Implementation of Gain Ratio on KNN Method in Predicting Sales of Electronic Sparepart at Panasonic Service Center Lhokseumawe

Authors

  • Samsul Bahri Siagian Prodi Sistem Informasi,Universitas Islam Negeri Sumatera Utara Medan
  • Samsudin Samsudin Prodi Sistem Informasi,Universitas Islam Negeri Sumatera Utara Medan
  • Muhammad Dedi Irawan Prodi Sistem Informasi,Universitas Islam Negeri Sumatera Utara Medan

DOI:

https://doi.org/10.35335/cit.Vol14.2022.242.pp36-47

Keywords:

Data Mining, Gain Ratio, K-Nearest Neighbor, Predicting Sales

Abstract

K-Nearest Neighbor is a good classification technique, but judging by previous studies, the accuracy of the KNN performance obtained is still inferior to other methods. in the classification process, if some characteristics are not good it can cause errors in the new classifier. As for this study, the researcher uses the gain ratio method as a parameter to see the correlation between each attribute in the dataset, and the gain ratio serves as a weighting for each attribute so as to produce a dataset. the correct way of classifying data using the KNN method, this study is very suitable for predicting sales of spare parts at the Panasonic Service Center company, where the company experienced a decline in sales, this research is very useful for predicting sales for the following month. The results of this study produce very precise predictions of distance with an accuracy value of 13%, where the comparison of the highest accuracy value is found in the total attribute with an accuracy distance of 13%, while the lowest accuracy difference is obtained in the month and type of sales dataset with 0.08%. the overall accuracy of all datasets increases by 100% with K=3, and K=5 gets 80% accuracy. so this method can be used to make sales predictions to make it easier for the company.

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Author Biographies

Samsul Bahri Siagian, Prodi Sistem Informasi,Universitas Islam Negeri Sumatera Utara Medan

Mahasiswa Prodi Sistem Informasi, UINSU Medan

Samsudin Samsudin, Prodi Sistem Informasi,Universitas Islam Negeri Sumatera Utara Medan

Ketua program studi sistem informasi, fakultas sains dan teknologi, Samsudin, ST, M.Kom

Muhammad Dedi Irawan, Prodi Sistem Informasi,Universitas Islam Negeri Sumatera Utara Medan

Dosen Prodi Sistem Informasi, Fakultas Sains dan Teknologi, UINSU Medan, Muhammad Dedi Irawan, M.Kom

References

A. A. Nababan, O. S. Sitompul, And Tulus, “Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio,†J. Phys. Conf. Ser., Vol. 1007, No. 1, Pp. 1–6, 2018, Doi: 10.1088/1742-6596/1007/1/012007.

Aryati, Samsudin, And M.Fakhriza, “Sistem Seleksi Penerimaan Tenaga Kerja Outsourcing Menggunakan Algoritma C5.0 Berbasis Android (Studi Kasus : Pt. Sinergi Indo Prima Medan) .,†J. Teknol. Dan Sist. Inf. Univrab, Vol. 7, No. 1, Pp. 52–63, 2022.

G. Rizka, U. Sinaga, And Samsudin, “Implementasi Framework Laravel Dalam Sistem Reservasi Pada Restoran Cindelaras Kota Medan,†J. Janitra Inform. Dan Sist. Inf., Vol. 1, No. 2, Pp. 73–84, 2021, Doi: 10.25008/Janitra.V1i2.131.

S. Siagian And M. Adilla, “Analisis Sistem Penjualan Pemasaran Produk Pada Pt. Panasonic Gobel Indonesia Cabang Medan,†J. Fasilkom, Vol. 11, No. 1, Pp. 26–31, 2021, Doi: 10.37859/Jf.V11i1.2429.

R. Febrilia, T. Wulandari, And D. Anubhakti, “Implementasi Algoritma Support Vector Machine ( Svm ) Dalam Memprediksi Harga Saham Pt . Garuda Indonesia Tbk,†Indones. J. Inf. Syst., Vol. 4, No. 2, Pp. 250–256, 2021.

M. Nanja And Purwanto, “Metode K-Nearest Neighbor Berbasis Forward Selection Untuk Memprediksi Harga Komoditi Lada,†J. Pseudocode, Vol. 2, No. 1, Pp. 53–64, 2017.

J. Sistem, I. Bisnis, And S. U. Kupang, “Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa Ppa Dan Bbm,†J. Sist. Infromasi Bisni, Vol. 1, No. 2, Pp. 52–62, 2017.

“Klasifikasi Sentimen Masyarakat Terhadap Harga Tiket Pada Twittwer Knn Dengan Aan,†J. Instek, Vol. 4, No. 2, Pp. 236–245, 2019.

M. J. Islam, Q. M. Jonathan, And M. Ahmadi, “Ivestigating The Performace Of Naive Bayes Clasifiers And K-Nearest Neighbor Classifiers,†J. Converg. Inf. Technol., Vol. 5, No. 2, Pp. 133–137, 2016.

