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All Journal JURNAL MUQODDIMAH : Jurnal Ilmu Sosial, Politik dan Hummaniora J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Komtekinfo
Gaja, Rizqi Nusabbih Hidayatullah
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

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Artificial Neural Network Untuk Prediksi Kelulusan Calon Peserta Didik Baru (Studi Kasus: MAN 1 Padangsidimpuan) Gaja, Rizqi Nusabbih Hidayatullah; Karseno, Doni; Rijal, Amir Salim Khairul; Juliantho, Dwana Abdi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.782

Abstract

There is fierce rivalry amongst schools as a result of competitiveness in the educational field. Therefore, the processing of an educational institution must keep up with technological advancements. Artificial intelligence (AI) and intelligent systems, sometimes called AI systems, are used to simulate human-like critical thinking and intelligent behavior. A popular technique for categorization and prediction is the artificial neural network. MAN 1 Padangsidimpuan faces the challenge of determining whether new students will graduate because each year's quota of applications is exceeded. In this study, potential new students at MAN 1 Padangsidimpuan will have their graduation dates predicted, and the degree of prediction accuracy will be assessed. Artificial Neural Networks are the research approach employed in this study. The steps that are completed include problem formulation and identification, literature review, data gathering, pre-processing, processing, and assessment. Data about potential new students for the academic year 2023–2024 was utilized in this study. The study's findings demonstrate that the neural network model produces outcomes that are 6-6-2 (6 input neurons, 6 hidden layers, and 2 output neurons). 97.31% was the greatest accuracy performance level attained, while 90.30% was the lowest. 615 people made the most accurate predictions, while 569 people made the fewest accurate ones. There were 17 forecasts that were wrong the least and 63 wrong predictions the most.
Co-Authors Gunadi Widi Nurcahyo Juliantho, Dwana Abdi Karseno, Doni Rijal, Amir Salim Khairul Sarjon Defit Veri, Jhon