Penerapan Algoritma K-Medoids untuk Klustering Bakery dan Cake yang Laris

Muhammad Ali

doi:10.47065/jussi.v2i4.4604

Muhammad Ali * Universitas Budi Darma, Medan, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/jussi.v2i4.4604

Keywords: Bakery and cake; K-Medoids; Data Mining

Abstract

The culinary business has been a promising business from the past until now, there are many kinds of businesses that are in demand, namely heavy food to light food. One of the promising businesses currently is the bakery and cake shop business. The large amount of sales competition makes the owner enthusiastic about competing to create delicious flavors and have characteristics that are different from other businesses. Mawar Bakery and Cake shop is a culinary business shop made from flour and has many branches in North Sumatra. The many delicious products created can attract the attention of customers, but every month the management must record the products that are sold a lot and the lack of interest. This is done to anticipate losses that occur every month. Therefore, the author created clustering data mining to group bakery and cake sales that were sold a lot and those that were not. The K-Medoids method is able to become an algorithm that is able to carry out the process of collecting data on Mawar bakery and cake sales because the algorithm is practical and efficient in grouping data. The results of data from sales that have been grouped can be concluded that cluster 2 is the best-selling because it gets the highest score

References

S. I. Nurhafida and F. Sembiring, “ANALISIS TEXT CLUSTERING MASYARAKAT DI TWITER MENGENAI MCDONALD ’ SXBTS MENGGUNAKAN ORANGE DATA MINING,” pp. 28–35, 2021.

Y. Mahena, M. Rusli, and E. Winarso, “Prediksi Harga Emas Dunia Sebagai Pendukung Keputusan Investasi Saham Emas Menggunakan Teknik Data Mining,” Kalbiscentia J. Sains dan Teknol., vol. 2, no. 1, pp. 36–51, 2015.

Z. Chen, P. Xu, F. Feng, Y. Qiao, and W. Luo, “Data mining algorithm and framework for identifying HVAC control strategies in large commercial buildings,” Build. Simul., vol. 14, no. 1, pp. 63–74, 2021, doi: 10.1007/s12273-019-0599-0.

D. P. U. Ulva Rizky Amanda, “Penerapan Data Mining Algoritma Hash Based Pada Data Pemesanan Buah Impor Cv. Green Uni Fruit,” vol. 5, pp. 86–93, 2021, doi: 10.30865/komik.v5i1.3653.

J. Nasir, “Penerapan Data Mining Clustering Dalam Mengelompokan Buku Dengan Metode K-Means,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 11, no. 2, pp. 690–703, 2021, doi: 10.24176/simet.v11i2.5482.

S. Alim, P. P. Lestari, and R. Rusliyawati, “Sistem Pakar Diagnosa Penyakit Tanaman Kakao Menggunakan Metode Certainty Factor Pada Kelompok Tani Pt Olam Indonesia (Cocoa) Cabang Lampung,” J. Data Min. Dan Sist. Inf., vol. 1, no. 1, pp. 26–31, 2020.

E. Irdiansyah, “Penerapan Data Mining Pada Penjualan Produk Minuman Di Pt . Pepsi Cola Indobeverages Menggunakan Metode,” J. TA/SKRIPSI, vol., no., pp. 1–6, 2017.

S. Sindi, W. R. O. Ningse, I. A. Sihombing, F. I. R. H. Zer, and D. Hartama, “Analisis algoritma k-medoids clustering dalam pengelompokan penyebaran covid-19 di indonesia,” J. Teknol. Inf., vol. 4, no. 1, pp. 166–173, 2020.

D. Marlina, N. F. Putri, A. Fernando, and A. Ramadhan, “Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak,” J. CoreIT, vol. 4, no. 2, p. 64, 2018.

I. Kamila, U. Khairunnisa, and M. Mustakim, “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 5, no. 1, pp. 119–125, 2019.

V. A. P. Sangga, “Perbandingan algoritma K-Means dan algoritma K-Medoids dalam pengelompokan komoditas peternakan di provinsi Jawa Tengah tahun 2015,” 2018.

B. Riyanto, “Penerapan Algoritma K-Medoids Clustering Untuk Pengelompokkan Penyebaran Diare Di Kota Medan (Studi Kasus: Kantor Dinas Kesehatan Kota Medan),” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, 2019.

F. Anggraini Fistiana, Evanita, and A. Akbar Riadi, “Sistem Pendukung Keputusan Pemilihan Tanaman Hias Hoya Carnosa Berbasis Android Menggunakan Metode TOPSIS,” J. Ris. Sist. Inf. Dan Tek. Inform., vol. 6, pp. 305–311.

D. Ismanda, B. Sihotang, and L. Simangunsong, “IMPLEMENTASI ALGORITMA HASH-BASED DALAM MENGETAHUI POLA PEMINJAMAN BUKU DI PERPUSTAKAAN ( STUDI KASUS : UNIVERSITAS BUDI DARMA ),” 2022.

U. Buana and P. Karawang, “TechnoXplore Jurnal Ilmu Komputer & Teknologi Informasi ISSN : 2503-054X Vol 4 No: 1, April 2019,” vol. 4, no. 1, pp. 46–55, 2019.

U. Hasanah and D. A. Mutiara, “Perbandingan Metode Cosine Similarity dan Jaccard Similarity untuk Penilaian Otomatis Jawaban Pendek,” Semin. Nas. Sist. Inf. dan Tek. Inform., no. 2019: SENSITIF 2019, pp. 1255–1263, 2019.

M. T. Nitamia and H. Februariyanti, “Analisis Sentimen Ulasan Ekpedisi J&T Expres Menggunakan Algoritma Naive Bayes,” J. Manaj. Inform. Sist. Inf., vol. 5, no. 1, pp. 20–29, 2022.

E. Tasia and M. Afdal, “Comparison Of K-Means And K-Medoid Algorithms For Clustering Of Flood-Prone Areas In Rokan Hilir District Perbandingan Algoritma K-Means Dan K-Medoids Untuk Clustering Daerah Rawan Banjir Di Kabupaten Rokan Hilir,” vol. 3, no. 1, pp. 65–73, 2023.

R. Setyadi, yesaya tommy Paulus, L. Mutawalli, S. Dwiasnati, ida bagus ary indra Iswara, and erlin windia Ambrasari, “Jurnal Manajemen Informatika & Sistem Informasi ( MISI ) jurnal Manajemen Informatika nformatika & Sistem Informasi ( MISI ) ISSN : 2614-1701 ( Cetak ) – 2614-3739 ( Online ) ii,” vol. 5, p. 29, 2022, [Online]. Available: https://www.google.com/search?q=jurnal+misi+stmik+lombok&rlz=1C1CHBF_enID993ID993&oq=jurn&aqs=chrome.0.69i59j69i57j0i131i433i512j0i433i512l2j69i60l2j69i61.12564j0j7&sourceid=chrome&ie=UTF-8.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Penerapan Algoritma K-Medoids untuk Klustering Bakery dan Cake yang Laris