Penerapan Algoritma Apriori Dalam Memprediksi Penjualan Sepeda Motor

Eferoni Ndruru

doi:10.47065/bit.v4i4.1054

Eferoni Ndruru * Universitas Budi Darma, Indonesia

DOI: https://doi.org/10.47065/bit.v4i4.1054

Keywords: Data mining;Apriori;Prediksi;Penjualan;

Abstract

Currently, companies operating in the motorbike sector are experiencing a decline in sales of spare part products and are not appropriate in determining the promotional strategies given to customers. A lot of transaction data is used as a reference for selling products at capital prices which only results in small profits. If it is still not sold, the incoming goods are delayed because the capital has not been returned. Therefore, a system is needed to process information data more quickly and precisely in predicting motorbike sales patterns using a priori algorithm data mining applications. The results of calculations using the a priori algorithm show that if a consumer buys a Yamaha lower arm and tires then the support value = 23.33 and the confidence value = 77.78 and if the consumer buys a Yamaha lower arm and AC filter then the support value = 26.67 and the confidence value = 72.72. The research objective is to predict and analyze motorbike sales patterns implemented on a desktop-based application. This is to make it easier to analyze the competitiveness of the best-selling motorcycle products simultaneously. As a recommendation for decision makers to improve marketing and promotion of better motorbike products.

References

S. Sahara, “Penerapan Metode Support Vector Machine (SVM) Guna Menentukan Tingkat Lulus Mahasiswa E-Learning.” [Online]. Available: www.kemendiknas.go.id

A. Pratama, R. C. Wihandika, and D. E. Ratnawati, “Implementasi algoritme support vector machine (SVM) untuk prediksi ketepatan waktu kelulusan mahasiswa,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. April, pp. 1704–1708, 2018.

A. Perdana and M. T. Furqon, “Penerapan Algoritma Support Vector Machine ( SVM ) Pada Pengklasifikasian Penyakit Kejiwaan Skizofrenia ( Studi Kasus : RSJ . Radjiman Wediodiningrat , Lawang ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 9, pp. 3162–3167, 2018.

“DATA MINING : PENERAPAN RAPIDMINER DENGAN K-MEANS CLUSTER PADA DAERAH TERJANGKIT DEMAM BERDARAH DENGUE ( DBD ) BERDASARKAN PROVINSI,” vol. 3, no. 2, pp. 173–178, 2018.

D. H. Kamagi and S. Hansun, “Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa,” J. Ultim., vol. 6, no. 1, pp. 15–20, 2014, doi: 10.31937/ti.v6i1.327.

S. Syarli and A. Muin, “Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi),” J. Ilm. Ilmu Komput., vol. 2, no. 1, pp. 22–26, 2016.

A. R. Isnain, A. I. Sakti, D. Alita, and N. S. Marga, “Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma SVM,” Jdmsi, vol. 2, no. 1, pp. 31–37, 2021, [Online]. Available: https://t.co/NfhnfMjtXw

R. Ramlawati, A. Anwar, and R. M. Thaha, “Model Estimasi Kejadian Diare Di Kota Makassar (Estimated Models of Occurrence Diarrhea in Makassar),” J. Kesehat. Masy. Marit., vol. 1, no. 1, pp. 73–78, 2019.

P. A. Octaviani, Yuciana Wilandari, and D. Ispriyanti, “Penerapan Metode Klasifikasi Support Vector Machine (SVM) pada Data Akreditasi Sekolah Dasar (SD) di Kabupaten Magelang,” J. Gaussian, vol. 3, no. 8, pp. 811–820, 2014, [Online]. Available: http://download.portalgaruda.org/article.php?article=286497&val=4706&title=PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG

M. R. Fradinata, I. G. J. E. Putra, and I. N. Y. A. Wijaya, “Evaluasi Tata Kelola TI Menggunakan Framework COBIT 5 Studi Kasus STMIK Primakara,” Kumpul. Artik. Mhs. Pendidik. Tek. Inform., vol. 10, no. 1, p. 68, 2021, doi: 10.23887/karmapati.v10i1.31993.

M. Zaki and T. Sutabri, “Analisis Manajemen Layanan Teknologi Informasi Perpustakaan SMK Negeri 5 Palembang Menggunakan Framework ITIL,” J. Econ. Manag. Sci., vol. 06, no. 02, pp. 200–205, 2023, doi: 10.37034/jems.v5i4.23.

N. Mutiah, “Penilaian Tata Kelola Teknologi Informasi Universitas Tanjungpura Menggunakan Cobit 5 Domain Align, Plan, Dan Organise (APO),” Comput. Eng. Sci. Syst. J., vol. 4, no. 1, p. 65, 2019, doi: 10.24114/cess.v4i1.11457.

N. L. Sari, “Pengukuran Maturity Level Cobit 5 Dan Domain Dss (Deliver, Service, and Support) Pada Regulasi Sandbox Ojk Klaster Aggregator,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 2, pp. 561–572, 2021, doi: 10.35957/jatisi.v8i2.843.

Renaldi, Khaerana, and Anhar Maulana, “Analisis Kualitas Layanan E-Tracking Terhadap Kepuasan Pelanggan Perusahaan Ekspedisi J&T Express Cabang Palopo,” J. Manaj. Perbank. Keuang. Nitro, vol. 6, no. 1, pp. 54–63, 2023, doi: 10.56858/jmpkn.v6i1.93.

R. Damayanti and A. D. Manuputty, “A Analysis Of Information Technology Governance In Department of Communication And Informatics of Salatiga Using COBIT 5 Framework DSS Domain,” J. Inf. Syst. Informatics, vol. 1, no. 2, pp. 97–122, 2019, doi: 10.33557/journalisi.v1i2.12.

S. Steven, M. N. N. Sitokdana, and A. F. Wijaya, “Evaluasi Kinerja Tata Kelola Teknologi Informasi Pt. Adicipta Inovasi Teknologi Menggunakan Framework Cobit 5,” J. Bina Komput., vol. 2, no. 2, pp. 64–78, 2020, doi: 10.33557/binakomputer.v2i2.916.

U. F. Afifah and I. Verdian, “Analisis Pemanfaatan Platform E-Learning pada Domain DSS05 Menggunakan Framework COBIT 5 di Perguruan Tinggi Swasta Kepulauan Riau,” J. Sist. Inf., vol. 11, no. 1, pp. 179–185, 2022.

V. pradani K. Wardana, D. Restiana, and I. A. Wijayanti, “Tingkat Kematangan Sistem Informasi E-Rapot Menggunakan Cobit 5 (Studi Kasus : Smk Negeri 2 Sampit),” Educ. J. Teknol. Pendidik., vol. 6, no. 2, p. 120, 2021, doi: 10.32832/educate.v6i2.5105.

Wella, “Audit Sistem Informasi Menggunakan Cobit 5 . 0 Domain DSS pada,” Ultim. InfoSys, vol. VII, no. 1, pp. 38–44, 2016.

W. Agustinus, Frizt and A. Anneke, Tri, “Evaluasi Kinerja Sistem Informasi E-Filing Menggunakan Cobit 5 Pada Kantor Pelayanan Pajak Pratama Kota Salatiga,” J. Terap. Teknol. Inf., vol. 1, no. 1, pp. 61–70, 2017, doi: 10.21460/jutei.2017.11.9.