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All Journal Jurnal Simantec Jurnal Ilmiah Kursor Jurnal Pekommas REKAYASA Network Engineering Research Operation [NERO] JSiI (Jurnal Sistem Informasi) Multitek Indonesia : Jurnal Ilmiah Water Circulation Monitoring System in The Vanname Shrimp Cultivation based on Internet of Thing Pancaran Pendidikan Jurnal Pengabdian dan Pemberdayaan Banyuwangi
Rochman, Eka Mala Sari
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Trunojoyo
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Physics Social Sciences Other

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Journal : Pancaran Pendidikan

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Predicting the Final result of Student National Test with Extreme Learning Machine Eka Mala Sari Rochman; Aeri Rachmad; Fitri Damayanti
Pancaran Pendidikan Vol 7, No 2 (2018)
Publisher : The Faculty of Teacher Training and Education The University of Jember Jember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.663 KB) | DOI: 10.25037/pancaran.v7i1.159

Abstract

The level of student achievement is a benchmark of the quality assessment of a school. This student's assessment is based on the national final exam scores every year. When the national exam score increases, it will affect the number of students who will enroll in a school. It affects the number of classes to be opened in the registration of new student candidates. This study aims to predict student achievement based on the value of subjects that become the focus on the final national examination. One method of forecasting in the Artificial Neural Network (ANN) is the Extreme Learning Machine (ELM). The working principle in this method is basically the same as ANN method in general. Namely, there are input layer, hidden layer and output layer. By randomly assigning the input parameters, the ELM generates good generalization performance. By using 20-20-1 network architecture, this research has a result in a small RMSE value of 0.314.
Co-Authors Aeri Rachmad Aeri Rachmad Bain Khusnul Khotimah Dyah S Rahayu Fitri Damayanti Husni - Ifan Pratama Imamah Imamah Khozaimi, Ach. Pramudita, Yoga Dwitya Ratna Nur Tiara Shanty Rima Tri Wahyuningrum Wahyudi Setiawan