Penentuan Pola Pada Dataset Penjualan Dalam Data Mining Menggunakan Metode Apriori
- Ulfa Utami Universitas Labuhanbatu, Rantauprapat, Indonesia
-
Deci Irmayani
*
Universitas Labuhanbatu, Rantauprapat,
Indonesia
- Budianto Bangun Universitas Labuhanbatu, Rantauprapat, Indonesia (*) Corresponding Author
Abstract
In everyday life and the business world, buying and selling activities play a central role. For companies, daily transaction data is not just a record, but an important asset that holds the potential to increase sales through analysis. The volume of sales data generated daily is enormous, making manual processing inefficient and prone to errors. The complexity of the number of products sold also makes it difficult to gain a comprehensive understanding of purchasing patterns. Dynamic changes in consumer preferences further complicate demand forecasting and may lead to inventory issues. This study aims to address these issues by analysing sales data to identify products that are frequently purchased together. This information will be utilised in designing more effective marketing strategies, such as cross-promotions or product bundling. Additionally, this data is useful for demand forecasting and optimising inventory management. The ultimate goal is to provide relevant product recommendations to customers and enhance their satisfaction. To achieve this objective, this study applies data mining techniques, specifically the Apriori Association method. Data from 15 types of items in 28 weekly transactions at TOKO BANGUNAN MAJU BERSAMA will be analysed as an initial sample to identify the most frequently purchased combinations of construction tools. The Apriori method will associate each item based on a minimum support value of 0.25 and a minimum confidence value of 0.80. The application of this method resulted in 4 rules from 3-item patterns with confidence values ranging from 0.88 to 0.89.
Downloads
References
R. Liang, C. Huang, C. Zhang, B. Li, S. Saydam, dan I. Canbulat, “Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization,” IEEE Access, vol. 11, hal. 40608–40628, 2023, doi: 10.1109/ACCESS.2023.3267813.
H. Guo, “Research on Web Data Mining Based on Topic Crawler,” J. Web Eng., vol. 20, no. 4, hal. 1193–1206, 2021, doi: 10.13052/jwe1540-9589.20411.
M. M. Rahman, Y. Watanobe, T. Matsumoto, R. U. Kiran, dan K. Nakamura, “Educational Data Mining to Support Programming Learning Using Problem-Solving Data,” IEEE Access, vol. 10, hal. 26186–26202, 2022, doi: 10.1109/ACCESS.2022.3157288.
A. J. P. Sibarani, “Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Meningkatkan Pola Penjualan Obat,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 7, no. 2, hal. 262–276, 2020, doi: 10.35957/jatisi.v7i2.195.
S. Saefudin dan S. DN, “Penerapan Data Mining Dengan Metode Algoritma Apriori Untuk Menentukan Pola Pembelian Ikan,” JSiI (Jurnal Sist. Informasi), vol. 6, no. 2, hal. 36, 2019, doi: 10.30656/jsii.v6i2.1587.
M. Syahril, K. Erwansyah, dan M. Yetri, “Penerapan Data Mining Untuk Menentukan Pola Penjualan Peralatan Sekolah Pada Brand Wigglo Dengan Menggunakan Algoritma Apriori,” J-SISKO TECH (Jurnal Teknol. Sist. Inf. dan Sist. Komput. TGD), vol. 3, no. 1, hal. 118, 2020, doi: 10.53513/jsk.v3i1.202.
Y. Andini, J. T. Hardinata, dan Y. P. Purba, “Penerapan Data Mining pada Tata Letak Buku Di Perpustakaan Sintong Bingei Pematangsiantar dengan Metode Apriori,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 7, no. 1, hal. 13–18, 2022, doi: http://dx.doi.org/10.30645/jurasik.v7i1.410.
A. N. Rahmi dan Y. A. Mikola, “Implementasi Algoritma Apriori Untuk Menentukan Pola Pembelian Pada Customer (Studi Kasus : Toko Bakoel Sembako),” Inf. Syst. J., vol. 4, no. 1, hal. 14–19, 2021, [Daring]. Tersedia pada: https://jurnal.amikom.ac.id/index.php/infos/article/view/561
S. Syahriani, “Penerapan Data Mining Untuk Menentukan Pola Penjualan Sepatu Menggunakan Metode Algoritma Apriori,” Bina Insa. Ict J., vol. 9, no. 1, hal. 43, 2022, doi: 10.51211/biict.v9i1.1758.
W. Sahara, S. D. Saragih, dan A. P. Windarto, “Teknik Asosiasi Datamining Dalam Menentukan Pola Penjualan dengan Metode Apriori,” TIN Terap. Inform. Nusant., vol. 2, no. 12, hal. 684–689, 2022, doi: 10.47065/tin.v2i12.1577.
A. M. Andika, N. Suarna, dan R. D. Dana, “Jurnal Teknologi Ilmu Komputer Analisa Dataset Asosiasi Penjualan Menggunakan Metode FP- Jurnal Teknologi Ilmu Komputer,” vol. 2, no. 1, hal. 80–88, 2023, doi: 10.56854/jtik.v2i1.108.
