Comparison of the K-Means Algorithm and C4.5 Against Sales Data

Authors

  • Eko Bambang Wijaya Universitas Prima Indonesia
  • Abdi Dharma Universitas Prima Indonesia
  • Daniel Heyneker Universitas Prima Indonesia
  • Jeff Vanness Universitas Prima Indonesia

DOI:

10.33395/sinkron.v8i2.12224

Abstract

In general, the process of collecting and grouping data requires a
long process. And if it has to be grouped manually it takes a very long time.
Therefore, data mining is a solution for clustering data - a lot of data to
classify it. In this research conducted at CV.Togu - Togu On Medan Branch,
data mining is applied using the K-Means process model and the C4.5
algorithm which provides a standard process for using data mining in various
fields used in classification because the results of this method easy to
understand and easy to interpret. . The K-means method is a non-herarical
method which is an algorithmic technique for grouping items into k clusters
by minimizing the distance of the SS (sum of square) to the cluster centroid.
In the K-means method, the number of clusters can be determined by the
researcher himself. And the testing methods used to measure cluster quality
are the Silhouette Coefficient and the Elbow Method. Based on the research
conducted, there are significant differences before and after using the two
methods. The results of the K-Means algorithm will be compared with the
results of the C4.5 algorithm in the form of rules (decision trees). This
research produces data on goods that have the highest level of
sales/behavior

GS Cited Analysis

Downloads

Download data is not yet available.

References

Damanik, I. S., Windarto, A. P., Wanto, A., Poningsih, Andani, S. R., & Saputra, W. (2019). Decision Tree Optimization in C4.5 Algorithm Using Genetic Algorithm. Journal of Physics: Conference Series, 1255(1). https://doi.org/10.1088/1742-6596/1255/1/012012

Hidayanti, I., Kurniawan, T. B., & Afriyudi, A. (2020). Perbandingan Dan Analisis Metode Klasifikasi Untuk Menentukan Konsentrasi Jurusan. Jurnal Ilmiah Informatika Global, 11(1), 16–21. https://doi.org/10.36982/jig.v11i1.1067

Kartikawati, L. (2022). Analisis Kualitas Pengelompokkan Algoritma K-Means di Knime dan Excel untuk PTMT Pasca Vaksinasi Covid-19. Ideguru: Jurnal Karya Ilmiah Guru, 7(1), 70–79. https://doi.org/10.51169/ideguru.v7i1.316

Lee, J. S. (2019). AUC4.5: AUC-Based C4.5 Decision Tree Algorithm for Imbalanced Data Classification. IEEE Access, 7, 106034–106042. https://doi.org/10.1109/ACCESS.2019.2931865

Mulyadin, I., & Winarso, D. S. (2019). Sistem Pendukung Keputusan Pemilihan Smartphone Menggunakan Metode Simple Additive Weighting. CAHAYAtech, 7(2), 88. https://doi.org/10.47047/ct.v7i2.13

Narulita, S., Oktaga, A. T., & Susanti, I. (2021). Pengujian Akurasi Model Prediksi Menggunakan Metode Data Mining Classification Decision Tree Algoritma C4 . 5. Jurnal Media Aplikom, 13(2), 15–29.

Nasution, F. M. (2019). Penerapan Metode K-Means Clustering Untuk Mengelompokkan Ketahanan Tanaman Pangan Kabupaten/Kota Diprovinsi Sumatera Utara. In Skripsi.

Paembonan, S., & Abduh, H. (2021). Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat. PENA TEKNIK: Jurnal Ilmiah Ilmu-Ilmu Teknik, 6(2), 48. https://doi.org/10.51557/pt_jiit.v6i2.659

Prasetyo, G. A., Santosa, R. G., & Chrismanto, A. R. (2017). Memprediksi Kategori Indeks Prestasi Mahasiswa. 5. https://doi.org/10.21460/jutei.2019.32.185

Pungky, A. (2019). Penerapan Metode K-Nn Untuk Memprediksi Hasil Pertanian Di Kabupaten Malang. JATI (Jurnal Mahasiswa Teknik Informatika), 3(1), 235–242.

Rozana, L., & Musfikar, R. (2020). Analisis Dan Perancangan Sistem Informasi Pengarsipan Surat Berbasis Web Pada Kantor Lurah Desa Dayah Tuha. Cyberspace: Jurnal Pendidikan Teknologi Informasi, 4(1), 14. https://doi.org/10.22373/cj.v4i1.6933

Siregar, M. H. (2018). Data Mining Klasterisasi Penjualan Alat-Alat Bangunan Menggunakan Metode K-Means (Studi Kasus Di Toko Adi Bangunan). Jurnal Teknologi Dan Open Source, 1(2), 83–91. https://doi.org/10.36378/jtos.v1i2.24

Sutoyo, M. N. (2019). Algoritma K-Means. 1, 1–7.

Wahyudi, I., Sulthan, M. B., & Suhartini, L. (2021). Analisa Penentuan Cluster Terbaik Pada Metode K-Means Menggunakan Elbow Terhadap Sentra Industri Produksi Di Pamekasan. Jurnal Aplikasi Teknologi Informasi Dan Manajemen (JATIM), 2(2), 72–81. https://doi.org/10.31102/jatim.v2i2.1274

Widiyati, D. K., Wati, M., & Pakpahan, H. S. (2018). Penerapan Algoritma ID3 Decision Tree Pada Penentuan Penerima Program Bantuan Pemerintah Daerah di Kabupaten Kutai Kartanegara. Jurnal Rekayasa Teknologi Informasi (JURTI), 2(2), 125. https://doi.org/10.30872/jurti.v2i2.1864

Downloads


Crossmark Updates

How to Cite

Wijaya, E. B., Dharma, A., Heyneker, D., & Vanness, J. . (2023). Comparison of the K-Means Algorithm and C4.5 Against Sales Data. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 741-751. https://doi.org/10.33395/sinkron.v8i2.12224

Issue

Vol. 8 No. 2 (2023): Research Article, Volume 8 Issue 2 April, 2023

Section

Articles

License

Copyright (c) 2023 Eko Bambang Wijaya, Abdi Dharma, Daniel Heyneker

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC