Particle Swarm optimization-based Neural Network method for predicting satisfaction of recipients of internet data quota assistance from the ministry of education and culture
Annahl Riadi(1); Irvan Muzakkir(2*); Marniyati H. Botutihe(3);
(1) Universitas Pohuwato
(2) Universitas Pohuwato
(3) Universitas Pohuwato
(*) Corresponding Author
AbstractThe free quota assistance program for students and lecturers is an assistance program provided by The Ministry of Education and Culture. This program has been implemented since the spread of the covid-19 pandemic in all regions of Indonesia. This assistance is expected to help students and lecturers carry out online learning caused by the pandemic covid-19. This study aims to predict the satisfaction level of the users so that it can help the government in advancing education. The data processing is carried out using the rapid miner application and the neural network method with particle swarm optimization. From the results of data processing, the accuracy value for the neural network algorithm model is 42.44%, and the accuracy value for the PSO-based neural network algorithm model is 91.86%. KeywordsNeural Networks; PSO; Covid-19; Quota Assistance; Pohuwato
|
Full Text:PDF |
Article MetricsAbstract view: 384 timesPDF view: 126 times |
Digital Object Identifierhttps://doi.org/10.33096/ilkom.v14i1.1094.52-56 |
Cite |
References
Presiden Republik Indonesia, Keppres Republik Indonesia Nomor 11 Tahun 2O2O Tentang Penetapan Kedarupgtan Kesehatan Masyarakat Corona Virus Disease 2O19 (Covid- 19), Penetapan Kedaruratan Kesehat. Masy., no. 031003, 2020.
ainun Naim, Peraturan Sekretaris Jenderal Kementerian Pendidikan, Kebudayaan, Riset, Dan Teknologi Nomor 14 Tahun 2021, 2021.
S. Sahril, Analisis Kelayakan Penerima Bantuan Covid-19 Menggunakan Metode KMeans Pada Kecamatan Sagulung Kota Batam, Comasie, vol. 05, no. 01, 2021.
yusak Novanto, Motivasi Belajar, Penyesuaian Diri, Kepuasan Mahasiswa Dan Prestasi Akademik Mahasiswa Penerima Beasiswa Di Universitas X, Senin Desember 2020.
A. Riadi and M. H. Botutihe, Visitor satisfaction prediction of the Pantai Pohon Cinta beach tourism using the backpropagation algorithm with particle swarm optimization feature selection, no. 1, p. 85.
J. S. Informasi et al., Ermawati, Algoritma Klasifikasi C4.5 Berbasis Particle Swarm Optimization Untuk Prediksi Penerima Bantuan Pangan Non Tunai 513, vol. 8, no. September, pp. 513528, 2019.
B. Hermanto and A. Jaelani, Penerapan Data Mining Untuk Prediksi Penerima Bantuan Pangan Non Tunai (Bpnt) Di Desa Wanacala Menggunakan Metode Nave Bayes, SIGMA - J. Teknol. Pelita Bangsa, vol. 18, no. 4, pp. 6472, 2019.
Asril, Jaringan Syaraf Tiruan Backpropagation Untuk Prediksi Jumlah Pengunjung Kolam Renang, J. SIMTIKA, vol. 2, no. 1, pp. 28, 2019.
k. faezehossadat and j. sayed mohammadmehdi, Predicting The 28 Days Compressive Strength Of Concrete Using Artificial Neural Network, i-managers J. Civ. Eng., vol. 6, no. 2, p. 1, 2016.
M. H. Botutihe, Model Neural Network Berbasis Forward Selection, vol. 9, pp. 239243, 2017.
P. Assist, T. H. E. Quota, L. Ministry, O. F. Education, and T. H. E. P. Period, Jurnal EPISTEMA, vol. 2, no. 1, 2021.
D. Rahmalia and T. Herlambang, Prediksi Cuaca Menggunakan Algoritma Particle Swarm Optimization-Neural Network ( Psonn ), pp. 4148, 2017.
T. N. Mandiri, J. T. Informatika, and V. X. No, Prediksi Hasil Pemilu Legislatif Dki Jakarta Dengan Metode Neural Network Berbasis Particle Swarm Optimization, vol. X, no. 1, pp. 3747, 2013.
S. Informasi, Prosiding seminar nasional sisfotek Penerapan Data Mining dengan Algoritma Neural Network ( Backpropagation ) Untuk Prediksi Lama Studi Mahasiswa, vol. 3584.
A. B. Mutiara, U. Gunadarma, R. Refianti, and U. Gunadarma, Buku Saku : Pengantar Deep Neural Network Untuk Sistem Cerdas, no. September. 2018.
D. N. Agus Perdana Windarto, M. S. H. Anjar Wanto, Frinto Tambunan, M. R. L. Muhammad Noor Hasan Siregar, and D. N. Solikhun, Yusra Fadhillah, Jaringan Saraf Tiruan: Algoritma Prediksi dan Implementasi, vol. 53, no. 9. 2019.
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Annahl Riadi, Irvan Muzakkir, Marniyati H. Botutihe
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.