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Muhammad Jalal Husain
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering

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Seleksi Fitur untuk Prediksi Hasil Produksi Agrikultur pada Algoritma K-Nearest Neighbor (KNN) Delvi Nur Aini; Bella Oktavianti; Muhammad Jalal Husain; Dian Ayu Sabillah; Said Thaufik Rizaldi; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 1 (2022): September 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4813

Abstract

Agriculture is one of the largest economic driving sectors in Indonesia. The Central Statistics Agency (BPS) in 2021 recorded that 37.02% of Indonesia's population worked in the agricultural sector. The problem faced by farmers today is the decline in yields, both in quantity and quality due to unpredictable weather, making it difficult for farmers to choose the types of plants that are suitable for planting. The application of data mining techniques has problems related to the complexity of weather parameters and natural conditions that support agricultural production, so it is very important to do feature selection, namely to form the most relevant features. This study conducted an experiment to determine the effect of implementing the Principal Component Analysis (PCA) selection feature on the performance of the K-Nearest Neighbor (KNN) algorithm which produces the highest accuracy of 99.64% in this study.
Co-Authors Bella Oktavianti Delvi Nur Aini Dian Ayu Sabillah Mustakim Mustakim Said Thaufik Rizaldi