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All Journal International Journal of Engineering, Science and Information Technology Prosiding SeNTIK STI&K
Adang Suhendra
Universitas Gunadarma
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Metode Deep Learning Pada Sistem Rekomendasi : Review Paper Diana Ikasari; Adang Suhendra; Nuke Farida
Prosiding Seminar SeNTIK Vol. 2 No. 1 (2018): Prosiding SeNTIK 2018
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Metode Deep Learning Pada Sistem Rekomendasi : Review Paper
Metode Temu Kembali Data Dengan Uji Similaritas Levenshtein Pada Sumber Data Terdistribusi Romdhoni Susiloatmadja; I Made Wiryana; Adang Suhendra; Lintang Yuniar Banowosari; Lily Wulandari
Prosiding Seminar SeNTIK Vol. 3 No. 1 (2019): Prosiding SeNTIK 2019
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Metode Temu Kembali Data Dengan Uji Similaritas Levenshtein Pada Sumber Data Terdistribusi
A Comprehensive Literature Review on Skin Disease Identification Through Deep Learning-Based Bi-Directional Image-Text Matching Techniques Alfharizky Fauzi; Adang Suhendra; Aries Muslim; Emy Haryatmi
International Journal of Engineering, Science and Information Technology Vol 6, No 4 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i4.1689

Abstract

Over the past few years, matching visual and textual data commonly known as image text matching has become increasingly important due to the rising demand for multimodal data understanding. ITM involves retrieving an image that matches a textual description, or vice versa, by evaluating the semantic similarity between visual and textual inputs. This task becomes particularly critical in medical applications such as skin disease identification, where accurate matching between clinical images and descriptive diagnostic text can aid in early detection and decision-making. One of the core challenges in this domain lies in bridging the semantic gap between visual features of skin lesions and medical terminology used in clinical notes or diagnostic reports. Despite various deep learning approaches developed for general ITM tasks, limited research has focused on domain-specific applications like dermatology. This paper provides a focused review of deep learning-based ITM frameworks, emphasizing their relevance to skin disease analysis. We explore model components such as image and text encoders, common embedding strategies (e.g., global, local, hybrid), and learning paradigms including adversarial, ranking, and pre-trained vision-language models. Additionally, benchmark datasets are discussed, including synthetic and real-world skin image–text pairs. The paper concludes by identifying key challenges such as dataset scarcity, domain adaptation, and explainability highlighting opportunities for advancing ITM in dermatological diagnostics.
Co-Authors Alfharizky Fauzi Aries Muslim Diana Ikasari Emy Haryatmi I Made Wiryana I Made Wiryana Lily Wulandari Lintang Yuniar Banowosari Nuke Farida Romdhoni Susiloatmadja