IMPLEMENTASI MACHINE LEARNING UNTUK MEMBUAT IMAGE SEARCH ENGINE BERDASARKAN ANALISIS SIMILARITAS [IMPLEMENTATION OF MACHINE LEARNING TO CREATE AN IMAGE SEARCH ENGINE BASED ON SIMILARITY ANALYSIS]
DOI:
https://doi.org/10.19166/jstfast.v7i2.7592Keywords:
convolutional neural network, firebase, image search engine, image similarity, mobile applicationAbstract
The development of technology makes it easier to search for something on the internet. Based on data in 2022, there will be more than 1 billion Google users worldwide. This data proves the importance of search engines for internet users. However, in general, search engine searches use keywords. This research aimed to produce image search engines for searching. The image search engine utilizes a Convolutional Neural Network, namely VGG-16, which would compare the similarity of the input image to all images in the database by comparing the neurons in the image. Image search engine performance was measured based on the accuracy and sequence of image output. Accuracy was obtained with a weighting system from 5 search result images produced by the image search engine. Mobile phone applications were used to improve the quality of images used for searching. The application would capture images or take images through the gallery then upload them to Firebase and be used to search by image search engines. From the research conducted, it was found that changing the angle had the greatest impact on decreasing accuracy values when compared to 3 other factors: color, background and image quality. The decrease in accuracy due to the influence of angle, color, backround,, and image quality was 96.25%, 0%, 22.5%, and 68.75%, respectively. The images from cellphone application were proven to have higher accuracy (95.32% ) than the images captured with webcam (86.67%). The increase in accuracy emphasizes that the influence of image quality and the angle of the image used to search influences search results. The specifications of the cellphone camera are 64 Megapixels, while the webcam camera is 2 Megapixels.
Bahasa Indonesia Abstract:
Berkembangnya teknologi memberikan kemudahan untuk mencari sesuatu dalam internet. Berdasarkan data pada tahun 2022 lebih dari 1 milliar pengguna google di seluruh dunia. Data tersebut membuktikan bahwa pentingnya search engine bagi pengguna internet. Namun pada umumnya, pencarian search engine memanfaatkan kata kunci. Penelitian ini bertujuan untuk menghasilkan image search engine untuk pencarian. Image search engine memanfaatkan Convolutional Neural Network yaitu VGG-16 yang membandingkan kemiripan gambar input dengan semua gambar pada database dengan membandingkan neuron-neuron pada gambar. Performa image search engine diukur berdasarkan akurasi dan urutan keluaran gambar. Akurasi didapatkan dengan sistem pembobotan dari 5 gambar hasil pencarian yang dihasilkan oleh image search engine. Aplikasi handphone dimanfaatkan untuk meningkatkan kualitas gambar yang digunakan untuk pencarian. Aplikasi akan menangkap gambar atau mengambil gambar melalui gallery kemudian diunggah ke firebase dan digunakan untuk mencari oleh image search engine. Dari penelitian yang dilakukan didapatkan bahwa perubahan angle memberikan dampak yang paling besar terhadap penurunan nilai akurasi jika dibandingkan dengan 3 faktor lainnya: warna, background, dan kualitas gambar. Penurunan akurasi yang didapatkan akibat pengaruh angle, warna, bakcground, dan kualitas gambar masing-masing sebesar 96,25%; 0%, 22,5%; dan 68,75%. Gambar dari aplikasi handphone terbukti memiliki akurasi lebih tinggi (95.32%) dari gambar hasil tangkapan webcam (86,67%). Peningkatan akurasi ini mempertegas bahwa pengaruh kualitas gambar dan angle pada gambar yang digunakan untuk mencari mempengaruhi hasil pencarian. Spesifikasi dari kamera handphone adalah 64 Megapixels, sedangkan kamera webcam 2 Megapixels.
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