Artificial Neural Network Untuk Prediksi Kelulusan Calon Peserta Didik Baru (Studi Kasus: MAN 1 Padangsidimpuan)

Rizqi Nusabbih Hidayatullah Gaja(1*), Doni Karseno(2), Amir Salim Khairul Rijal(3), Dwana Abdi Juliantho(4),

(1) Universitas Islam Negeri Syekh Ali Hasan Ahmad Addary Padangsidimpuan, Indonesia
(2) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(3) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(4) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(*) Corresponding Author

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.

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References


R. Pahlevi And P. Rosyani, “Perancangan Sistem Informasi Penerimaan Peserta Didik Baru Berbasis Web Pada Mi Madinatunnjah,” Jurnal Riset Komputer), Vol. 8, No. 5, Pp. 2407–389, 2021, Doi: 10.30865/Jurikom.V8i5.3626.

A. F. A. Naibaho And A. Zahra, “Prediksi Kelulusan Siswa Sekolah Menengah Pertama Menggunakan Machine Learning,” Jurnal Informatika Dan Teknik Elektro Terapan, Vol. 11, No. 3, Jul. 2023, Doi: 10.23960/Jitet.V11i3.3056.

W. Halim And P. Mudjihartono, “Kecerdasan Buatan Dalam Teknologi Kedokteran: Survey Paper,” Konstelasi: Konvergensi Teknologi Dan Sistem Informasi, Vol. 2, No. 1, 2022.

M. Shandy, T. Putra, And Y. Azhar, “Perbandingan Model Logistic Regression Dan Artificial Neural Network Pada Prediksi Pembatalan Hotel,” 2021.

D. T. Saputro And D. Swanjaya, “Analisa Prediksi Harga Saham Menggunakan Neural Network Dan Net Foreign Flow.”

W. Santoso And P. Sukmasetya, “Jurnal Media Informatika Budidarma Prediksi Volume Sampah Di Tpsa Banyuurip Menggunakan Metode Backpropagation Neural Network,” 2023, Doi: 10.30865/Mib.V7i1.5499.

J. Permadi, H. Rhomadhona, W. Aprianti, P. A. Negeri Tanah Laut Jalan Yani Km, D. Panggung, And K. Pelaihari, “Perbandingan K-Nearest Neighbor Dan Backpropagation Neural Network Dalam Prediksi Resiko Diabetes Tahap Awal,” Kumpulan Jurnal Ilmu Komputer (Klik), Vol. 08, No. 3, 2021.

N. Selle, N. Yudistira, And C. Dewi, “Perbandingan Prediksi Penggunaan Listrik Dengan Menggunakan Metode Long Short Term Memory (Lstm) Dan Recurrent Neural Network (Rnn),” Vol. 9, No. 1, Pp. 155–162, 2022, Doi: 10.25126/Jtiik.202295585.

E. Febrion Rahayuningtyas, F. Novia Rahayu, Y. Azhar, And I. Artikel, “Prediksi Harga Rumah Menggunakan General Regression Neural Network,” Jurnal Informatika, Vol. 8, No. 1, 2021, [Online]. Available: Https://Archive.Ics.Uci.Edu/Ml/Datasets/Real

A. Lasarudin And R. Maku, “Prediksi Pertumbuhan Jumlah Penduduk Menggunakan Algoritma Neural Network (Nn),” 2021. [Online]. Available: Http://Journal.Umgo.Ac.Id/Index.Php/Jsimgo

R. Kania, T. Intan Solihati, J. Teknik Informatika, U. Banten Jaya Jl Syeh Nawawi Albantani, And S. -Banten, “Prediksi Tingkat Kelulusan Mahasiswa Program Studi Teknik Informatika Universitas Banten Jaya Menggunakan Algoritma Neural Network,” 2022.

A. Tiarasari And E. Haryatmi, “Penerapan Convolutional Neural Network Deep Learning Dalam Pendeteksian Citra Biji Jagung Kering,” Jurnal Resti (Rekayasa Sistem Dan Teknologi Informasi), Vol. 5, No. 2, Pp. 265–271, Apr. 2021, Doi: 10.29207/Resti.V5i2.3040.

M. D. Chandra, E. Irawan, I. S. Saragih, A. P. Windarto, And D. Suhendro, “Penerapan Algoritma K-Means Dalam Mengelompokkan Balita Yang Mengalami Gizi Buruk Menurut Provinsi,” Bios : Jurnal Teknologi Informasi Dan Rekayasa Komputer, Vol. 2, No. 1, Pp. 30–38, Mar. 2021, Doi: 10.37148/Bios.V2i1.19.




DOI: http://dx.doi.org/10.30645/j-sakti.v8i1.782

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