Algoritma C4.5 Berbasis Seleksi Atribut Untuk Menentukan Kemungkinan Pengunduran Diri Mahasiswa

Hoiriyah, Hoiriyah

doi:10.31602/tji.v9i1.1104

Cited by 2 publications

(4 citation statements)

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“…dengan menggunakan atribut chi squared, Information Gain, Relief, dengan Criterion : Information Gain yang memiliki nilai akurasi yang tertinggi. Sehingga dapat dikatakan klasifikasi algoritma C4.5 menggunakan kriteria atribut Information gain adalah yang tertinggi untuk nilai akurasinya (Hoiriyah, 2018).…”

Section: Pendahuluanunclassified

Optimasi Algoritma C4.5 Untuk Mengukur Keputusan Pembelajaran Daring Berbasis Particle Swarm Optimization (PSO)

Wulandari

Masripah²,

Saputra³

2022

IJCIT

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Algoritma yang populer dan modern dalam pengolahan data dengan teknik data mining adalah Algoritma C4.5. Algoritma C4.5 banyak digunakan untuk melakukan pengklasifikasian data karena algoritma C4.5 dapat menghasilkan sebuah pohon keputusan yang mudah dipahami dan mudah dimengerti. Pada penelitian ini metode yang digunakan adalah dengan menambahkan teknik optimasi menggunakan algoritma Particle Swarm Optimization (PSO) dengan tujuan meningkatkan nilai akurasi pada information gain algoritma C4.5 untuk mengukur keputusan pembelajaran daring. PSO merupakan salah satu metode dan teknik untuk mengklasifikasi dan meningkatkan akurasi, dimana PSO terdiri dari sekumpulan partikel yang mencari posisi yang terbaik. Hasil penelitian menunjukkan akurasi dan Kappa pada nilai information gain yaitu sebesar 91,76 dan 0,834, akurasi dan Kappa Gain Ratio sebesar 89,41 dan 0,788, akurasi dan Kappa Gini Index sebesar 90,59 dan 0,811. Sehingga diperoleh kesimpulan penerapan algoritma PSO dapat berpengaruh terhadap nilai akurasi pada setiap criteria splitting algoritma C4.5 A popular and modern algorithm in data processing with data mining techniques is the C4.5 Algorithm. The C4.5 algorithm is widely used to classify data because the C4.5 algorithm can produce a decision tree and easy to understand. In this study, the author made a comparison between previous studies using the C4.5 algorithm by adding optimization techniques using the Particle Swarm Optimization (PSO) algorithm with the aim of increasing the accuracy value of the C4.5 algorithm information gain in measuring Online Learning Decisions. PSO Technique is one of the methods and techniques for classifying and improving accuracy, where PSO consists of a set of particles that are looking for the best position. The results of this study showed the results of accuracy and Kappa on the value of information gain, namely 91.76 and 0.834, accuracy and Kappa Gain Ratio of 89.41 and 0.788, accuracy and Kappa Gini Index of 90.59 and 0.811. So that it can be concluded that the application of the PSO algorithm can affect the accuracy value in each criteria splitting the C4.5 algorithm

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Section: Pendahuluanunclassified

Optimasi Algoritma C4.5 Untuk Mengukur Keputusan Pembelajaran Daring Berbasis Particle Swarm Optimization (PSO)

Wulandari

Masripah²,

Saputra³

2022

IJCIT

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“…Pemilihan atribut ini diambil berdasarkan irisan dari penelitian-penlitian sebelumnya yang dipresentasikan pada Tabel II. [13]. Berikut penjelasan tahapannya:…”

Section: A Pemilihan Atributunclassified

“…Data Validation dilakukan untuk melakukan seleksi pada data dengan menghilangkan noise dan outliers. Hasil akhirnya berupa data yang konsisten dan data lengkap atau tidak ada missing value [13].…”

Section: ) Data Validationunclassified

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Komparasi Model Prediksi Daftar Ulang Calon Mahasiswa Baru Menggunakan Metode Decision Tree Dan Adaboost

2021

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Every year, all the colleges hold new student enrollment. It is needed to start a new school academic year. Unfortunately, the number of students who resigned is considerably high to reach 837 students and caused 324 empty seats. The college’s stakeholders can minimize the resignation number if the selection phase of new students is done accurately. Making a machine learning-based model can be the answer. The model will help predict which candidates who potentially complete the enrollment process. By knowing it in the first place will help the management in the selection process. This prediction is based on historical data. Data is processed and used to train the model using the Adaboost algorithm. The performance comparison between Adaboost and Decision Tree model is performed to find the best model. To achieve the maximum performance of the model, feature selection is performed using chi-square calculation. The results of this research show that the performance of Decision Tree is lower than the performance of the Adaboost algorithm. The Adaboost model has f-measure score of 90.9%, precision 83.7%, and recall 99.5%. The process of analyzing the data distribution of prospective new students was also conducted. The results were obtained if prospective students who tended to finish the enrollment process had the following characteristics: graduated from an Islamic school, 19-21 years old, parents' income was IDR 1,000,000 to IDR. 5,000,000, and through the SBMPTN program.

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Algoritma C4.5 Berbasis Seleksi Atribut Untuk Menentukan Kemungkinan Pengunduran Diri Mahasiswa

Cited by 2 publications

References 0 publications

Optimasi Algoritma C4.5 Untuk Mengukur Keputusan Pembelajaran Daring Berbasis Particle Swarm Optimization (PSO)

Optimasi Algoritma C4.5 Untuk Mengukur Keputusan Pembelajaran Daring Berbasis Particle Swarm Optimization (PSO)

Komparasi Model Prediksi Daftar Ulang Calon Mahasiswa Baru Menggunakan Metode Decision Tree Dan Adaboost

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