Ranglalin, Habel David (2023) Penerapan Data Mining pada Data Impor Beras di Indonesia dengan Menggunakan K-Means Clustering. Undergraduate thesis, Fakultas Teknologi Informasi Universitas Merdeka Malang.

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Abstract

For most Indonesians, rice is a staple food. Every year, Indonesian people consume more rice. Importing involves purchasing commodities or services from abroad for domestic consumption, as capital goods, as inputs for domestic production, or all of the above. The main purpose of imports in the importing country is to meet domestic demand, but it is also used to support the growth of the country's industrial sector and lay the foundation for future international trade. This study discusses the Application of Data Mining on Rice Imports in Indonesia Using K-Means Clustering. The data source for this research was collected based on export-import documents produced by the Directorate General of Customs and Excise. The data used in this research is data from 2000-2021 which consists of 11 countries. The variables used are (1) total net weight imports (net) and (2) CIF (Cost, Insurance, Freight) values. The data will be processed by clustering into 3 clusters, namely the high import level cluster, the medium import level cluster and the low import level cluster. The data centroid for the high import level cluster is 8,949,703.3, the data centroid for the medium import level cluster is 581,303.5 and the data centroid for the low import level cluster is 392.9. In order to obtain an assessment based on the rice import index with 2 high-level import cluster countries, Vietnam and Thailand, 3 medium-level import cluster countries, China, India, and Pakistan, and 6 low-level import cluster countries, the United States, Taiwan, Singapore, Myanmar, Japan, and other countries. This can be input to the government, countries that are the highest priority in rice import activities based on the clusters that have been carried out.

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