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All Journal SemanTIK : Teknik Informasi Journal of Engineering and Science Application
Adha Mashur Sajiah
Halu Oleo University
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering

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IMPLEMENTASI DEEP LEARNING DENGAN METODE CONVOLUTIONAL NEURAL NETWORK UNTUK IDENTIFIKASI OBJEK SECARA REAL TIME BERBASIS ANDROID Indra Fransiskus Alam; Muhammad Ihsan Sarita; Adha Mashur Sajiah
semanTIK Vol 5, No 2 (2019): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (392.817 KB) | DOI: 10.55679/semantik.v5i2.7554

Abstract

The development of artificial intelligence has now undergone significant changes. This underlies the birth of a method to deal with object detection in real time with high accuracy. Basically Deep Learning is the implementation of the basic concept of Machine Learning which applies the smart algorithm with more layers between the input layer and the output layer. Convolutional Neural Network (CNN) is one method of Deep learning (DL) that can be used to detect and recognize an object in a digital image. The ability of CNN is claimed to be the best model to solve the problem of object detection and object recognition because it is a development of the backpropagation method and does not require large computation in the process. The results obtained in this study that android applications can run well with an accuracy of 92.33% can be seen from the test results using the 10-fold cross validation method, all available menus can be run and the mention of object labels is appropriate for image recognition and classification. Calculation of precision and recall has good values, each at 97.51% and 94.33%. In the classification process, objects that do not exist in datasets that have been modeled by the system will be null or unrecognized, especially in the image of an object captured by an Android camera that has many objects and is close together
Implementation of a Food Image Retrieval System Based on Color Feature Extraction Using Color Moments (CIELab) and Texture Feature Using Local Binary Pattern (LBP) Nurdian Kasim; Adha Mashur Sajiah; L.M. Fid Aksara
Journal of Engineering and Science Application Vol. 3 No. 2 (2026): Mei-Oktober
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v3i2.65

Abstract

The rapid growth of digital image content on social media has positioned food as one of the most frequently shared visual objects. Text-based search is often inadequate for retrieving food images due to their high visual complexity in color, shape, and texture, as variations in ingredients, lighting, and camera angle can produce highly diverse images even within the same food category. This study implements a Content-Based Image Retrieval (CBIR) system for food images by combining Grid Color Moments in the CIELab color space for color feature extraction and Multi-Scale Local Binary Pattern (LBP) for texture feature extraction. The CIELab color space was chosen for its stability under varying lighting conditions, while the multi-scale LBP approach captures texture patterns at several levels of observation. The system processes 3,600 food images across nine classes (Burger, Donut, Ice Cream, French Fries, Klepon, Fried Noodles, Fried Rice, Pizza, Rendang), using an 80:20 stratified split for database and query images. The retrieval process involves image preprocessing, feature extraction, feature vector normalization, similarity computation, and ranking of the most visually similar images. Similarity is computed using Euclidean Distance and Chi-Square Distance, evaluated with Precision@10 and Recall@10 on a Top-10 retrieval scheme. Results show the proposed feature combination improves performance over a baseline method that does not use grid-based and multi-scale feature extraction, with the best result achieved using Chi-Square Distance: Mean Precision@10 of 0.7133 and Mean Recall@10 of 0.0223. These findings confirm that combining color and texture features effectively represents food image characteristics and improves retrieval accuracy, offering a practical alternative to text-based food image search systems.
Co-Authors Indra Fransiskus Alam LM. Fid Aksara Muhammad Ihsan Sarita Nurdian Kasim