Metode Jaringan Syaraf Tiruan Untuk Prediksi Performa Mahasiswa Pada Pembelajaran Berbasis Problem Based Learning (PBL)

*Badieah Badieah  -  Universitas Islam Sultan Agung, Indonesia
Rachmat Gernowo  -  Universitas Diponegoro, Indonesia
Bayu Surarso  -  Universitas Diponegoro, Indonesia
Received: 27 Apr 2016; Published: 30 Nov 2016.
Open Access
Citation Format:
Article Info
Section: Research Articles
Language: EN
In Vol 6, No 1 (2016): Volume 6 Nomor 1 Tahun 2016
Statistics: 779 6897
Abstract

In order to improve academic quality in higher education, students’ performance evaluation is becoming important. To prevent increasing failure rate in the course, we need a system that is capable of predicting student’s performance in the end of the course. The research used several factors that are considered to affect students' performance on Problem Based Learning (PBL), such as students’ demography, students’ prior knowledge and group heterogeneity.  The method used in the study was Artificial Neural Network (ANN) with backpropagation training algorithm. Total 8 neurons were used as inputs for ANN which were obtained from gender variable (2 neurons), age variable (1 neuron), students’ average knowledge variable (1 neuron), students’ average skill variable (1 neuron) and group heterogeneity variable (3 neurons). Several different ANN architecture were tested in the study using 2, 7 and 12 hidden neurons respectively. Each architecture was trained using various different training parameters in order to find the best ANN architecture. Dataset used  in the research were obtained from Academic Information System in Faculty of Dentistry Unissula which contained Adult and Elderly Diseases Course’s participants from year 2009 to 2013. The ANN output were numeric values which represented students’ performance in Adult and Elderly Diseases Course. The output of this study is a system that is able to predict the student performance in block course. The result shows that using 7 hidden neurons in the network combining with 0.5 ,0.1 and  9000 for learning rate, momentum and epoch respectively, were the best ANN architechture and parameters in the study. The MSE obtained from validation test was 0,011926 with correlation coefficient (R) 0,796879. The prediction system are expected to help faculty and academic evaluation team to conduct actions to improve student’s academic performance and prevent them from failure in the course. 

Keywords: Artificial Neural Network; Problem Based Learning; Prediction

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