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Analisis Kelayakan Finansial pada Proyeksi Bisnis Sentra Mebel di Kabupaten XYZ Khasanah, Annisa Uswatun; Wicaksono, Z Arifin; Suryani, Dewi Amanatun; Permanawati, Zaenab Fitria
Journal of Appropriate Technology for Community Services Vol. 6 No. 1 (2025)
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jattec.vol6.iss1.art1

Abstract

MSMEs (Micro, Small, and Medium Enterprises) play a crucial role in supporting Indonesia’s economy, including the furniture sector, which holds significant development potential. XYZ Regency is well-known as a furniture MSME hub with products exported widely. However, challenges such as raw material cutting efficiency, production quality standardization, and limited working capital remain obstacles for business actors. The XYZ Regency Government is planning to develop a furniture industrial center to improve community welfare, increase global market competitiveness, and benefit various stakeholders. This study aims to evaluate the business feasibility of a furniture industrial center in XYZ from a financial perspective. The quantitative method used involved data collection through surveys and questionnaires engaging 1,155 furniture MSMEs in A and B Subdistricts. Analysis was conducted by calculating cost of goods manufactured (COGM), labor costs, overhead, and projected profit and loss. The results indicate that annual labor costs reach IDR 1,513,046,088, overhead is IDR 997,270,000, and non-production operational costs are IDR 724,560,000. The estimated Cost of Goods Sold (COGS) is IDR 3,234,876,088, with annual revenue potential of IDR 4,464,000,000 and a net profit of IDR 629,123,912. Projections show that the Break-Even Point (BEP) is expected to be achieved between the 8th and 9th years, with optimum operational targets being reached by the 10th year. This study provides a basis for XYZ Regency Government to make informed decisions regarding the development of the furniture industrial center business in the future.
Analysis of consumer characteristics on retail business with clustering analysis method and association rule for selling improvement strategy recommendations Khasanah, Annisa Uswatun; Baihaqie, Muhammad Rafly Qowi
OPSI Vol 17 No 1 (2024): ISSN 1693-2102
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v17i1.11411

Abstract

In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.