KLASIFIKASI PENERIMAAN MOBIL BEKAS BERDASARKAN METODE NEURAL NETWORK

  • Akmal Dirgantara Ilmu Komputer STMIK Nusa Mandiri
  • Syarifudin Herdyansyah Ilmu Komputer STMIK Nusa Mandiri
  • Rasenda Rasenda Ilmu Komputer STMIK Nusa Mandiri
Keywords: Neural Network, Used Car, Backpropagation, Classification

Abstract

Considering the need for cars in big cities is increasing and the price of 4-wheeled vehicles is relatively expensive, used cars are a good alternative solution to offer. But in business we cannot just buy stock for a car showroom, for example, especially with a limited budget, car showroom entrepreneurs must do a thorough analysis to save the budget to open a showroom and prevent losses. This article helps open the car showroom to determine which cars are suitable as stock to be displayed in the showroom with the parameters of buying, maintenance, doors, boot lugs, and safety. That predicted using 1728 artificial neural network methods data obtained from the UCI repository with a fairly high degree of accuracy that is equal to 98.26%. This is quite efficient compared to the showroom owner who must conduct a survey in advance and ask 1 per 1 person to survey the interest in receiving a car.

Downloads

Download data is not yet available.

References

Agustin, M., & Prahasto, T. (2012). Penggunaan Jaringan Syaraf Tiruan Backpropagation Untuk Seleksi Penerimaan Mahasiswa Baru Pada Jurusan Teknik Komputer Di Politeknik Negeri Sriwijaya. JURNAL SISTEM INFORMASI BISNIS, 2(2). https://doi.org/10.21456/vol2iss2pp089-097

Ariawan, I. (2011). Kurva Receiver Operating Characteristic. Retrieved from https://id.scribd.com/doc/15123416/Kurva-Receiver-Operating-Characteristic

Dirgantara, A., Herdyansyah, S., & Rasenda, R. (2019). Laporan Penelitian: Klasifikasi Penerimaan Mobil Bekas Berdasarkan Metode Neural Network. Jakarta.

Guntoro, G., Costaner, L., & Lisnawita, L. (2019). Prediksi Jumlah Kendaraan di Provinsi Riau Menggunakan Metode Backpropagation. Nformatika Mulawarman: Jurnal Ilmiah Ilmu Komputer, 14(1), 51–57. Retrieved from http://e-journals.unmul.ac.id/index.php/JIM/article/view/1745

Pakaja, F., Naba, A., & Purwanto, P. (2012). Peramalan Penjualan Mobil Menggunakan Jaringan Syaraf Tiruan dan Certainty Factor. Jurnal EECCIS, 6(1), 23–28. Retrieved from https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/162

Sucipto, A. (2012). CREDIT PREDICTION WITH NEURAL NETWORK ALGORITHM Ir . Adi Sucipto , M . Kom . Sains and Technology Faculty Universitas Islam Nahdlatul Ulama Jepara, (15), 978–979.

Suhendra, C. D., & Wardoyo, R. (2015). Penentuan Arsitektur Jaringan Syaraf Tiruan Backpropagation (Bobot Awal dan Bias Awal) Menggunakan Algoritma Genetika. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 9(1), 77. https://doi.org/10.22146/ijccs.6642

Vercellis, C. (2009). Business Intelligence: Data Mining and Optimization for Decision Making. Business Intelligence: Data Mining and Optimization for Decision Making. https://doi.org/10.1002/9780470753866
Published
2020-02-12
How to Cite
Dirgantara, A., Herdyansyah, S., & Rasenda, R. (2020). KLASIFIKASI PENERIMAAN MOBIL BEKAS BERDASARKAN METODE NEURAL NETWORK. Jurnal Riset Informatika, 2(1), 43-48. https://doi.org/10.34288/jri.v2i1.119
Article Metrics

Abstract viewed = 45 times
PDF downloaded = 24 times