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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Sistem Informasi
Ari Wibisono
Faculty of Computer Science, Universitas Indonesia
Computer Science & IT Library & Information Science

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ANALYSIS OF QUALITY ASSURANCE ON SISTEM INFORMASI ZAKAT (SIZAKAT) THROUGH SOFTWARE TESTING Abdul Haris; Wisnu Jatmiko; Ari Wibisono
Jurnal Sistem Informasi Vol. 9 No. 2 (2013): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (172.857 KB) | DOI: 10.21609/jsi.v9i2.355

Abstract

Sistem Informasi Zakat (SIZakat) is a web-based information system that is used to assist in the management of zakat in Imam Bonjol Mosque Pondok Labu, South Jakarta. In this thesis, we conducted testing to the SIZakat application to know the quality and the feasibility. We conducted seven kinds of testing: Unit Testing, Integration Testing, Stress Testing, Load Testing, Testing SQL Injection, XSS Injection Testing and User Acceptance Testing. In addition to ensure the quality of SIZakat, the SIZakat test result is expected to be a reference for future quality improvement. Test results show that SIZakat have accurate functionalities, good security, and good performance.
Embedded Deep Learning System for Classification of Car Make and Model Ari Wibisono; Hanif Arief Wisesa; Satria Bagus Wicaksono; Puteri Khatya Fahira
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i1.1118

Abstract

Automatic car make, and model classification is essential to support activities of intelligent traffic systems in urban areas, such as surveillance, traffic information collection, statistics, etc. In order to classify this data, we need an embedded system approach for real-time car recognition. Many approaches could be made, from image processing to machine learning. Recently, the development of the Convolutional Neural Network has spurred various research in the Area. ResNet, Inception, DenseNet, and NasNet are some of the most commonly used Neural Network based method that is used to classify images. In this research, these Neural Network methods are going to be compared in classifying vehicle make and model in the Stanford dataset. The dataset contains 196 different labels. Several evaluation metrics are used to compare the performance of the methods. From the experiment, the InceptionV3 method achieved the best performance of the AUROC ratio for training the dataset under 50 epochs. Other methods that achieve a high AUROC value tends to have a higher computational time. Real-time simulations have shown that the embedded system is capable of classifying a 100 % success rate for six concurrent users.
Co-Authors Abdul Haris Hanif Arief Wisesa Puteri Khatya Fahira Satria Bagus Wicaksono Wisnu Jatmiko