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All Journal JURIKOM (Jurnal Riset Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Baenil Huda
Universitas Buana Perjuangan Karawang, Karawang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Strategi Promosi untuk Meningkatkan Penjualan Kedai Kopi Desimal Menggunakan Algoritma K-Medoids Clustering Anggi Octa Fadilah; Baenil Huda; Agustia Hananto; Tukino Tukino
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5561

Abstract

The Decimal coffee shop is a coffee shop located in the city of Karawang and is a coffee shop that is already busy with many customers, the Decimal coffee shop has been established since 2020 until now. Decimal coffee shop offers 35 diverse menu items, and sales can fluctuate, sometimes increasing and sometimes decreasing in the quality of the menu items sold. In this problem, sales data at Decimal coffee shops is not used to improve sales quality, the sales data is only used as an archive for the coffee shop, if the data is analyzed properly, it will be useful to determine which menu items are selling well and which are not selling well. By analyzing sales data, it will be possible to determine which menu needs to be improved in terms of sales. This information can then be used by the coffee shop as a reference in developing a promotional strategy aimed at increasing sales of the menu product. To find out how many menus are sold at Decimal coffee shops, a clustering study was carried out. This research was conducted by analyzing sales data in excel form, the K-Medoids method was used to create clusters based on product sales data that had been obtained from the Decimal Coffee Shop. From the clustering results, there are 3 clusters which are classified as high, medium, and low, and the accuracy is determined using the RapidMiner tool. Of the 35 items analyzed, the first cluster contains 18 items which are rated the highest, the second cluster contains 12 items which are classified as moderate, and the third cluster contains 5 items which are classified as the lowest. From these results there are 5 items on sales that are classified as low, therefore a promotional strategy is needed to increase the menu product.
Klasterisasi Siswa Berdasarkan Profil Akademik dan Karakteristik Belajar Menggunakan Algoritma K-Means untuk Mendukung Pembelajaran Attaya Faiharani; Baenil Huda; Fitria Nurapriani; April Lia Hananto
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9572

Abstract

Grouping students based on academic and non-academic characteristics is important to support the development of more targeted educational guidance strategies in schools. The main problem addressed in this study is the absence of objective data-based student mapping, which causes development programs to remain general and less targeted. This study aims to classify students using the K-Means clustering algorithm based on academic profiles and other supporting variables, and to evaluate cluster quality using the silhouette coefficient method. The research stages include data preprocessing, determining the optimal number of clusters, clustering using K-Means, and evaluating the clustering result. The results showed that four clusters were selected as the final configuration with a silhouette score of 0,1093, with cluster membership distributed into 12, 4, 2, and 2 students. Visualization using principal component analysis shows that most clusters are sufficiently well separeted. This study contributes a data-driven student grouping model that can be used as a basis for recommending student potential development according to the characteristics of each group.
Co-Authors Anggi Octa Fadilah April Lia Hananto Attaya Faiharani Fitria Nurapriani Hananto, Agustia Tukino Tukino