PENERAPAN METODE K-MEANS CLUSTERING UNTUK PENGELOMPOKAN STOK BARANG PADA SISTEM INVENTORI TOKO KACAMATA NOBITA

indah

Authors

Indah Politeknik Negeri Padang

Keywords:

K-Means Clustering; Laravel; Metode Elbow; Pengelompokan Stok; Sistem Informasi Inventori

Abstract

Inventory mismanagement can lead to overstocking of slow-moving items and stockouts of high-demand products. This study aims to develop a web-based inventory information system at Nobita Eyewear Store that integrates K-Means Clustering to group stock based on historical sales data. The dataset consists of 308 transactions from January to April 2026 across 113 product types. Stock, purchase price, total inbound, and total outbound attributes were normalized using Min-Max Normalization. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The system was developed using Laravel and SQLite with the waterfall method. The results show that K=3 is optimal, converging at the fifth iteration and producing very fast-moving, fast-moving, and slow-moving categories. Black Box Testing confirmed that all features functioned as required. A User Acceptance Test with 30 respondents yielded an 89% acceptance rate, categorized as very good. The system helps Nobita Eyewear Store prioritize stock more objectively. Research limitations are discussed for future development

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