Perbandingan Algoritma K-Means dan K-Medoids untuk Clustering Pada Transaksi Penjualan Minimarket
Abstract
When shopping, buyers often have difficulty finding daily necessities. One of the causes of this is because the product arrangement process in minimarkets is still carried out randomly and does not match consumer shopping patterns. On the contrary, buyers usually want to buy products through daily necessities packages, but these packages are usually not yet available in minimarkets. Identifying relationship patterns in minimarket transaction data can help overcome product arrangement and product packaging problems. By using the clustering method, objects are grouped into groups that have many similarities with each other. This method allows the grouping process to be carried out. Some of the methods in clustering include the K-Means and K-medoids methods. The purpose of this study is to group the data on goods in the minimarket which can be a guide for more neatly arranged product planning. Data grouping is divided into 3 categories namely slow, medium and fast. The results obtained show that the two algorithms produce different Davies-Bouldin Index values, with the K Medoids algorithm obtaining a lower value of 0.50387 while K-Means obtains a value of 0.50391 where the K-Medoids clustering results have better quality compared to K-Means. With the results of the grouping of these goods data, minimarkets can balance the stock of goods to prevent excess or shortage of inventory of these goods.
Downloads
References
H. Prastiwi, Jeny Pricilia, and Errissya Rasywir, “Implementasi Data Mining Untuk Menentuksn Persediaan Stok Barang Di Mini Market Menggunakan Metode K-Means Clustering,” Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM), vol. 2, no. 1, pp. 141–148, Apr. 2022, doi: 10.33998/jakakom.2022.2.1.34.
M. M. Purba and Chaerul Rahmat, “Perancangan Sistem Informasi Stok Barang Berbasis Web Di Pt Mahesa Cipta,” Jurnal Sistem Informasi Universitas Suryadarma, vol. 8, no. 2, Jun. 2021, doi: 10.35968/jsi.v8i2.721.
Aqib Fharaj Zhaky, Sutan Faisal, and Yana Cahyana, “Segmentasi Jumlah Tenaga Kesehatan Berdasarkan Kecamatan di Kabupaten Karawang Menggunakan Metode K-Medoids,” Scientific Student Journal for Information, Technology and Science, vol. V, no. 02, Jul. 2024.
Sirojul Alam, Amril Mutoi Siregar, and Ayu Ratna Juwita, “Penerapan Algoritme C4.5 Untuk Klasifikasi Kasus Covid-19,” Scientific Student Journal for Information, Technology and Science, vol. III No.1, Jul. 2022.
E. Widodo, “Pelita Teknologi Prediksi Penjurusan IPA, IPS dan BAHASA dengan Menggunakan Machine Learning Abstrak Informasi Artikel,” Jurnal Pelita Teknologi, vol. 15, no. 1, pp. 37–48, Apr. 2020.
K. Annisa, B. Serasi Ginting, and M. A. Syari, “Penerapan Data Mining Pengelompokan Data Pengguna Air Bersih Berdasarkan Keluhannya Menggunakan Metode Clustering Pada PDAM Langkat,” ALGORITMA: Jurnal Ilmu Komputer dan Informatika, vol. 06, no. 01, Apr. 2022, doi: 10.30829/algoritma.v6i1.11624.
S. Ramadhani, D. Azzahra, and T. Z, “Comparison of K-Means and K-Medoids Algorithms in Text Mining based on Davies Bouldin Index Testing for Classification of Student’s Thesis,” Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, vol. 13, no. 1, pp. 24–33, May 2022, doi: 10.31849/digitalzone.v13i1.9292.
F. Fathurrahman, S. Harini, and R. Kusumawati, “Evaluasi Clustering K-Means Dan K-Medoid Pada Persebaran Covid-19 Di Indonesia Dengan Metode Davies-Bouldin Index (DBI),” Jurnal Mnemonic, vol. 6, no. 2, pp. 117–128, Oct. 2023, doi: 10.36040/mnemonic.v6i2.6642.
