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<doi_batch_id>_1732435997</doi_batch_id>
<timestamp>20241124081317000</timestamp>
<depositor>
<depositor_name>aaud</depositor_name>
<email_address>ardian.infantono@aau.ac.id</email_address>
</depositor>
<registrant>Akademi Angkatan Udara</registrant>
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<journal>
<journal_metadata>
<full_title>Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO)</full_title>
<abbrev_title>senastindo</abbrev_title>
<issn media_type="electronic">2808-2540</issn>
<issn media_type="print">2685-8991</issn>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>10</month>
<day>31</day>
<year>2022</year>
</publication_date>
<journal_volume>
<volume>4</volume>
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<doi_data>
<doi>10.54706/senastindo.v4.2022</doi>
<resource>https://aau.e-journal.id/senastindo/issue/view/18</resource>
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<journal_article xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" publication_type="full_text" metadata_distribution_opts="any" language="id">
<titles>
<title>KLASIFIKASI JENIS TAS PADA GAMBAR 360 DERAJAT (FISH EYE) DENGAN MENGGUNAKAN TENSORFLOW</title>
</titles>
<titles>
<title>CLASSIFICATION OF BAG TYPES IN 360 DEGREE IMAGES (FISH EYE) USING TENSORFLOW</title>
</titles>
<contributors>
<person_name contributor_role="author" sequence="first" language="id">
<given_name>Agnes Novi Anna</given_name>
<surname>Pangemanan</surname>
</person_name>
<person_name contributor_role="author" sequence="additional" language="id">
<given_name>Arief Suryadi</given_name>
<surname>Satyawan</surname>
</person_name>
<person_name contributor_role="author" sequence="additional" language="id">
<given_name>Sri Desy</given_name>
<surname>Siswanti</surname>
</person_name>
</contributors>
<jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xml:lang="id">
<jats:p>Klasifikasi objek merupakan salah satu kajian yang sedang berkembang saat ini. Dampaknya pada dunia fashion dimana wanita dan pria, remaja hingga orang tua saat ini tidak lepas dari tas sebagai pelengkap fashion sehari-hari. Dalam pemilihan tas sering terjadi kesalahan agar tidak salah dalam memilih, pada tugas akhir ini saya mengklasifikasikan jenis-jenis tas, yaitu cara yang digunakan untuk membedakan karakteristik tas berdasarkan jenisnya. Sistem klasifikasi tas berdasarkan jenisnya merupakan program yang dapat mengidentifikasi tas seseorang sesuai dengan jenisnya yang telah dilatih dan disimpan dalam database program yang sedang dijalankan. Klasifikasi jenis tas dapat dilakukan dengan berbagai cara salah satunya Deep Learning dengan metode Convolutional Neural Network (CNN), implementasi CNN menggunakan Tensorflow dengan bahasa pemrograman python. Penelitian ini dilakukan dengan menggunakan 5 klasifikasi dataset tipe tas berjumlah 6.720 citra yang telah dilatih dengan ukuran citra 180 x 180 menggunakan kamera 360o. Diharapkan sistem ini mampu bekerja dengan baik untuk mengklasifikasikan jenis tas dalam format citra 360o (fish eye). Penelitian ini menghasilkan true detection rate sebesar 55% dan false detection sebesar 45% dimana true detection dilihat dari jumlah kebenaran akurasi yang menentukan hasil keluaran, sedangkan false detection merupakan kebalikan dari true detection dari jumlah 135 citra yang dihasilkan telah diuji.</jats:p>
</jats:abstract>
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<jats:p>Classification of objects is one of the studies that are currently being developed. The impact on the world of fashion is where women and men, teenagers to parents today cannot be separated from the bag as an addition to daily fashion. In the selection of bags, mistakes are often made, so as not to make a wrong choice, in this final project, I classify the types of bags, which is the method used to distinguish the characteristics of bags by type. The bag classification system based on type is a program that can identify a person's bag according to the type that has been trained and stored in the database of the program being run. Classification of bag types can be done in various ways, one of which is Deep Learning with the Convolutional Neural Network (CNN) method, CNN implementation using Tensorflow with the python programming language. This study was conducted using 5 classifications of bag type datasets totaling 6,720 images that have been trained with an image size of 180 x 180 using a 360o camera. It is hoped that this system is able to work well for classifying bag types in 360o (fish eye) image format. This study resulted in true detection rates of 55% and false detection of 45% where true detection is seen from the number of truths of accuracy in determining the output results, while false detection is the opposite of true detection from the number of 135 images that have been tested.</jats:p>
</jats:abstract>
<publication_date media_type="online">
<month>03</month>
<day>13</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>353</first_page>
<last_page>364</last_page>
</pages>
<doi_data>
<doi>10.54706/senastindo.v4.2022.212</doi>
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