IMPLEMENTASI ALGORITMA DECISION TREE C4.5 DENGAN IMPROVISASI MEAN DAN MEDIAN PADA DATASET NUMERIK

Bagus Sudirma

doi:10.69688/jikr.v2i1.17

Authors

Bagus Sudirma Universitas STEKOM Author

DOI:

https://doi.org/10.69688/jikr.v2i1.17

Keywords:

decision tree, algoritma C4.5, algoritma decision tree C4.5

Abstract

A decision tree algorithm or commonly called a decision tree is a classification method of data mining. The decision tree has one type of algorithm model, namely the C4.5 algorithm. The C4.5 decision tree algorithm is easy to understand because it has a tree-like structure in general. The C4.5 algorithm in handling quantitative data is often less efficient and effective. So to minimize information loss and time complexity, we can improvise the dataset on the numeric attributes when Preprocessing the data. Improvisation is done by using the mean and median on the numerical attributes to get a threshold value for implementing the C4.5 algorithm from the training data. Evaluation of the system used in this study uses a confusion matrix. Confusion matrix as a benchmark for testing the classification method using data testing. In this study, the dataset is partitioned into three scenarios. In scenario 1 with 70% training data and 20% testing data, the highest accuracy is 75%. The improvisation of the mean and median on the numerical attributes in the C4.5 algorithm can use in this scenario.

References

I. C. Wibowo, A. C. Fauzan, M. Dwi, P. Yustiana, and F. A. Qhabib, “Komparasi Algoritma Naive Bayes dan Decision tree Untuk Memprediksi Lama Studi Mahasiswa,” vol. 1, no. 2, pp. 65–74, 2019.

N. Azwanti, E. Elisa, U. P. Batam, and J. R. S. Kuning, “InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Analisis Pola Penyakit Hipertensi Menggunakan Algoritma C4 . 5,” vol. 2, 2019.

I. Massulloh and Fitriyani, “Implementasi Algoritma C4.5 Untuk Klasifikasi Anak Berkebutuhan Khusus Di Ibnu Sina Stimulasi Center,” eProsiding Sist. Inf., vol. 1, no. 1, pp. 136–144, 2020.

A. S. Budiman and X. A. Parandani, “Uji Akurasi Klasifikasi Dan Validasi Data Pada Penggunaan Metode Membership Function Dan Algoritma C4.5 Dalam Penilaian Penerima Beasiswa,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 1, pp. 565–578, 2018, doi: 10.24176/simet.v9i1.2021.

N. Cahyani and M. A. Muslim, “Increasing Accuracy of C4 . 5 Algorithm by Applying Discretization and Correlation-based Feature Selection for Chronic Kidney Disease Diagnosis,” vol. 12, no. 1, pp. 25–32, 2020.

A. Ferchichi, K. Nouira, and A. Cherfi, “MC4.5 decision tree algorithm: an improved use of continuous attributes,” Int. J. Comput. Intell. Stud., vol. 9, no. 1/2, p. 4, 2020, doi: 10.1504/ijcistudies.2020.10028137.

I. Setiawati, A. P. Wibowo, and A. Hermawan, “IMPLEMENTASI DECISION TREE UNTUK MENDIAGNOSIS PENYAKIT LIVER,” J. Inf. Syst. Manag., vol. 1, no. 1, pp. 13–17, 2019.

S. R. J. I. Alham, “Sistem Diagnosis Penyakit Jantung Koroner Dengan Menggunakan Algoritma C4.5 Berbasis Website (Studi Kasus: RSUD Dr. Soedarso Pontianak),” Petir, vol. 14, no. 2, pp. 214–222, 2021, doi: 10.33322/petir.v14i2.1338.

Suyanto, Data Mining Untuk Klasifikasi dan Klasterisasi Data, Revisi. Bandung: Informatika Bandung, 2019.

I. Yulianti, R. A. Saputra, M. S. Mardiyanto, and A. Rahmawati, “Optimasi Akurasi Algoritma C4.5 Berbasis Particle Swarm Optimization dengan Teknik Bagging pada Prediksi Penyakit Ginjal Kronis,” Techno.Com, vol. 19, no. 4, pp. 411–421, 2020, doi: 10.33633/tc.v19i4.3579.

N. A. Sinaga, A. T. Purba, K. Akuntansi, P. B. Indonesia, T. Komputer, and P. B. Indonesia, “Penerapan algoritma c.45 untuk memprediksi tingkat kepuasan mahasiswa terhadap politeknik bisnis indonesia,” vol. 4, pp. 245–254, 2021.

B. Santosa and A. Umam, Data Mining dan Big Data Analytics, 2nd ed. Yogyakarta: Penebar Media Pustaka, 2018.

H. D. Fahma and A. C. Fauzan, “Prediksi Keberlangsungan Studi Mahasiswa Fakultas Ilmu Pendidikan dan Sosial Universitas Nahdlatul Ulama Blitar,” vol. 1, no. 2, pp. 110–119, 2021.

H. H. Patel and P. Prajapati, “Study and Analysis of Decision tree Based Classification Algorithms,” Int. J. Comput. Sci. Eng., vol. 6, no. 10, pp. 74–78, 2018, doi: 10.26438/ijcse/v6i10.7478.

V. S. Ginting, Kusrini, and E. Taufiq, “Implementasi algoritma c4.5 untuk memprediksi keterlambatan pembayaran sumbangan pembangunan pendidikan sekolah menggunakan python,” vol. 10, pp. 36–44, 2020.