JIHAN AZZAHRA, NPM 210910005 (2025) IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING MENGGUNAKAN MACHINE LEARNING UNTUK ANALISIS STOK BARANG PADA TOKO R2 COLLECTION DI RANTAUPRAPAT. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Penelitian ini bertujuan untuk mengimplementasikan algoritma K-Means Clustering menggunakan machine learning dalam menganalisis stok barang pada Toko R2 Collection di Rantauprapat. Toko ini menghadapi masalah ketidakseimbangan stok barang, seperti produk yang cepat habis dan produk yang menumpuk di gudang sehingga berdampak pada efisiensi operasional. Penelitian dilakukan dengan mengelompokkan data penjualan berdasarkan variabel stok masuk, stok keluar, dan stok akhir untuk mengidentifikasi produk dengan permintaan rendah (cluster 1), sedang (cluster 2), dan tinggi (cluster 3). Data diolah menggunakan Python dan Google Collaboratory, dengan bantuan metode evaluasi seperti Elbow Method, Davies-Bouldin Index (DBI), dan Silhouette Coefficient (SC) untuk menentukan jumlah cluster yang optimal. Hasil dari pengelompokan adalah cluster 1 berjumlah 7 produk dengan permintaan rendah, cluster 2 berjumlah 4 produk dengan permintaan sedang, dan cluster 3 berjumlah 44 produk dengan permintaan tinggi. Dengan hasil tersebut menunjukkan bahwa penerapan K-Means dapat membantu dalam pengambilan keputusan pengadaan barang yang lebih tepat dan efisien, serta meningkatkan manajemen persediaan toko secara keseluruhan. Kata Kunci: K-Means Clustering, Machine Learning, Persediaan Barang, Data Mining, Google Colab ================================================================================================== This research aims to implement the K-Means Clustering algorithm using machine learning in analyzing the stock of goods at R2 Collection Store in Rantauprapat. This store faces stock imbalance problems, such as products that run out quickly and products that accumulate in the warehouse, which impacts operational efficiency. The research was conducted by clustering sales data based on the variables of incoming stock, outgoing stock, and ending stock to identify products with low (cluster 1), medium (cluster 2), and high (cluster 3) demand. The data is processed using Python and Google Collaboratory, with the help of evaluation methods such as the Elbow Method, Davies-Bouldin Index (DBI), and Silhouette Coefficient (SC) to determine the optimal number of clusters. The results of clustering are cluster 1 totaling 7 products with low demand, cluster 2 totaling 4 products with medium demand, and cluster 3 totaling 44 products with high demand. With these results, it shows that the application of K-Means can help in making more precise and efficient procurement decisions, as well as improving overall store inventory management. Keywords: K-Means Clustering, Machine Learning, Stock of Goods, Data Mining, Google Colab
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | K-Means Clustering, Machine Learning, Persediaan Barang, Data Mining, Google Colab==================K-Means Clustering, Machine Learning, Stock of Goods, Data Mining, Google Colab |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 11 Jul 2025 08:56 |
| Last Modified: | 11 Jul 2025 08:56 |
| URI: | http://repository.ulb.ac.id/id/eprint/1565 |
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