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All Journal CommIT (Communication & Information Technology) Jurnal Informatika: Jurnal Pengembangan IT
Saputro, Satria Nur
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Computer Science & IT

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Classification Taxonomies Genus of 90 Animals Using Transfer Learning Resnet-152 Saputro, Satria Nur; Adhinata, Faisal Dharma; Athiyah, Ummi
CommIT (Communication and Information Technology) Journal Vol. 18 No. 1 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i1.9482

Abstract

The process of learning theory and the limited ability to remember anything, especially a foreign language, often cause students to have difficulty understanding lessons, especially in determining the type and taxonomy of the animal. With the assistance of computer vision technology, students can more effectively face various challenges, enhance their understanding, and improve their ability to apply the concept of animal classification. The research classifies the taxonomy of 90 animals using Transfer Learning ResNet 152. It aims to analyze the performance of Transfer Learning ResNet 152 on the 90-animal dataset. The results show that in Model A with an architecture with frozen layers in 6 ResNet blocks, the highest evaluation value obtained is 0.9222 on Batch size 4 with Dropout 6, 0.9241 on Batch size 8 with Dropout 7, 0.9259 on Batch size 16 with Dropout 8, and 0.9296 on Batch size 32 with Dropout 4 and Dropout 7. Meanwhile, in model B with an architecture with frozen layers in 5 ResNet blocks and one non-frozen block, the highest evaluation value obtained is 0.7611 on Batch size 4 with Dropout 8, 0.8713 on Batch size 8 with Dropout 2, 0.8852 on Batch size 16 with Dropout 1, and 0.9204 on Batch size 32 with Dropout 3.
Klasifikasi Judul Berita Clickbait menggunakan RNN-LSTM Afandi, Widi; Saputro, Satria Nur; Kusumaningrum, Andini Mulia; Adriansyah, Hikari; Kafabi, Muhammad Hilmi; Sudianto, Sudianto
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 2 (2022)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i2.3401

Abstract

Amid technological developments, online news of various life topics is shared across various platforms. Many media often take advantage of this opportunity by uploading their news on several online platforms to increase the traffic and rankings they upload to make much profit. However, many online media attract readers' attention by exaggerating the headlines or news headlines they upload. That way, the news title is often not by the content of the news. This phenomenon is commonly known as "clickbait" among the public. The media usually do this to increase traffic, rankings, and finances. Therefore, this study classified the news with clickbait and non-clickbait titles using the RNN-LSTM architecture. In this study, the classification of clickbait news titles uses the RNN-LSTM architecture. The classification results obtained calculation accuracy of 79% on training data and 77% accuracy on test data.
Co-Authors Adriansyah, Hikari Afandi, Widi Faisal Dharma Adhinata Kafabi, Muhammad Hilmi Kusumaningrum, Andini Mulia Sudianto, Sudianto Ummi Athiyah