E. Gunia, A. I. Purnamasari, dan I. Ali, “Penerapan Datamining Dalam Menentukan Pola Penjualan Produk Menggunakan Algoritma Fp-Growth,” vol. 8, no. 2, hal. 2417–2422, 2024, doi: https://doi.org/10.36040/jati.v8i2.9506.
J. Gong, “Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis,” J. Web Eng., vol. 20, no. 2, hal. 513–532, 2021, doi: 10.13052/jwe1540-9589.20213.
M. N. Ashtiani dan B. Raahemi, “Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review,” IEEE Access, vol. 10, hal. 72504–72525, 2022, doi: 10.1109/ACCESS.2021.3096799.
K. Dhanushkodi, A. Bala, N. Kodipyaka, dan V. Shreyas, “Customer Behavior Analysis and Predictive Modeling in Supermarket Retail: A Comprehensive Data Mining Approach,” IEEE Access, vol. 13, hal. 2945–2957, 2025, doi: 10.1109/ACCESS.2024.3407151.
A. S. L. T. T. H. Hafizah, “Data Mining Estimasi Biaya Produksi Ikan Kembung Rebus Dengan Regresi Linier Berganda,” J. Sist. Inf. Triguna Dharma (JURSI TGD), no. Vol 1, No 6 (2022): EDISI NOVEMBER 2022, hal. 888–897, 2022, [Daring]. Tersedia pada: https://ojs.trigunadharma.ac.id/index.php/jsi/article/view/5732/1938
Y. L. Nainel, E. Buulolo, dan I. Lubis, “Penerapan Data Mining Untuk Estimasi Penjualan Obat Berdasarkan Pengaruh Brand Image Dengan Algoritma Expectation Maximization (Studi Kasus: PT. Pyridam Farma Tbk),” JURIKOM (Jurnal Ris. Komputer), vol. 7, no. 2, hal. 214, 2020, doi: 10.30865/jurikom.v7i2.2097.
M. Azhari, Z. Situmorang, dan R. Rosnelly, “Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes,” J. Media Inform. Budidarma, vol. 5, no. 2, hal. 640, 2021, doi: 10.30865/mib.v5i2.2937.
S. Widaningsih, “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan Svm,” J. Tekno Insentif, vol. 13, no. 1, hal. 16–25, 2019, doi: 10.36787/jti.v13i1.78.
S. M. Darwish, R. M. Essa, M. A. Osman, dan A. A. Ismail, “Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics,” IEEE Access, vol. 10, hal. 76268–76280, 2022, doi: 10.1109/ACCESS.2022.3192447.
B. Harli Trimulya Suandi As dan L. Zahrotun, “Penerapan Data Mining Dalam Mengelompokkan Data Riwayat Akademik Sebelum Kuliah Dan Data Kelulusan Mahasiswa Menggunakan Metode Agglomerative Hierarchical Clustering (Implementation Of Data Mining In Grouping Academic History Data Before Students And Stud,” J. Teknol. Informasi, Komput. dan Apl., vol. 3, no. 1, hal. 62–71, 2021, [Daring]. Tersedia pada: http://jtika.if.unram.ac.id/index.php/JTIKA/
B. Mohanty, S. L. Champati, dan S. K. Barisal, “Enhancing Retail Strategies Through Anomaly Detection in Association Rule Mining,” IEEE Access, vol. 13, hal. 92376–92405, 2025, doi: 10.1109/ACCESS.2025.3573807.
M. Kaushik, R. Sharma, P. Kõiva, I. Fister, dan D. Draheim, “An Exhaustive Multi-Aspect Analysis of Swarm Intelligence Algorithms in Numerical Association Rule Mining,” IEEE Access, vol. 12, hal. 138985–139002, 2024, doi: 10.1109/ACCESS.2024.3417334.
W. Thurachon dan W. Kreesuradej, “Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm,” IEEE Access, vol. 9, hal. 55726–55741, 2021, doi: 10.1109/ACCESS.2021.3071777.
R. Gupta dan M. K. Trivedi, “AEHO: Apriori-Based Optimized Model for Building Construction to Time-Cost Tradeoff Modeling,” IEEE Access, vol. 10, hal. 103852–103871, 2022, doi: 10.1109/ACCESS.2022.3208966.
R. Takdirillah, “Penerapan Data Mining Menggunakan Algoritma Apriori Terhadap Data Transaksi Sebagai Pendukung Informasi Strategi Penjualan,” Edumatic J. Pendidik. Inform., vol. 4, no. 1, hal. 37–46, 2020, doi: 10.29408/edumatic.v4i1.2081.
Z. Abidin, A. K. Amartya, dan A. Nurdin, “Penerapan Algoritma Apriori Pada Penjualan Suku Cadang Kendaraan Roda Dua (Studi Kasus: Toko Prima Motor Sidomulyo),” J. Teknoinfo, vol. 16, no. 2, hal. 225, 2022, doi: 10.33365/jti.v16i2.1459.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penentuan Pola Pada Dataset Penjualan Dalam Data Mining Menggunakan Metode Apriori
Pages: 605-615
Copyright (c) 2025 Ulfa Utami, Deci Irmayani, Budianto Bangun
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
