skip to main content

Penerapan Algoritma C4.5 Untuk Memprediksi Keuntungan Pada PT SMOE Indonesia

*Tukino Tukino orcid  -  Universitas Putera Batam, Indonesia
Open Access Copyright (c) 2019 JSINBIS (Jurnal Sistem Informasi Bisnis)

Citation Format:
Abstract
In the construction project activities, planning is used as a reference for job implementers and becomes the standard of project implementation, including: documents, technical specifications, schedule and budget. Inappropriate planning, inaccurate project realization investigations, inadequate project management skills and lack of professional service providers, are closely related to the outcome of a construction project process. PT SMOE Indonesia which is a company engaged in construction consulting services. At the present time PT SMOE Indonesia has done many construction planning projects both from government and private, this research will discuss how data mining with algorithm C4.5 process data from budget plan consultant planner cost to predict company profit. Data mining is a technique for extracting new information from piles or data warehouses, as we know information is seen as something that is very important and valuable because by mastering information it is easy to achieve a desired goal, this makes everyone race to while C4.5 algorithm is one of induction algorithm of decision tree that is ID3 (Iterative Dichotomiser 3). ID3 was developed by J. Ross Quinlan. In the ID3 algorithm procedure, the inputs are training samples, training labels and attributes. which will illustrate the profit prediction, the results of this study will result in the rules of profit and loss decisions company.
Fulltext View|Download
Keywords: Profit; Data Mining; Algorithm C4.5; Tree Decision

Article Metrics:

  1. Faradillah, S., 2013. Implementasi data mining untuk pengenalan karakteristik transaksi customer dengan algoritma C4.5. Pelita Informatika Budi Darma, 63–70
  2. Gamarra, C., Guerrero, J. M., Montero, E., 2016. A knowledge discovery in databases approach for industrial microgrid planning. Renewable and Sustainable Energy Reviews, 60, 615–630
  3. Gunadi, G., Sensuse, D.I., 2012. Penerapan metode data mining market basket analysis terhadap data penjualan produk buku dengan menggunakan algoritma apriori dan frequent pattern growth FP-GROWTH ), 4(1)
  4. Haryati, S., Sudarsono, A., Suryana, E., 2015. Implementasi data mining untuk memprediksi masa studi mahasiswa menggunakan algoritma C4.5 (studi kasus: Universitas Dehasen Bengkulu). Jurnal Media Infotama Vol., 11(2), 130–138
  5. Lestari, S., Suryadi, A., 2014. Model klasifikasi kinerja dan seleksi dosen berprestasi dengan klasifikasi algoritma C.45. Proseding Seminar Bisnis & Teknologi, 15–16
  6. Santoso, H., Hariyadi, I. P., Prayitno, 2016. Data mining analisa pola pembelian produk. Teknik Informatika, (1), 19–24
  7. Wowor, A. S., Mangantar, M., 2012. Laba bersih dan tingkat risiko harga saham pengaruhnya terhadap Dividen pada perusahaan otomotif yang terdaftar Di bursa efek indonesia, 2(4), 13–23. https://doi.org/10.1002/eji.201444988
  8. Zulkifli, A., 2016. Metode C45 Untuk Mengklarifikasi Pelanggan Perusahaan Telekomunikasi Seluler, 2(1), 65–76

Last update:

  1. C4.5 Algorithm Application For Prediction 0f Customer Satisfaction Accuracy In PT. Pico Jaya Telesindo

    Tukino, Algifanri Maulana. 2021 International Conference on Computer Science and Engineering (IC2SE), 2021. doi: 10.1109/IC2SE52832.2021.9791939

Last update: 2024-03-19 14:30:54

No citation recorded.