Application of Multiple Linear Regression Algorithm for Motorcycle Sales Estimation

Authors

  • Elvri Rahayu STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Iin Parlina STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Zulia Almaida Siregar STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v1i1.142

Keywords:

Data Mining, Motorcycle, Regression Algorithm, Sales, Estimation

Abstract

CV. Kerinci Motor is a company engaged in the transportation and automotive sector, especially in the sale of motorcycles. The uncertainty in the number of motorcycle sales at this company has hampered the company's development, because the company cannot take definite policies regarding the sales that occur. Therefore, it is necessary to estimate the sales of motorcycles at this company in the future, so that the management can estimate consumer demand in the future. So that the company is able to serve and provide consumer demand. The estimation algorithm that will be used in this research is Multiple Linear Regression which is one of the data mining methods. This method was chosen because it is able to make an estimate by utilizing data regarding sales. The results of the estimated (estimated) sales of manual motorcycles in 2021 by January are 56,941 or 57 motorcycles in the manual category. This means that there is an increase in the number of manual motorbikes by 5 motorbikes, while the results until May 2021 amounted to 65,710 motorbikes. So it can be concluded that sales of motorcycles at CV. Kerinci Motor have increased sales in the next 5 months.

References

N. Sagala, J. Junita, and C. Hayat, “Sistem Pendukung Keputusan Pembelian Sepeda Motor Menggunakan Metode Promethee,” Komputika : Jurnal Sistem Komputer, vol. 9, no. 2, pp. 123–129, 2020.

H. Erlangga, N. Nurjaya, D. Sunarsi, M. Mas’adi, and J. Jasmani, “Pengaruh Kualitas Pelayanan Dan Kualitas Produk Terhadap Keputusan Pembelian Konsumen Sepeda Motor Honda Di PT Panca Sakti Perkasa Di Bintaro,” Jurnal Ilmiah PERKUSI, vol. 1, no. 4, p. 464, 2021.

S. F. Utami, S. Y. Arisma, K. Hermanto, and E. Ruskartina, “Peramalan Jumlah Penjualan Sepeda Motor Menggunakan Metode Time Series Studi Kasus : Dealer Motor Nusantara Surya Sakti (NSS) Sumbawa,” Hexagon, vol. 1, no. 2, pp. 33–41, 2020.

A. Tri Wibowo, I. Salamah, and A. Taqwa, “Rancang Bangun Sistem Keamanan Sepeda Motor Berbasis Iot (Internet of Things),” Jurnal Fasilkom, vol. 10, no. 2, pp. 103–112, 2020.

S. T. U. . Ginting, “Pengaruh Kualitas Pesan, Daya Tarik Iklan Dan Frekuensi Penayangan Terhadap Efektivitas Iklan Media Televisi Pada Produk Sepeda Motor Merek Honda,” Intelektiva : Jurnal Ekonomi, Sosial & Humaniora, vol. 01, no. 10, pp. 24–39, 2020.

D. Herdiansyah and M. Fahrizal, “Jurnal Ilmiah Poli Bisnis,” Jurnal Ilmiah Poli Bisnis, vol. 13, no. 2, pp. 140–151, 2021.

P. V. Tannia and N. N. Yulianthini, “Pengaruh Kualitas Produk, Desain Produk dan Harga Terhadap Keputusan Pembelian Sepeda Motor Honda Merk PCX,” Prospek: Jurnal Manajemen dan Bisnis, vol. 3, no. 2, pp. 87–94, 2021.

I. S. Budi, A. Octavia, and N. Sari, “Pengaruh Inovasi Produk, Harga dan Kualitas Produk Terhadap Keputusan Pembelian Sepeda Motor Honda Beat di Kota Jambi,” Jurnal Dinamika Manajemen, vol. 7, no. 2, pp. 59–72, 2019.

Ratna, N. Khoirul, and M. Ridho, “Faktor Dominan yang Mempengaruhi Keputusan Pembelian Sepeda Motor Yamaha NMAX di Muara Bulian,” Citra Ekonomi, vol. 2, no. 1, pp. 52–65, 2021.

A. Z. Siregar, “Implementasi Metode Regresi Linier Berganda Dalam Estimasi Tingkat Pendaftaran Mahasiswa Baru,” KESATRIA : Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), vol. 2, no. 3, pp. 133–137, 2021.

A. P. W. Fica Oktavia Lusiana*, IndriFatma, “Estimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Simalungun,” Journal of Informatics Management and Information Technology, vol. 1, no. 2, pp. 79–84, 2021.

M. A. H. Laksamana, Amroni, and A. N. Toscany, “Penerapan Data Mining untuk Memprediksi Jumlah Total Produksi Hcl Pada Perusahaan PT . Lontar Papyrus Menggunakan Algoritma Regresi Linier Berganda,” Jurnal Ilmiah Mahasiswa Teknik Informatika, vol. 3, no. 2, pp. 187–198, 2021.

