Analisa Algoritma C4.5 Untuk Memprediksi Penjualan Motor Pada Pt. Capella Dinamik Nusantara Cabang Muka Kuning

Azwanti, Nurul

doi:10.30872/jim.v13i1.629

Cited by 37 publications

(41 citation statements)

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“…Pohon keputusan menggunakan struktur representasi pohon ( tree) dimana setiap node merepresentasikan atribut, cabangnya merepresentasikan nilai atribut dan daun merepresentasikan kelas [7]. Pohon keputusan merupakan representasi sederhana dari teknik klasifikasi untuk sejumlah kelas berhingga, dimana simpul internal maupun akar ditandai dengan nama atribut, rusuk-rusuknya diberi label nilai atribut yang mungkin dan simpul daun ditandai dengan kelas-kelas yang berbeda [5].…”

Section: Pohon Keputusan (Decision Tree)unclassified

Prediksi Kelulusan Mahasiswa Stikom Tunas Bangsa Prodi Sistem Informasi Dengan Menggunakan Algoritma C4.5

2021

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Graduation is an important element in an accreditation assessment process of an institution or university. It is important to find out information about the predictions of student graduation in the Information Systems Study Program at STIKOM Tunas Bangsa Pematangsiantar, so that students who cannot graduate on time can be identified earlier. The application of data mining can be used to predict student graduation. Method that often used to predict student graduation is classification method. This research using C4.5 Algorithm. Data that used as training data are from alumni of Information Systems Study Program at STIKOM Tunas Bangsa and the final level student data of Information Systems Study Program at STIKOM Tunas Bangsa used as testing data. Through this research, it is expected that the results can provide information on predictions of student graduation on time and as a suggestion for the university in making good decisions for improvement in the future.

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Section: Pohon Keputusan (Decision Tree)unclassified

Prediksi Kelulusan Mahasiswa Stikom Tunas Bangsa Prodi Sistem Informasi Dengan Menggunakan Algoritma C4.5

2021

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“…Algoritma C4.5 dianggap sebagai [5] algoritma yang sangat membantu dalam melakukan klasifikasi data karena karakteristik data yang diklasifikasi dapat diperoleh dengan jelas, baik dalam bentuk struktur pohon keputusan maupun aturan if-then sehingga memudahkan pengguna dalam melakukan penggalian informasi terhadap data yang bersangkutan [6].…”

Section: Algoritma C45unclassified

Analisis Faktor Kompetensi Pedagogik Guru Dalam Proses Pembelajaran Menggunakan Algoritma C45

2021

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Pedadogic competencies are the competencies that teachers must have in managing learning so that the process can run well. Mining data is a series of processes to explore the added value obtained from information or knowledge that can form a decision pattern that will later be used in analyzing pedagogical competency factors the teacher in the learning process for one semester is based on the criteria of understanding students, designing learning, evaluating learning outcomes, learning that educates and dialogues and develops students to share their potential. Pedagogic assessment is done by summing the value of each criterion and using a standard value to determine the final value. The application of the c45 algorithm can be used to prodece a decission tree that will be taken to determine the form of rules or rules that will be used as a decision tree

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“…From previous studies, researchers found several studies that discussed topics related to research researchers, including the use of the C4.5 method to predict various things and the use of RFM analysis as a segmentation method. first research [16] C4.5 algorithm with the decision tree method can provide predictive rule information to describe the processes associated with predicting motorcycle sales. C4.5 algorithm is considered as an algorithm which is very helpful in classifying data because the characteristics of classified data can be obtained, both in the form of decision tree structures and if-then rules, making it easier for users to extract information from the relevant data.…”

Section: Previous Researchmentioning

confidence: 99%

Prediction using C4.5 Method and RFM Method for Selling Furniture

Budi*,

Ranggadara,

Prihandi

et al. 2019

IJEAT

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Based on sales transaction data in Borobudur Furniture, it can be seen that customer demand for furniture can be said to be large, therefore special methods are needed to estimate sales that are most in-demand by customers in the future, and also special methods used to provide customer loyalty ratings. The method used to predict sales is the C4.5 method, while the method used to provide customer ratings based on customer loyalty is the RFM method. Through the process of data mining with the C4.5 method, it was found that the five items most in demand by customers were wardrobe, office chairs, buffet tv, guest table, and sofa set. While using RapidMiner as a test, the precision results are 63.64%, 89.36% for recall and 60.81% for accuracy. While through the RFM analysis process that has been carried out, there are four categories of customers and the minimum RFM total point is 3 points while the maximum RFM total point is 12 points.

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Analisa Algoritma C4.5 Untuk Memprediksi Penjualan Motor Pada Pt. Capella Dinamik Nusantara Cabang Muka Kuning

Cited by 37 publications

References 0 publications

Prediksi Kelulusan Mahasiswa Stikom Tunas Bangsa Prodi Sistem Informasi Dengan Menggunakan Algoritma C4.5

Prediksi Kelulusan Mahasiswa Stikom Tunas Bangsa Prodi Sistem Informasi Dengan Menggunakan Algoritma C4.5

Analisis Faktor Kompetensi Pedagogik Guru Dalam Proses Pembelajaran Menggunakan Algoritma C45

Prediction using C4.5 Method and RFM Method for Selling Furniture

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