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All Journal Jurnal Ilmu Komputer dan Sistem Informasi
Maarif, Muhammad Rafa
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Computer Science & IT Library & Information Science

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A Meningkatkan Akurasi KNN Menggunakan Metode Particle Swarm Optimization pada Klasifikasi Kualitas Buah Apel Amelia, Mutiara Mega; Harafani, Hani; Maarif, Muhammad Rafa; Fazrin, Bintang Maulana
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 4 No. 1 (2025): Januari 2025
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v4i1.173

Abstract

Apple quality is a crucial aspect in the agriculture and food processing industry, quality assessment is essential to meet consumer standards and ensure customer satisfaction. This research explores the use of K-nearest Neighbor (KNN) algorithm optimized with Particle Swarm Optimization (PSO) for apple quality classification based on the attributes of size, weight, sweetness, crispness, juiciness, ripeness, and acidity. The dataset used contains 4000 apple samples that have been measured and evaluated based on these attributes. The results showed that setting the population size and inertia weights in the PSO algorithm successfully optimized the performance of KNN in apple quality classification. The combination of population size and inertia weight in the PSO algorithm can increase KNN's accuracy to 91.15% with a recall value of 89.53% and precision of 92.59%. This research also has a better accuracy value than previous research on apple quality classification.
Co-Authors Amelia, Mutiara Mega Fazrin, Bintang Maulana Hani Harafani, Hani