengurangan fitur adalah untuk menghilangkan fitur yang tidak relevan [10]. Pemilihan fitur berdasarkan Gain Ratio menggunakan metode ranker.…”

Section: Splite Feature Reduction Modelunclassified

Analisis Kinerja dalam Mendeteksi Student Loses Berdasarkan Nilai Gain dengan Splite Feature Reduction Model pada Algoritma C4,5

Yusup¹,

Panjaitan²,

Yunis³

2020

Com, Engine, Sys, Sci

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Abstrak— Perguruan tinggi diharapkan mampu menghasilkan sumber daya manusia yang berpengetahuan juga kreatif. faktor penilaian perguruan tinggi adalah persentase kemampuan mahasiswa dalam menyelesaikan studi. Saat ini, masalah student loses dan faktor-faktor yang mempengaruhinya telah menjadi topik yang menarik untuk diteliti. Institusi perlu mendeteksi kinerja mahasiswa yang berstatus "student loses" sehingga dapat diidentifikasi. Dengan metode klasifikasi algoritma C4.5 dalam mengambil keputusan yang dianggap memiliki kinerja yang baik, tetapi algoritma C4.5 memiliki beberapa kelemahan terhadap data yang tidak seimbang, yaitu dataset yang berbeda dua kelas yang berbeda antara kelas mayoritas dan kelas minoritas, berpengaruh pada algoritma C4.5, yang menghasilkan nilai hasil akurasi kurang optimal pada algoritma C4,5 untuk mengurangi pengaruh ketidakseimbangan kelas pada klasifikasi C4.5, maka perlu dilakukan pendekatan metode Split Feature Reduction Model hal ini dilakukan untuk memberi bobot pada setiap fitur yang direkomendasikan, sehingga ditemukan fitur yang merupakan klasifikasi yang kuat, berdasarkan perangkingan pada setiap fitur agar dapat direkomendasikan pada klasifikasi C4.5 dalam proses klasifikasi Hasil menunjukkan bahwa, kinerja akurasi klasifikasi C4.5 pada dataset mahasiswa dengan pendekatan SFRM sebelum proses Pengujian, 10 fold cross-validation, menunjukkan tingkat akurasi klasifikasi yang lebih baik yaitu akurasi 98% dalam penanganan ketidakseimbangan kelas.

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Application of C4.5 decision tree for scholarship evaluations

Cited by 38 publications

References 2 publications

Prediksi Hasil Belajar Siswa Secara Daring pada Masa Pandemi COVID-19 Menggunakan Metode C4.5

Prediksi Hasil Belajar Siswa Secara Daring pada Masa Pandemi COVID-19 Menggunakan Metode C4.5

Utilization of Data Mining to Predict Non-Performing Loan

Analisis Kinerja dalam Mendeteksi Student Loses Berdasarkan Nilai Gain dengan Splite Feature Reduction Model pada Algoritma C4,5

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