R. Siagian, P. Sirait, and A. Halim, “SISTEMASI: Jurnal Sistem Informasi Penerapan Algoritma K-Means dan K-Medoids untuk Segmentasi Pelanggan pada Data Transaksi E-Commerce The Implementation of K-Means and K-Medoids Algorithm for Customer Segmentation on E-commerce Data Transactions,” May 2022. [Online]. Available: http://sistemasi.ftik.unisi.ac.id
F. Farahdinna, I. Nurdiansyah, A. Suryani, and A. Wibowo, “PERBANDINGAN ALGORITMA K-MEANS DAN K-MEDOIDS DALAM KLASTERISASI PRODUK ASURANSI PERUSAHAAN NASIONAL,” Jurnal Ilmiah FIFO, vol. 11, no. 2, p. 208, Nov. 2019, doi: 10.22441/fifo.2019.v11i2.010.
C. Fathia Palembang, M. Yahya Matdoan, S. P. Palembang, and K. Kunci, “BULLET : Jurnal Multidisiplin Ilmu Perbandingan Algoritma K-Means Dan K-Medoids Dalam Pengelompokkan Tingkat Kebahagiaan Provinsi Di Indonesia,” BULLET : Jurnal Multidisiplin Ilmu, vol. 01, no. 5, pp. 830–839, Nov. 2022.
E. Prasetyaningrum and P. Susanti, “Jurnal Media Informatika Budidarma Perbandingan Algoritma K-Means Dan K-Medoids Untuk Pemetaan Hasil Produksi Buah-Buahan,” vol. 7, pp. 1775–1783, 2023, doi: 10.30865/mib.v7i4.6477.
E. T. Ena Tasia and M. Afdal, “Perbandingan Algoritma K-Means Dan K-Medoids Untuk Clustering Daerah Rawan Banjir Di Kabupaten Rokan Hilir,” Indonesian Journal of Informatic Research and Software Engineering (IJIRSE), vol. 3, no. 1, pp. 65–73, Mar. 2023, doi: 10.57152/ijirse.v3i1.523.
N. Basuni and Amril Mutoi Siregar, “Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 6, pp. 1348–1353, Dec. 2023, doi: 10.29207/resti.v7i6.5401.
A. Satriawan, R. Andreswari, and O. N. Pratiwi, “Segmentasi Pelanggan Telkomsel Menggunakan Metode Clustering Dengan Rfm Model Dan Algoritma K-Means Telkomsel Customer Segmentation Using Clustering Method With Rfm Model And K-Means Algorithm,” Apr. 2021.
F. Tempola, M. Muhammad, and A. Mubarak, “Penggunaan Internet Dikalangan Siswa Sd Di Kota Ternate: Suatu Survey, Penerapan Algoritma Clustering Dan Validasi Dbi Use Of The Internet In The Elementary School Students In Ternate City: A Survey, Implemented Of Clustering Algorithm And Validation Dbi,” vol. 7, no. 6, 2020, doi: 10.25126/jtiik.202072370.
N. Sari, H. H. Handayani, and A. M. Siregar, “Implementasi Clustering Data Kasus Covid 19 Di Indonesia Menggunakan Algoritma K-Means,” Bianglala Informatika, vol. 11, no. 1, pp. 7–12, Mar. 2023, doi: 10.31294/bi.v11i1.14762.
Nopiti Yulistiani, Ayu Ratna Juwita, and Anis Fitri Nur Masruriyah, “Pengaruh Outlier pada Algoritma K-Medoid untuk Mengelompokan Rekanan Vendor dalam Pengadaan Barang,” Scientific Student Journal for Information, Technology and Science, vol. V NO.2, 2024.
N. A. Kamilah, T. Rohana, R. Rahmat, and A. Fauzi, “Implementasi Algoritma K-Means dan K-Medoids Dalam Klasterisasi Kasus Kekerasan Terhadap Perempuan,” Jurnal Media Informatika Budidarma, vol. 8, no. 2, p. 810, Apr. 2024, doi: 10.30865/mib.v8i2.7558.
M. Mughnyanti, S. Efendi, and M. Zarlis, “Analysis of determining centroid clustering x-means algorithm with davies-bouldin index evaluation,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Jan. 2020. doi: 10.1088/1757-899X/725/1/012128.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Algoritma K-Means dan K-Medoids untuk Clustering Pada Transaksi Penjualan Minimarket
Pages: 14-24
Copyright (c) 2024 Ajeng Shalwa Alganiu, Ayu Ratna Juwita, Rahmat Rahmat, Sutan Faisal

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).