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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Information Systems Engineering and Business Intelligence Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Vriza Wahyu Saputra
Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Jenis Makanan menggunakan Neighbor Weighted K-Nearest Neighbor dengan Seleksi Fitur Information Gain Vriza Wahyu Saputra; Yuita Arum Sari; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (540.454 KB)

Abstract

Smartphones with powerful camera sensor capabilities can be used to analyze photos and object recognition. Food is one of the popular photography objects and seeing it makes you want to cook or taste it. Cooking requires recipes as a tool to make dishes because not everyone knows how to make dishes. Food recipe search techniques with food image input are needed because not everyone knows the name of the food made. There are several steps in the method carried out to do the introduction of food types namely preprocessing, feature extraction using the Color Moments and Gray Level Counseling Matrix (GLCM) method, feature selection using the Information Gain method and classification using the Weighted K-Nearest Neighbor (NWKNN) method. Tests were carried out to determine the accuracy of the NWKNN method and also to know the effect of the Information Gain feature selection. The results of testing with the K-Fold Cross Validation method obtained the highest average accuracy of 92.37% by dividing the test data by 30, the number of features by 10, the value of k on the NWKNN by 3 and calculating distances using Cosine Similarity. On other hands, the testing of the Information Gain effect resulted in the highest accuracy of 86.96% with the 15 best features. It can be concluded that the NWKNN method can answer the problem of unbalanced data and Information Gain can find out the best features for classification.
A Systematic Literature Review of Student Assessment Framework in Software Engineering Courses Reza Fauzan; Daniel Siahaan; Mirotus Solekhah; Vriza Wahyu Saputra; Aditya Eka Bagaskara; Muhammad Ihsan Karimi
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 2 (2023): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.2.264-275

Abstract

Background: Software engineering are courses comprising various project types, including simple assignments completed in supervised settings and more complex tasks undertaken independently by students, without the oversight of a constant teacher or lab assistant. The imperative need arises for a comprehensive assessment framework to validate the fulfillment of learning objectives and facilitate the measurement of student outcomes, particularly in computer science and software engineering. This leads to the delineation of an appropriate assessment structure and pattern. Objective: This study aimed to acquire the expertise required for assessing student performance in computer science and software engineering courses. Methods: A comprehensive literature review spanning from 2012 to October 2021 was conducted, resulting in the identification of 20 papers addressing the assessment framework in software engineering and computer science courses. Specific inclusion and exclusion criteria were meticulously applied in two rounds of assessment to identify the most pertinent studies for this investigation. Results: The results showed multiple methods for assessing software engineering and computer science courses, including the Assessment Matrix, Automatic Assessment, CDIO, Cooperative Thinking, formative and summative assessment, Game, Generative Learning Robot, NIMSAD, SECAT, Self-assessment and Peer-assessment, SonarQube Tools, WRENCH, and SEP-CyLE. Conclusion: The evaluation framework for software engineering and computer science courses required further refinement, ultimately leading to the selection of the most suitable technique, known as learning framework. Keywords: Computer science course, Software engineering course, Student assessment, Systematic literature review
Fog and rain augmentation for license plate recognition in tropical country environment Wahyu Saputra, Vriza; Suciati, Nanik; Fatichah, Chastine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3951-3961

Abstract

Automatic license plate recognition (ALPR) is a critical component in modern traffic management systems. However, ALPR systems often face challenges in accurately recognizing license plates under adverse weather conditions, such as fog and rain, prevalent in tropical regions. Deep learning ALPR models necessitate huge and diverse datasets for robustness, but data availability remains a concern since unpredictable fog and rain patterns hinder data collection. In this study, we address the issue of enhancing ALPR's robustness by introducing a novel augmentation strategy that combines traditional and weather augmentation techniques. By augmenting the dataset with weather-induced variations, we aim to improve the generalization capability of ALPR models, enabling them to handle a wider range of weather-related challenges. We also investigate the synergy between these weather augmentations and established scene text recognition (STR) methods, such as convolutional recurrent neural network (CRNN), TPS-ResNet BiLSTM-attention (TRBA), autonomous bidirectional iterative scene text recognition (ABINet), vision transformer (ViTSTR), and permutated autoregressive sequence (PARSeq), to determine their impact on recognition accuracy. Experiments using different training data sets show that training data containing a combination of traditional and weather augmentation produces the best accuracy and 1-NED performance compared to training data without augmentation and traditional augmentation only. The average increase accuracy of all STR model is 1.13% with the best increase accuracy of 3.68% using TRBA.
Co-Authors Aditya Eka Bagaskara Agus Wahyu Widodo Chastine Fatichah Daniel Oranova Siahaan Mirotus Solekhah Muhammad Ihsan Karimi Nanik Suciati Reza Fauzan Yuita Arum Sari