EKPLORASI DATA PENJUALAN DI OTOXPERT MENGGUNAKAN ALGORITMA APRIORI
DOI:
https://doi.org/10.33884/comasiejournal.v14i01.11210Keywords:
Apriori Algorithm, Association Rule, Data Mining, Sales Transaction Data, Spare PartsAbstract
This study aims to explore sales transaction data of spare parts at OTOXPERT Batam using
the Apriori algorithm to identify association patterns among products. The main problem
addressed is that transaction data, although available in large quantities, has not been
optimally utilized to uncover relationships between spare parts, so the potential use of these
patterns to support cross-selling activities and stock management has not been fully
realized. The data used in this study were sales transactions from January 1 to March 31,
2025. The research method includes data preprocessing, transformation of transaction data
into basket form, descriptive analysis, and the application of the Apriori algorithm using
support, confidence, and lift parameters. The results show that the Apriori algorithm is able
to discover frequent itemsets and association rules that describe the tendency of spare
parts to be purchased together. These patterns provide meaningful information about
customer purchasing behavior and can be used as supporting information in decision
making related to sales strategies and inventory management. Therefore, this study
demonstrates that the Apriori algorithm is effective in transforming transaction data into
valuable information for business analysis at OTOXPERT Batam.
References
Alhillah, Y. A., Priatna, W., & Fitriyani, A. (2023). Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages. In Journal of Applied Informatics and Computing (JAIC) (Vol. 7). Retrieved from http://jurnal.polibatam.ac.id/index.php/JAIC
Andika Johan, R., Himilda, R., & Auliza, N. (2005). Penerapan Metode Association Rule Untuk Strategi Penjualan Menggunakan Algoritma Apriori.
Chairuddin, I., Sentagi Asa, R., & Darmansah. (2021). Analisis Penyebaran Penularan Virus Corona Di Provinsi Jawa Barat Menggunakan Algoritma K-Means Clustering. Jurnal Teknik Informatika Dan Sistem Informasi, 8(3). Retrieved from http://jurnal.mdp.ac.id
Darmansah. (2024). Penerapan Algoritma K-Means Clustering Pada Segmentasi Article Info ABSTRAK. JSAI: Journal Scientific and Applied Informatics, 7(3). https://doi.org/10.36085
Dewi Astuti, T., Iman Hermanto, T., Kaniawulan, I., Informatika, T., & Wastukancana, S. (2016). ANALISA DATA MINING MENGGUNAKAN ALGORITMA APRIORI UNTUK MENINGKATKAN CROSS SELLING DAN UP SELLING (STUDI KASUS RUMAH MAKAN MAS NUR PURWAKARTA).
Efendi1, A., Maryono2, D., & Sujana3, Y. (2025). Peningkatan Literasi Data Guru Informatika Melalui Pelatihan Analisis Data Menggunakan Python dan Google Colab di SMA Se-Surakarta. Jurnal Abdidas, 6(6). https://doi.org/10.31004/abdidas.v6i6.1248
Essalmi, H., & El Affar, A. (2025). Dynamic Algorithm for Mining Relevant Association Rules via Meta-Patterns and Refinement-Based Measures. Information (Switzerland), 16(6). https://doi.org/10.3390/info16060438
Handoko, K., Darmansah, & Adhiatma, N. (n.d.). Implementasi FP-Growth untuk Analisis Pola Pembelian Produk Elektronik pada Tokopedia. Jurnal Pendidikan Sains Dan Komputer, 5, 2809–476. https://doi.org/10.47709/jpsk.v5i02.6963
Kabir, M. A., Ahmed, F., Islam, M. M., & Ahmed, Md. R. (2024). Python For Data Analytics: A Systematic Literature Review Of Tools, Techniques, And Applications. ACADEMIC JOURNAL ON SCIENCE, TECHNOLOGY, ENGINEERING & MATHEMATICS EDUCATION, 4(04), 134–154. https://doi.org/10.69593/ajsteme.v4i04.146
Mahjouby, M. El, Bennani, M. T., Lamrini, M., & Far, M. El. (2024). Association rules forecasting for the foreign exchange market. International Journal of Electrical and Computer Engineering, 14(3), 3443–3454. https://doi.org/10.11591/ijece.v14i3.pp3443-3454
Mostafa, A. A. N., & Mahmoud, H. E. A. (2022). Review of Data Mining Concept and its Techniques. International Journal of Academic Research in Business and Social Sciences, 12(6). https://doi.org/10.6007/ijarbss/v12-i6/13135
Ovilianda, A., & Ginting, B. (2021). Penerapan Data Mining Korelasi Penjualan Spare Part Mobil Menggunakan Metode Algoritma Apriori (Studi Kasus: CV. Citra Kencana Mobil). In JOURNAL OF INFORMATION AND TECHNOLOGY UNIMOR.
Steven Simanjuntak, H., Sarkis, I. M., Simanullang, H. G., & Artikel, H. (2023). Penerapan Data Mining Penjualan Sparepart Kendaraan Menggunakan Algoritma Apriori Pada PT. MPM. In Methotika : Jurnal Ilmiah Teknik Informatika (Vol. 3). Retrieved from https://ejurnal.methodist.ac.id/index.php/methotika
Subechi, F. H., Surorejo, S., & Utami, E. U. S. (2025). Analisis Pola Pembelian Konsumen Menggunakan Algoritma Apriori pada Toko Komputer. RIGGS: Journal of Artificial Intelligence and Digital Business, 4(3), 1504–1513. https://doi.org/10.31004/riggs.v4i3.2172
Yanti, R., Maradjabessy, P. N., Qurtubi, & Rachmadewi, I. P. (2024). Determining the retail sales strategies using association rule mining. International Journal of Advances in Applied Sciences, 13(3), 530–538. https://doi.org/10.11591/ijaas.v13.i3.pp530-538
