OPTIMASI PENGELOLAAN STOK MELALUI IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR PADA CV. WIVIOS GLOBAL INDO

Putra, Steven Adi (2024) OPTIMASI PENGELOLAAN STOK MELALUI IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR PADA CV. WIVIOS GLOBAL INDO. Undergraduate thesis, Universitas Katolik Musi Charitas.

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Abstract

Technological advancements have introduced new challenges in stock management, particularly for companies such as CV. Wivios Global Indo. Common issues include the accumulation of unsold goods and stock shortages of highly demanded items, both of which can lead to financial losses. This study aims to enhance stock management by applying the K-Nearest Neighbor (K-NN) algorithm to sales classification. Data mining techniques, specifically classification, were employed to identify sales patterns from a dataset of 145,075 sales records, including attributes such as product name, quantity sold, product weight, and price. The analysis was performed using Google Colab with the Scikit-Learn, numpy, and Pandas libraries. The results indicate that the K-NN algorithm can develop a model capable of predicting whether items are performing well or poorly in terms of sales. The model achieved an accuracy of 96%, precision of 96.1%, recall of 95.9%, and an F1-score of 95.9%, with an optimal K value of 43. In conclusion, the K-NN method has proven effective in predicting sales, leading to more optimal stock management.

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