M. D. Irawan, F. Nurhidayahti, R. Hsb, And A. Widarma, “Decision Support System Determining Computer Virus Protection Applications Using Simple Additive Weighting ( Saw ) Method,†J. Comput. Networks , Archit. High Perform. Comput., Vol. 3, No. 1, Pp. 68–79, 2021.

S. Samsudin, Diktat Pengantar Ilmu Kompuer. Medan: Uinsu, 2018.

A. Almira, A. Ikhwan, And Suendri, “Implementasi Data Mining Menggunakan Algoritma Fp-Growth Pada Analisis Pola Pencurian Daya Listrik,†J. Inform. Univ. Pamulang, Vol. 6, No. 2, Pp. 442–448, 2021.

A. Ikhwan And N. Aslami, “Implementasi Data Mining Untuk Manajemen Bantuan Sosial Menggunakan Algoritma K-Means,†Jurti (Jurnal Teknol. Informasi), Vol. 4, No. 2, 2020.

M. D. Irawan And Herviana, “Implementasi Logika Fuzzy Dalam Menentukan Jurusan Bagi Siswa Baru Sekolah Menengah Kejuruan ( Smk ) Negeri 1 Air Putih,†(Jurnal Teknol. Inf., Vol. 2, No. 2, Pp. 129–137, 2018.

R. Hutami And E. Z. Astuti, “Implementasi Metode K-Nearest Neighbor Untuk Prediksi Penjualan Furniture Pada Cv.Octo Agung Jepara,†Univ. Dian Nuswantoro Semarang, Vol. 3, No. 2, Pp. 40–51, 2016.

N. H. Harani And F. S. Damayanti, “Implementasi Algoritma C5.0 Untuk Menentukan Pelanggan Potensial Di Kantor Pos Cimahi,†J. Sitech, Vol. 4, No. 1, Pp. 70–76, 2021.

B. Aisyah Farahdiba And Y. S. Nugroho, “Klasifikasi Kanker Payudara Menggunakan Algoritma Gain Ratio,†J. Tek. Elektro, Vol. 8, No. 2, Pp. 43–46, 2017.

I. M. Sudarma, “Implementasi Algoritma C5.0 Pada Penilaian Kinerja Pegawai Negeri Sipil,†Maj. Ilm. Teknol. Elektro, Vol. 17, No. 3, Pp. 1–6, 2018.

Y. Adani And W. Nurjaya, “Penerapan Algoritma Naïve Bayes Untuk Memprediksi Keputusan Calon Nasabah Dan Nasabah Tetap Bank Bri Syariah Menerima Penawaran Program Deposito Berjangka,†J. Tek., Vol. 5, No. 2, Pp. 13–24, 2ad.

T. Setiyorini And R. T. Asmono, “Implementation Of Gain Ratio And K-Nearest Neighbor For Classification Of Student Performance,†J. Pilar Nusa Mandiri, Vol. 16, No. 1, Pp. 19–24, 2020, Doi: 10.33480/Pilar.V16i1.813.

F. G. Dewanto, J. J. M. R. Londok, R. A. V Tuturoong, And W. B. Kaunang, “Pengaruh Pemupukan Anorganik Dan Organik Terhadap Produksi Tanaman Jagung Sebagai Sumber Pakan.,†Zootec, Vol. 32, No. 5, Pp. 1–8, 2017, Doi: 10.35792/Zot.32.5.2013.982.

Triase And Samsudin, “Implementasi Data Mining Dalam Mengklasifikasikan Ukt ( Uang Kuliah Tunggal ) Pada Uin Sumatera Utara Medan,†J. Teknol. Inf., Vol. 4, No. 2, Pp. 370–376, 2020.

K. Imtihan, Ernawati, And L. Mutawalli, “Penerapan Research And Development (R&D) Dalam Membangun Alat Penyiraman Tanaman Otomatis Berbasis Arduino,†J. Manaj. Inform. Sist. Inf., Vol. 5, 2022.

M. J. Christianto, D. Tjahjadi, And C. Juliane, “Designing The Architecture Of Population Administration Information Systems Using Methods Rational Unified Process (Rup),†J. Sos. Dan Teknol., Vol. 2, No. 2, Pp. 107–124, 2022.

Suendri, “Implementasi Diagram Uml (Unified Modelling Language) Pada Perancangan Sistem Informasi Remunerasi Dosen Dengan Database Oracle (Studi Kasus: Uin Sumatera Utara Medan),†J. Ilmu Komput. Dan Inform., Vol. 3, No. 1, Pp. 1–9, 2018.

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Published

2022-03-30

How to Cite

Siagian, S. B. ., Samsudin, S., & Irawan, M. D. . (2022). Implementation of Gain Ratio on KNN Method in Predicting Sales of Electronic Sparepart at Panasonic Service Center Lhokseumawe. Jurnal Teknik Informatika C.I.T Medicom, 14(1), 36–47. https://doi.org/10.35335/cit.Vol14.2022.242.pp36-47