ANALISIS DATA MINING PENJUALAN KELAPA SAWIT DENGAN MENGGUNAKAN METODE K-MEANS CLUSTERING DI RAM BS

HAFIZAH NASUTION, NPM 2109100105 (2025) ANALISIS DATA MINING PENJUALAN KELAPA SAWIT DENGAN MENGGUNAKAN METODE K-MEANS CLUSTERING DI RAM BS. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Penelitian ini dilatarbelakangi oleh kebutuhan untuk mengelola data penjualan kelapa sawit di RAM BS yang selama ini masih dicatat secara manual sehingga kurang optimal untuk mendukung pengambilan keputusan. Dengan memanfaatkan metode Data Mining, penelitian ini bertujuan menemukan pola tersembunyi yang dapat memberikan manfaat strategis bagi pengelolaan penjualan. Landasan teori penelitian ini mengacu pada konsep Knowledge Discovery in Database (KDD) yang melibatkan tahapan pengolahan data secara sistematis. Metode K-Means Clustering dipilih karena mampu mengelompokkan data berdasarkan kesamaan karakteristik tertentu secara efektif dan efisien. Tahap analisis dan perancangan dilakukan melalui pengumpulan, pembersihan, serta pengolahan data penjualan sawit yang kemudian diproses menggunakan aplikasi Orange. Perancangan model ini menghasilkan pembagian klaster berdasarkan jumlah penjualan, harga, dan frekuensi transaksi dari para pengepul atau petani. Hasil penelitian menunjukkan bahwa data penjualan sawit terbagi ke dalam beberapa klaster, yaitu klaster dengan volume penjualan tinggi dan konsisten, klaster dengan penjualan menengah, serta klaster dengan penjualan rendah namun rutin. Dari pembagian klaster tersebut dapat diketahui karakteristik pelanggan sehingga memudahkan pihak RAM BS dalam menyusun strategi pelayanan dan pemasaran yang lebih tepat sasaran. Kesimpulan dari penelitian ini adalah penerapan metode K-Means Clustering terbukti mampu mengelompokkan data penjualan sawit secara lebih terstruktur. Dengan demikian, hasil penelitian ini diharapkan dapat membantu RAM BS dalam pengambilan keputusan bisnis yang lebih efektif dan berbasis data. Kata Kunci: Data Mining, K-Means Clustering, Penjualan Kelapa Sawit, Segmentasi Pelanggan, Orange ================================================================================================ This research is motivated by the need to manage palm oil sales data at RAM BS, which has been recorded manually, making it less than optimal for supporting decision-making. By utilizing Data Mining methods, this study aims to discover hidden patterns that can provide strategic benefits for sales management. The theoretical basis of this research refers to the concept of Knowledge Discovery in Database (KDD), which involves systematic Data Processing stages. The K-Means Clustering method was chosen because it is able to group data based on certain similar characteristics effectively and efficiently. The analysis and design stages are carried out through the collection, cleaning, and processing of palm oil sales data, which are then processed using the Orange application. This model design results in a division of Clusters based on sales volume, price, and transaction frequency from collectors or farmers. The results show that palm oil sales data is divided into several Clusters: Clusters with high and consistent sales volume, Clusters with medium sales volume, and Clusters with low but regular sales volume. From this Cluster division, customer characteristics can be identified, making it easier for RAM BS to develop more targeted service and marketing strategies. The conclusion of this study is that the application of the K-Means Clustering method has proven to be able to group palm oil sales data in a more structured manner. Therefore, the results of this study are expected to assist RAM BS in making more effective, data-driven business decisions. Keywords: Data Mining, K-Means Clustering, Palm Oil Sales, Customer Segmentation, Orange

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Data Mining, K-Means Clustering, Penjualan Kelapa Sawit, Segmentasi Pelanggan, Orange==============Data Mining, K-Means Clustering, Palm Oil Sales, Customer Segmentation, Orange
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	22 Oct 2025 09:53
Last Modified:	22 Oct 2025 09:53
URI:	http://repository.ulb.ac.id/id/eprint/1844

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