Clustering and Trend Analysis of Priority Commodities in the Archipelago Capital Region (IKN) using a Data Mining Approach
(1) Universitas Siliwangi
(2) Universitas Siliwangi
(3) Universitas Siliwangi
(4) Universitas Siliwangi
(*) Corresponding Author
Abstract
The policy of moving the capital from Jakarta to East Kalimantan planned by the President of the Republic of Indonesia Joko Widodo has caused a lot of polemic among the public. There are quite a few positive and negative comments on social media regarding the policy of moving the capital. The process of moving the capital requires careful preparation. One thing that needs to be considered is food security in IKN. This research provides recommendations for the main food commodities in IKN by applying data mining. We collect food productivity data available on the official website for East Kalimantan province. These data are processed and grouped into two groups, namely horticulture and livestock products using the K-Means method. After grouping, we predict the increase in productivity of each group using the ARIMA method. This research produces output in the form of grouping commodities into horticulture and livestock products. Productivity results for each type of commodity are displayed from 2016 to 2020 based on data on the official East Kalimantan Province website. Based on this data, predictions are made using the ARIMA method to predict productivity results from 2021 to 2025. Commodities with total productivity are grouped into high-priority commodities. Grouping the amount of productivity is carried out using the clustering method by comparing the amount of productivity for each commodity and producing commodities that are low priority, middle priority, priority and top priority based on the highest to lowest productivity numbers. The cluster quality for grouping horticultural commodities is 99.1%, while the cluster quality for grouping livestock commodities is 87.5%. Hasil prediksi terbaik yaitu ketika memprediksi produksi salak dan slaughter cattle dengan model ARIMA (0, 1, 0) dan ARIMA (2, 2, 2).
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DOI: https://doi.org/10.24071/ijasst.v6i1.7798
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