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All Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Joseph Sanjaya
Maranatha Chrisitian University
Computer Science & IT

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Integrasi Micro-Apps Individual menjadi One-Stop Services Maranatha Application Suite Joseph Sanjaya; Erick Renata; Vincent Elbert Budiman; Francis Anderson; Mewati Ayub
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 3 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i3.1993

Abstract

Use of information systems at the university, which is the standard management of the university, is required to support all the activities of the academic community. Aside of determining the smooth operations, information technology also maintain the competitiveness of the competitors by constantly updating the information technology so as not to miss. Trends in the development of technology will make a strong connectivity between universities and stakeholders, governments, and partners. Some of the problems that occur when information systems are not yet fully integrated system, separate user management, application is implemented on different platforms and dashboards for management, not integrated. It is important for the university to provide Single and Integrated Application applications that are integrated into each of their services. Single and Integrated Applications are available at the web application / desktop / mobile multi-platform, for which data are available in real time, and there is no duplication of data occurs.
Prediksi Kelalaian Pinjaman Bank Menggunakan Random Forest dan Adaptive Boosting Joseph Sanjaya; Erick Renata; Vincent Elbert Budiman; Francis Anderson; Mewati Ayub
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 1 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i1.2313

Abstract

Abstract — A loan is one of the most important products on the bank, which used for main revenue. All bank tries to find the most effective business strategy to persuade a customer to use the loan, but loan default has a negative effect after the application is approved. Loan default causes loss on the bank, therefore it is mandatory to calculate in order to decrease the risk of the loan default. This study uses random forest and adaptive boosting machine learning methods to get the prediction and decision. The random forest uses a voting method from many decision trees and adaptive boosting can support to increase accuracy, stability and handle an underfit or overfit problem. The experimental results show that Adaptive Boosted Random Forest outperformed normal random forest and Deep learning Neural Network (DNN) in recall rate evaluation metrics with small trade-offs in the accuracy. Keywords— Adaptive Boosting; Bank; Loan Default; Machine learning; Random Forest;
Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup Joseph Sanjaya; Mewati Ayub
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2688

Abstract

Deep convolutional neural networks (CNNs) have achieved remarkable results in two-dimensional (2D) image detection tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. In this paper, a Deep Learning system for accurate car model detection is proposed using the ResNet-152 network with a fully convolutional architecture. It is demonstrated that significant generalization gains in the learning process are attained by randomly generating augmented training data using several geometric transformations and pixel-wise changes, such as image cropping and image rotation. We evaluated data augmentation techniques by comparison with competitive data augmentation techniques such as mixup. Data augmented ResNet models achieve better results for accuracy metrics than baseline ResNet models with accuracy 82.6714% on Stanford Cars Dataset.
Sistem Pengenalan Spesifikasi Mobil pada Showroom Berbasis Haar-Like Features Andrew Sebastian Lehman; Joseph Sanjaya
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i3.2903

Abstract

Changes in science and technology have affected the structure of societies and have led to rapid change in human profile. In order to adapt to the changing human profile, reforms in advertising as well as scientific and technological enrichment in advertisement environments have become necessary. This study aims to investigate the impact of advertisement materials developed with augmented reality (AR) technology on car specification presentation and attitudes towards the advertisement, and to determine their attitudes towards AR applications. In this study, AR application was developed using haar-like features method for marker detector. A quasi-experimental design was used in which intact showroom at two different location, consisting of a total of one hundred customers, were randomly assigned to either the experimental or control group. The experimental group researched their selected car using AR technology, while the control group researched their selected car using traditional methods and the help of salesman. Customers in the experimental group were found to have higher understanding and slightly faster to learn about the car than those in the control group. In addition, the results revealed that the customers were pleased and wanted to continue using AR applications in the future. They also showed no signs of anxiety when using AR applications. In addition, it was found that advertisement achievements and attitudes of the customers in the experimental group showed a positive, significant and intermediate correlation.
Co-Authors Erick Renata Erick Renata Francis Anderson Francis Anderson Lehman, Andrew Sebastian Mewati Ayub Mewati Ayub Vincent Elbert Budiman Vincent Elbert Budiman