N. Arminarahmah, A. D. GS, G. W. Bhawika, M. P. Dewi, and A. Wanto, “Mapping the Spread of Covid-19 in Asia Using Data Mining X-Means Algorithms,” IOP Conf. Series: Materials Science and Engineering, vol. 1071, no. 012018, pp. 1–7, 2021.

A. Pradipta, D. Hartama, A. Wanto, S. Saifullah, and J. Jalaluddin, “The Application of Data Mining in Determining Timely Graduation Using the C45 Algorithm,” IJISTECH (International Journal of Information System & Technology), vol. 3, no. 1, pp. 31–36, 2019.

T. H. Sinaga, A. Wanto, I. Gunawan, S. Sumarno, and Z. M. Nasution, “Implementation of Data Mining Using C4.5 Algorithm on Customer Satisfaction in Tirta Lihou PDAM,” Journal of Computer Networks, Architecture, and High-Performance Computing, vol. 3, no. 1, pp. 9–20, 2021.

N. A. Febriyati, A. D. GS, and A. Wanto, “GRDP Growth Rate Clustering in Surabaya City uses the K- Means Algorithm,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 276–283, 2020.

J. Hutagalung, N. L. W. S. R. Ginantra, G. W. Bhawika, W. G. S. Parwita, A. Wanto, and P. D. Panjaitan, “COVID-19 Cases and Deaths in Southeast Asia Clustering using K-Means Algorithm,” Journal of Physics: Conference Series, vol. 1783, no. 1, p. 012027, 2021.

I. Parlina et al., “Naive Bayes Algorithm Analysis to Determine the Percentage Level of visitors the Most Dominant Zoo Visit by Age Category,” in Journal of Physics: Conference Series, 2019, vol. 1255, no. 1, p. 012031.

M. A. Hanafiah, A. Wanto, and P. B. Indonesia, “Implementation of Data Mining Algorithms for Grouping Poverty Lines by District/City in North Sumatra,” International Journal of Information System & Technology, vol. 3, no. 2, pp. 315–322, 2020.

I. S. Damanik, A. P. Windarto, A. Wanto, Poningsih, S. R. Andani, and W. Saputra, “Decision Tree Optimization in C4.5 Algorithm Using Genetic Algorithm,” Journal of Physics: Conference Series, vol. 1255, no. 1, pp. 1–7, 2019.

A. Wanto et al., Data Mining : Algoritma dan Implementasi. Yayasan Kita Menulis, 2020.

D. Hartama, A. Perdana Windarto, and A. Wanto, “The Application of Data Mining in Determining Patterns of Interest of High School Graduates,” Journal of Physics: Conference Series, vol. 1339, no. 1, p. 012042, Dec. 2019.

A. Luthfiarta, A. Febriyanto, H. Lestiawan, and W. Wicaksono, “Analisa Prakiraan Cuaca dengan Parameter Suhu, Kelembaban, Tekanan Udara, dan Kecepatan Angin Menggunakan Regresi Linear Berganda,” JOINS (Journal of Information System), vol. 5, no. 1, pp. 10–17, 2020.

Y. Rokhayati, N. S. Utomo, and Sartikha, “Prediksi Kelayakan Operasional Mesin Rivet Menggunakan Regresi Linear Berganda,” Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan, vol. 10, no. 01, pp. 10–15, 2021.

I. M. Sari, A. Rinaldi, and F. G. Putra, “Pengaruh Sisa Hasil Usaha (SHU) pada Koperasi menggunakan Regresi Linear Berganda,” MAJU: Jurnal Ilmiah Pendidikan Matematika, vol. 7, no. 2, pp. 110–120, 2020.

Y. Asohi and A. Andri, “Impelementasi Algoritma Regresi Linier Berganda Untuk Prediksi Penjualan,” Jurnal Nasional Ilmu Komputer, vol. 1, no. 3, pp. 149–158, 2020.

A. Fitri Boy, “Implementasi Data Mining Dalam Memprediksi Harga Crude Palm Oil (CPO) Pasar Domestik Menggunakan Algoritma Regresi Linier Berganda (Studi Kasus Dinas Perkebunan Provinsi Sumatera Utara),” Journal of Science and Social Research, vol. 3, no. 2, pp. 78–85, 2020.

R. A. Samosir, M. F. Rozy, and A. P. Windarto, “Penerapan Algoritma Regresi Linier Berganda dalam Mengestimasi Jumlah Perceraian di Pengadilan Agama Simalungun,” TIN: Terapan Informatika Nusantara, vol. 2, no. 1, pp. 16–20, 2021.

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Published

2022-03-18

How to Cite

Rahayu, E., Parlina, I., & Siregar, Z. A. (2022). Application of Multiple Linear Regression Algorithm for Motorcycle Sales Estimation. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 1(1), 1–10. https://doi.org/10.55123/jomlai.v1i1.142

Issue

Vol. 1 No. 1 (2022): March

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Articles

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