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All Journal Jurnal Riset Informatika Journal of Informatics and Advanced Computing
Shafa Aurelia Putri
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Computer Science & IT

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Perbandingan Sistem Pendukung Keputusan Dalam Penentuan Top 3 Best Product Kategori Baby Travel Gear (Studi Kasus : Babykhahouse) Shafa Aurelia Putri; Tasya Ilmelia Sabarwati Sianturi; Shela Pratiwi; Desti Fitriati; Andiani
Journal of Informatics and Advanced Computing (JIAC) Vol 3 No 2 (2022): Journal of Informatics and Advanced Computing (JIAC)
Publisher : Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/jiac.v3i2.4540

Abstract

ABSTRAKTujuan dari penelitian ini yaitu untuk membuat perusahaan babykhahouse mendapatkan keputusan mengenai Top 3 best product dalam kategori Baby Travel Gear pada bulan Oktober 2022. Sistem Pendukung Keputusan (SPK) adalah suatu penerapan sistem perangkat lunak yang dikembangkan secara khusus untuk membantu proses pengambilan keputusan berdasarkan kriteria yang sudah ditentukan. Dalam penelitian ini terdapat 3 metode sistem pendukung keputusan yang akan digunakan yaitu Simple Additive Weighting (SAW), Weighted Product (WP) dan Metode Perbandingan Eksponensial (MPE). Kualitas dan harga produk menjadi nilai penting dalam pemilihan produk untuk bayi. Simple Additive Weighting (SAW), Weighted Product (WP) dan Eksponensial merupakan metode dalam Sistem Pendukung Keputusan (SPK) yang akan digunakan untuk membantu “Babykhahouse” dalam menentukan 3 produk bayi kategori travelling terbaik, dimana ketiga metode tersebut akan dibandingkan untuk mengetahui metode terbaik dalam mengatasi permasalahan tersebut. Kata Kunci: Simple Additive Weighting (SAW), Weighted Product (WP), Metode Perbandingan Eksponensial (MPE). ABSTRACTThe purpose of this research is to make the babykhahouse company get a decision regarding the Top 3 best products in the Baby Travel Gear category in October 2022. Decision Support System (SPK) is an implementation of a software system developed specifically to assist the decision-making process based on predetermined criteria. In this study there are 3 methods of decision support systems that will be used namely Simple Additive Weighting (SAW), Weighted Product (WP) dan Exponential Comparison Method. Product quality and price are important values ​​in product selection for babies.Simple Additive Weighting (SAW), Weighted Product (WP) and Exponential is a method in the Decision Support System (SPK) that will be used to help "Babykhahouse" in determining the 3 best travelling category baby products, where the three methods will be compared to find out the best method for solving these problems. Keywords: Simple Additive Weighting (SAW), Weighted Product (WP), Exponential Comparison Method.
Application of Data Mining Using Methods K-Means Clustering for Clustering Baby Goods Rental Patterns (Case Study: Baby Kha House Store) Roja' Putri Cintani; Shafa Aurelia Putri; Desti Fitriati
Jurnal Riset Informatika Vol. 6 No. 2 (2024): March 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i2.265

Abstract

A baby item rental business is a practical option for parents who want to fulfill their baby's needs without buying them. Babykhahouse is one of the stores that offer rental services for various kinds of mother, baby, and child equipment. As the volume of data related to rental transactions increases, it is also increasingly difficult to know and understand the rental patterns found at the Babykhahouse store. This research aims to get a rental pattern that can later be a consideration for the store in determining promos and adding stock items. In handling these problems, data mining methods, especially clustering, are applied to group data and classify it based on certain groups. The clustering method used in this research is K-Means Clustering, which generates clusters to find similar rental patterns. In this study, 2 (two) types of clusters were formed, where, based on the 2 (two) clusters, it will be known which products have high and low rental rates. Based on the research, the results are 100 data in cluster 0, or the unsold cluster, and 64 in cluster 1, or the sold cluster. Products included in cluster 1 or in-demand clusters are products with a high level of sales.
Co-Authors Andiani Desti Fitriati Roja' Putri Cintani Shela Pratiwi Tasya Ilmelia Sabarwati Sianturi