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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Rahmadhan Gatra
UIN SUNAN KALIJAGA YOGYAKARTA
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Algoritma K-Nearest Neighbor untuk Memprediksi Prestasi Mahasiswa Berdasarkan Latar Belakang Pendidikan dan Ekonomi Daru Prasetyawan; Rahmadhan Gatra
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 7 No. 1 (2022): Januari 2022
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (327.538 KB) | DOI: 10.14421/jiska.2022.7.1.56-67

Abstract

Student academic performance is one measure of success in higher education. Prediction of student academic performance is important because it can help in decision-making. K-Nearest Neighbor (K-NN) algorithm is a method that can be used to predict it. Normalization is needed to scale the attribute value, so the data are in a smaller range than the actual data. Feature selection is used to eliminate irrelevant features. Data cleaning from outliers in the dataset aims to delete data that can affect the classification process. In the classification process, the dataset is divided into a training set by 80% and a validation set by 20% using the cross-validation method. The classification model that is formed is tested using data that is separate from the training data and is evaluated using a confusion matrix. As an evaluation, the K-NN model has 95.85% average accuracy, 95.97% average precision, and 95.84% average recall.
Model Convolutional Neural Network untuk Mengukur Kepuasan Pelanggan Berdasarkan Ekspresi Wajah Daru Prasetyawan; Rahmadhan Gatra
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5493

Abstract

Customer satisfaction shows how well the product or service of an organization meets customer expectations. Customers' facial expressions can show their satisfaction with the services provided. Convolution Neural Network (CNN) is a type of neural network algorithm that can be used to recognize an object in an image. CNN utilizes the convolution process to determine and distinguish an object in the image from other objects such as to recognize various facial expressions. This study aims to measure customer satisfaction by utilizing the CNN model by recognizing any changes in facial expressions. From the results of the CNN model training, an accuracy of 90.57% was obtained. Furthermore, the formed model is implemented into a web-based system that records facial expressions and performs a classification (satisfied or dissatisfied) on any detected facial changes. The most dominant expression is the result of measuring customer satisfaction.
Co-Authors Daru Prasetyawan