Comparison of K-Means and K-Medoids Clustering Algorithms for Export and Import Grouping of Goods in Indonesia

Ulvi, Hazrul Anshari (2024) Comparison of K-Means and K-Medoids Clustering Algorithms for Export and Import Grouping of Goods in Indonesia. Sinkron : Jurnal dan Penelitian Teknik Informatika, 8 (3).

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Abstract

International relations affect the economic growth of each country, which can affect the economic growth of each country. As a result, global economic growth is necessary, which means that the global economy has a greater capacity to produce goods and services. Exports and imports are very important to drive economic growth. but if exports and imports are not balanced, it will have a bad impact if the value of imports is greater than exports, export prices abroad will definitely fall. An analysis comparing export and import categories is needed to determine which goods are most imported and exported in Indonesia in 2021-2023. This study uses a quantitative methodology and machine learning methods, namely k-means and k-medoids algorithms. These two methods will be compared to determine which is the most effective for export and import data of goods in Indonesia in 2021-2023. The results of the study were obtained by K-Means more effectively in handling data on the grouping of exports and imports of goods in Indonesia in 2021-2023. The dataset shows the results of the evaluation of KMeans using DBI of 0.59, while the results of the evaluation using K-Medoids show a result of 1.7868. Because the evaluation value of K-Means has low computing performance compared to K-Medoids. The largest amount of the value and weight of exports and imports of goods in Indonesia is in C1 where in the HS code [27], namely Mineral fuels with a total export value of goods in 2021 to 2023 of 134,999,470,522 US$ and a total import value of 113,714,568,740 US$. Meanwhile, the total export weight of goods from 2021 to 2023 in mineral fuel goods is 1,505,006,250,327 Kg or around 1,658,985,413 tons and the total import weight is 186,446,782,134 Kg or around 205,522,397 tons.

Jenis Item:	Artikel
Subjects:	300 Social sciences > 330 Economics
Divisions:	Fakultas Sains dan Teknologi > Ilmu Komputer
Pengguna yang mendeposit:	Mrs Siti Masitah
Date Deposited:	22 Jan 2025 03:14
Last Modified:	22 Jan 2025 03:14
URI:	http://repository.uinsu.ac.id/id/eprint/23634

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