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All Journal YUME : Journal of Management Global Abdimas: Jurnal Pengabdian Masyarakat Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Inspiration: Jurnal Teknologi Informasi dan Komunikasi Jurnal MediaTIK
Rezty Amalia Aras
Institut Teknologi dan Bisnis Kalla
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Transportation Other

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Journal : Inspiration: Jurnal Teknologi Informasi dan Komunikasi

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Implementation of Haar Cascade and Adaboost Algorithms in Photo Classification on Social Networks Rezty Amalia Aras; Hutami Endang
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2023): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v13i1.45

Abstract

Instagram is one of the fastest social networks in recent years. Instagram is a popular social media for sharing images. For example, image searches on Instagram may use certain keywords, sometimes called hashtags. There are no rules for specifying hashtags when users upload images. As such, the specified hashtag may not be relevant to the uploaded image. There are photos whose content is dominated by selfies. The study was conducted using data from Instagram, using hashtags to refine searches. Next, classify from the search results. The survey has three categories: selfies, food, and travel. Results: Two of her classification algorithms, Haar Cascade and Adaboost, were used in this study. From the study results, we can conclude that the Haar cascade has a precision rate of 0.7081/s and a detection error of 0.8816/s, while Adaboost has a precision rate of 0.7072/s and a detection error of 0.8424/s. According to the recognition results, the two algorithms can recognize and classify photos with almost the same accuracy (only 0.0392 seconds).
Co-Authors Emmy Puspita Sari Hutami Endang Ilna Nardiyah Kiki Resky Ramdhani Sucipto Muhammad Fachrul Salam Muhammad Sukri Ahmad Yogi Hady Afrizal