Data Mining Analysis of Commodity Distribution in Central Aceh Through an Integrated Auction Market System Using the Android-Based Association Rule Mining Method

Dita Amelia, Dahlan Abdullah, Zahratul Fitri

Abstract


Commodity distribution in Central Aceh faces inefficiencies due to lengthy distribution chains and limited price control, which often leads to higher costs for consumers and lower profits for farmers. To address these issues, this study develops an integrated auction market system based on Android, utilizing the Association Rule Mining (ARM) method to optimize the distribution and pricing of commodities. ARM is a data mining technique that uncovers high-frequency patterns in transaction data. By applying ARM with the apriori algorithm, the system identifies key associations among commodities, allowing for more efficient and targeted price recommendations. The system calculates the highest bid for each commodity and recommends optimal pricing strategies to sellers based on frequent pattern analysis, improving transparency and reducing distribution inefficiencies. Testing and implementation of this system indicate that it successfully reduces distribution costs while increasing the effectiveness and speed of the auction process. Overall, the Android-based auction market system shows promise as a tool for enhancing distribution efficiency, optimizing bid values, and supporting local economies in Central Aceh through more equitable commodity pricing. The final result of this process resulted in four association rules based on predefined parameters, namely a minimum support of 20% and a minimum confidence of 50%. These rules indicate that 60% of the transactions in the Integrated Auction Market system that include Cassava also include Carrots. In other words, a bidder who buys Cassava has a 60% probability of also buying Carrots. This rule is significant as it shows that 20% of all transactions recorded in the system contain both items. This analysis provides important insights into the relationship patterns between items that can be used to provide item recommendations based on purchasing patterns.


Keywords


Data Mining, Association Rule Mining, Commodity Distribution, Android Studio, Auction, Apriori Algorithm

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References


W. A. Triyanto, F. Teknik, P. Studi, S. Informasi, and U. M. Kudus, “ASSOCIATION RULE MINING UNTUK PENENTUAN REKOMENDASI,” vol. 5, no. 2, pp. 121–126, 2014.

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, pp. 16–25, 2019, doi: 10.36787/jti.v13i1.78.

L. Irawan, L. H. Hasibuan, and F. Fauzi, “Analisa Prediksi Efek Kerusakan Gempa Dari Magnitudo (Skala Richter) Dengan Metode Algoritma Id3 Menggunakan Aplikasi Data Mining Orange,” J. Teknol. Inf. J. Keilmuan dan Apl. Bid. Tek. Inform., vol. 14, no. 2, pp. 189–201, 2020, doi: 10.47111/jti.v14i2.1079.

I. Maryani et al., “Penerapan Data Mining Terhadap Penjualan Pipa Pada Cv .,” J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 3, no. 2, p. 437, 2019.

A. Pangestu and T. Ridwan, “Penerapan Data Mining Menggunakan Algoritma K-Means Pengelompokan Pelanggan Berdasarkan Kubikasi Air Terjual Menggunakan Weka,” JUST IT J. Sist. Informasi, Teknol. Inf. dan Komput., vol. 11, no. 3, pp. 67–71, 2022, [Online]. Available: https://jurnal.umj.ac.id/index.php/just-it/article/view/11591

S. Choerunnisa Nurzanah, S. Alam, and T. Iman Hermanto, “Analisis Association Rule Untuk Identifikasi Pola Gejala Penyakit Hipertensi Menggunakan Algoritma Apriori (Studi Kasus: Klinik Rafina Medical Center),” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 2, pp. 132–141, 2022, doi: 10.33387/jiko.v5i2.4792.

S. Saefudin and D. Fernando, “Penerapan Data Mining Rekomendasi Buku Menggunakan Algoritma Apriori,” JSiI (Jurnal Sist. Informasi), vol. 7, no. 1, p. 50, 2020, doi: 10.30656/jsii.v7i1.1899.

A. Prasetyo, R. Sastra, and N. Musyaffa, “Implementasi Data Mining Untuk Analisis Data Penjualan Dengan Menggunakan Algoritma Apriori (Studi Kasus Dapoerin’S),” J. Khatulistiwa Inform., vol. 8, no. 2, 2020, doi: 10.31294/jki.v8i2.8994.

