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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Multimedia JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Daru Prasetyawan
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.
Pengembangan Sistem Seleksi Proposal Penelitian Berbasis Web Service Menggunakan REST API Daru Prasetyawan; Prihanto Dwi Rahmanto
Jurnal Teknologi Informasi dan Multimedia Vol. 6 No. 3 (2024): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i3.585

Abstract

Web service-based application development has become a vital component of the modern in-formation technology ecosystem. The implementation of web services facilitates interoperability among diverse systems and platforms. REST API (REpresentational State Transfer Application Programming Interface) is one of the most popular architectures for creating efficient and scalable web services. This study examines the implementation of REST API in the development of web service-based applications. The case study focuses on the creation of a Web service-based Research Proposal Selection System. The REST API implementation method in this study adheres to the system development life cycle (SDLC) to ensure that the developed API meets business needs while being efficient, secure, and reliable. The analysis phase is conducted to gather both functional and non-functional REST API requirements. During the design phase, the REST API architecture is designed in detail, including how data will be transferred and processed, and how the API will interact with databases and other systems. Furthermore, in the implementation phase, the REST API is developed, ensuring that each endpoint operates correctly. In developing the Proposal Selection System, the implementation of web services integrates data from various existing sys-tems, including employee, academic, and research data. The system encompasses various features, such as proposal import, proposal mapping, proposal review, proposal assessment, proposal se-lection, and reporting. The research findings indicate that implementing the REST API simplifies the development of web service applications. The test results reveal that the average response time is relatively stable, measuring 31 ms without iterations and 29 ms with five iterations.
Co-Authors Prihanto Dwi Rahmanto Rahmadhan Gatra