RENI YUSNITA, NPM 2309105145 (2025) ANALISIS DATA MINING UNTUK MENGETAHUI POLA PEMAKAIAN, STOK DAN PENGAMBILAN BARANG DI WAREHOUSE TELKOM MENGGUNAKAN METODE APRIORI. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Penelitian ini membahas penerapan algoritma Apriori untuk membantu PT Telkom Indonesia dalam mengidentifikasi pola pemakaian, stok, dan pengambilan barang di gudang, yang berangkat dari masalah ketidaksesuaian stok dengan kebutuhan operasional. Data mining, khususnya algoritma Apriori dengan parameter support dan confidence, digunakan sebagai landasan teori untuk menemukan aturan asosiasi antar item dalam data transaksi. Analisis dilakukan melalui pengumpulan 20 data transaksi, preprocessing, hingga perancangan sistem berbasis binary matrix agar pola keterkaitan barang dapat terlihat dengan nilai support minimal 50%. Hasil penelitian menunjukkan bahwa item seperti AC-OF SM-1B, S-CLAMP-SPRINER, dan SOC-ILS memiliki support tinggi (0,8), serta terdapat aturan asosiasi dengan confidence 1 yang menandakan keterkaitan kuat antar barang dan dapat dijadikan dasar pengaturan stok serta tata letak gudang. Dengan demikian, penerapan algoritma Apriori terbukti mampu mengungkap pola pemakaian dan hubungan antar barang sehingga membantu mengurangi risiko overstocking maupun stockout, serta mendukung operasional gudang PT Telkom secara lebih efisien. Kata Kunci: Asosiasi, Metode Apriori, Metode Fp-Growth, Pola Pemakaian, Data Mining ============================================================================ This study discusses the application of the Apriori algorithm to assist PT Telkom Indonesia in identifying usage, stock, and retrieval patterns of goods in the warehouse, which starts from the problem of stock mismatch with operational needs. Data mining, specifically the Apriori algorithm with support and confidence parameters, is used as a theoretical basis to find relationship rules between items in transaction data. The analysis is carried out through the collection of 20 transaction data, preprocessing, and designing a binary matrix based system so that the pattern of item relationships can be seen with a support value of at least 50%. The results show that items such as AC-OF-SM-1B, S CLAMP-SPRINER, and SOC-ILS have high support (0.8), and there are association rules with confidence 1 which indicates a strong relationship between items and can be used as a basis for stock management and warehouse layout. Thus, the application of the Apriori algorithm is proven to be able to reveal usage patterns and relationships between items, thereby helping to reduce the risk of overstocking and stockouts, as well as supporting more efficient warehouse operations at PT Telkom. Keywords: Association, Apriori Method, Fp-Growth Method, Usage Pattern, Data Mining
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Asosiasi, Metode Apriori, Metode Fp-Growth, Pola Pemakaian, Data Mining=============Association, Apriori Method, Fp-Growth Method, Usage Pattern, Data Mining |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science 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: | 20 Nov 2025 07:41 |
| Last Modified: | 20 Nov 2025 07:41 |
| URI: | http://repository.ulb.ac.id/id/eprint/1988 |
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