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All Journal JAIS (Journal of Applied Intelligent System) MASALIQ: Jurnal Pendidikan dan Sains
Sugiarto, Triga Agus
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Religion Education Social Sciences

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Enhancing Augmentation-Based Resnet50 for Car Brand Classification Sugiarto, Triga Agus; Soeleman, Moch Arief; Pujiono, Pujiono
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9385

Abstract

This research focuses on car classification and the use of the ResNet-50 neural network architecture to improve the accuracy and reliability of car detection systems. Indonesia, as one of the countries with high daily mobility, has a majority of the population using cars as the main mode of transportation. Along with the increasing use of cars in Indonesia, many automotive industries have built factories in this country, so the cars used are either local or imported. The importance of car classification in traffic management is a major concern, and vehicle make and model recognition plays an important role in traffic monitoring. This study uses the Vehicle images dataset which contains high-resolution images of cars taken from the highway with varying viewing angles and frame rates. This data is used to analyze the best- selling car brands and build car classifications based on output or categories that consumers are interested in. Digital image processing methods, machine learning, and artificial neural networks are used in the development of automatic and real-time car detection systems.The ResNet-50 architecture was chosen because of its ability to overcome performance degradation problems and study complex and abstract features from car images. Residual blocks in the ResNet architecture allow a direct flow of information from the input layer to the output layer, overcoming the performance degradation problem common in neural networks. In this paper, we explain the basic concepts of ResNet-50 in car detection and popular techniques such as optimization, augmentation, and learning rate to improve performance and accuracy. in this study, it is proved that ResNet has a fairly high accuracy of 95%, 92% precision, 93% recall, and 92% F1-Score.
Sistem Pendukung Keputusan Penentu Merek Handphone Terbaik Daerah Jawa Tengah Menggunakan Logika Fuzzy Tahani Sugiarto, Triga Agus
MASALIQ Vol 5 No 2 (2025): MARET
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/masaliq.v5i2.5031

Abstract

Smartphone usage has become increasingly widespread and integral to daily life, with more than 80% of adults in Indonesia using smartphones. The diversity of features and applications available on smartphones, such as high-quality cameras, internet browsing, and social media, makes them a popular choice compared to other electronic devices. However, the wide range of designs, specifications, and prices can make it difficult for potential users to choose a smartphone that meets their needs. To assist in this selection process, a Decision Support System (DSS) based on fuzzy logic Tahani has been developed. This system is designed to provide smartphone recommendations based on several key criteria such as brand, Antutu score, purchase location, camera quality, RAM capacity, and price. With this method, the DSS can handle uncertainty and consider various criteria to deliver recommendations that best match the preferences and needs of individual users.
Co-Authors Moch Arief Soeleman, Moch Arief Pujiono Pujiono