Transformation of Food Security Analysis: A Data Science Approach to Agricultural Commodities in Ntt

Rolland E. Fanggidae; Rikhard T. Ch. Bolang; Aldarine Molidya; Edy Purwanto; Ginanjar Aji Nugroho; Bonifasius Irmgardus Nggajo

doi:10.55927/ijba.v5i6.15921

Authors

Rolland E. Fanggidae Universitas Nusa Cendana
Rikhard T. Ch. Bolang Universitas Nusa Cendana
Aldarine Molidya Universitas Nusa Cendana
Edy Purwanto Universitas Nusa Cendana
Ginanjar Aji Nugroho Universitas Nusa Cendana
Bonifasius Irmgardus Nggajo Universitas Nusa Cendana

DOI:

https://doi.org/10.55927/ijba.v5i6.15921

Keywords:

Food Security, Forecasting, Clustering.

Abstract

This study aims to assess food commodity data forecasting in East Nusa Tenggara (NTT) Province using the ARIMA model and to conduct a clustering analysis of food security status using the K-Means Clustering and Hierarchical Agglomerative Clustering (HAC) approaches. The analytical method used was the Autoregressive Integrated Moving Average (ARIMA) model to calculate the forecast of time series food commodity data in East Nusa Tenggara (NTT) Province, and the food security analysis used K-Mean Clustering and Hierarchical Agglomerative Clustering (HAC). The ARIMA test results produced forecasts of food commodity data or variables for 2025 to 2027. However, these results did not yield satisfactory results when evaluated using three different coefficients, one of which was the Mean Absolute Percentage Error (MAPE), which produced a relatively large value. This concludes that the test results for forecasting data or food commodity variables in NTT are inaccurate, requiring additional factors capable of producing good predictions. The importance of food security policies that adapt to regional capacity. Cluster 1 focuses on increasing production and stabilizing prices through simple irrigation, agricultural assistance, and market operations. Cluster 3 focuses on strengthening logistics through road infrastructure, distribution hubs, and digitalization. Across clusters, a simple food information system, interregional partnerships, and local climate adaptation are needed to make policies more realistic and measurable.

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