D. S. Nugroho, N. Islahudin, V. Normasari, and S. Z. Al Hakiim, “Penerapan Market Basket Analysis (Mba) Data Mining Menggunakan Metode Asosiasi Appriori Dan Fp-Growth Pada Wan Caffeine Addict Yogyakarta,” JISI J. Integr. Sist. Ind., vol. 11, no. 1, p. 121, 2024, doi: 10.24853/jisi.11.1.121-134.

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, pp. 262–276, 2020, doi: 10.35957/jatisi.v7i2.195.

E. Umar, D. Manongga, and A. Iriani, “Market Basket Analysis Menggunakan Association Rule dan Algoritma Apriori Pada Produk Penjualan Mitra Swalayan Salatiga,” J. Media Inform. Budidarma, vol. 6, no. 3, p. 1367, 2022, doi: 10.30865/mib.v6i3.4217.

M. A. M. Afdal and M. Rosadi, “Penerapan Association Rule Mining Untuk Analisis Penempatan Tata Letak Buku Di Perpustakaan Menggunakan Algoritma Apriori,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 5, no. 1, p. 99, 2019, doi: 10.24014/rmsi.v5i1.7379.

A. Maulidah and F. A. Bachtiar, “Penerapan Metode Association Rule Mining Untuk Asosiasi Ulasan Terhadap Aspek Tempat Wisata Jawa Timur Park 3 Application of Association Rule Mining Method for Association of Reviews on Aspect in Tourist Attraction Jawa Timur Park 3,” vol. 8, no. 5, pp. 1029–1038, 2021, doi: 10.25126/jtiik.202184417.

M. Syafii and N. E. Budiyanto, “Penerapan Digital Marketing dengan Analisis STP (Segmenting, Targeting, Positioning),” J. Inform. dan Rekayasa Perangkat Lunak, vol. 4, no. 1, p. 66, 2022, doi: 10.36499/jinrpl.v4i1.5950.

Muh Haidir Hakim, Aulia Saraswati, and Dian Ayu Zulfina, “Strategi Pemasaran Dalam Peningkatan Penjualan Keripik Keladi (Studi Kasus: Ud Karmina Kota Sorong),” J. Ilm. Tek. Inform. dan Komun., vol. 2, no. 1, pp. 1–11, 2022, doi: 10.55606/juitik.v2i1.200.

A. Tunggal and S. Budi, “Pengambilan Keputusan Strategis Pemasaran di Perguruan Tinggi dengan menggunakan Analytics Hierarchy Process (AHP),” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, 2020, doi: 10.28932/jutisi.v6i2.2748.

T. G. Eka Setiajatnika, “Kelayakan Pembangunan Gudang Pusat Distribusi Provinsi (PDP) Jawa Barat Ditinjau Dari Aspek Keuangan,” J. Ilm. Akunatansi dan Keuang., vol. 3, no. 2, pp. 364–365, 2021.

N. Anggraini, C. Fatih, M. Zaini, E. Humaidi, Sutarni, and Analianasari, “Digital Marketing Produk Pertanian di Desa Sukawaringin Kecamatan Bangunrejo Kabupaten Lampung Tengah,” J. Pengabdi. Nas., vol. 1, no. 1, pp. 36–45, 2020, [Online]. Available: https://jurnal.polinela.ac.id/JPN/article/view/1642

K. Indrawan, E. Putra, N. Santoso, and E. Santoso, “Pengembangan Aplikasi Pelelangan Ternak Burung Lovebird berbasis Android,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 7, pp. 6887–6895, 2019, [Online]. Available: http://j-ptiik.ub.ac.id

A. Febrian et al., “Pembelajaran Pembuatan Aplikasi Android Menggunakan Android Studio di Ekskul IoT SMK Wira Buana 2,” AMMA J. Pengabdi. Masy., vol. 1, no. 11, pp. 1523–1527, 2022, [Online]. Available: https://journal.mediapublikasi.id/index.php/amma/article/view/1740

Selvie, Sahpira M, Widyakusuma R, Jumhari, Kotan J, and Hartati E, “Pelatihan Pengenalan Dasar Android Studio Pada SMK Methodist 2 Palembang,” J. Pengabdi. Kpd. Masy. FORDICATE (INFORMATICS Eng. DEDICATION), vol. 3, no. 1, pp. 17–23, 2023, [Online]. Available: https://jurnal.mdp.ac.id/index.php/fordicate/article/view/5066




DOI: https://doi.org/10.52088/ijesty.v5i2.834

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International Journal of Engineering, Science and Information Technology (IJESTY) eISSN 2775-2674