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Puspita Sari, Desti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Penerapan Data Mining Metode K-Nearest Neighbor Untuk Memprediksi Kelulusan Siswa Sekolah Menengah Pertama Puspita Sari, Desti; Shofia Hilabi, Shofa; Agustia Hananto
SMARTICS Journal Vol 9 No 1 (2023): SMARTICS Journal (April 2023)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v9i1.8088

Abstract

Technology has become an important need for society that supports all activities to streamline student graduation with an average credit card, reduces problems related to predicting student graduation, the application of data mining to predict school graduation. The need for data mining stems from the amount of data that can be retrieved useful information and insights. Based on the results of calculations carried out using the orange modeling, the average grades of Class IX students vary from 80 to 90, and all students are declared PASS because their grades meet the graduation requirements set. is in SMPN 3 West Karawang. The K-Nearest Neighbor algorithm is useful for predicting a large number of graduates due to the consistent data processing of these predictions. For further research, use all student data to predict class growth towards student graduation.
Co-Authors Hananto, Agustia Shofia Hilabi, Shofa