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All Journal International Journal of Engineering, Science and Information Technology
Alfharizky Fauzi
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 Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Herbal Plant Leaves Classification Using Convolutional Neural Network Models: A Literature Review Alfharizky Fauzi; Emy Haryatmi; Tri Agus Riyadi; Murniyati Murniyati
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.723

Abstract

Plants are essential to human beings because plants are considered most as foods. Plants can be used for food ingredients, medical purposes, and industrial applications. People inspect plants using traditional methods, such as using the naked eye, which can be time-consuming and expensive. Therefore, the effectiveness and high quality of automated crop identification classification systems are needed for adequate crop protection. This study aims to identify and classify nine plant species using different datasets, focusing on transfer learning from models trained on plant leaf datasets. Most research has shown that increasing the dataset size would significantly improve classification accuracy. The accuracy of the first test using the modified N1 classification model was 99.45%. In the second experiment, the accuracy of the N2 model was 99.65%. The accuracy of the N3 model, despite being slightly less accurate than AlexNet, was 99.55%, and it performed better, while the accuracy of AlexNet was 99.73%. Compared to the AlexNet model, the proposed model performed better and required less training time. The N1 model reduced the training time by 34.58%, the N2 model by 18.25%, and the N3 model by 20.23%. The N1 and N3 resulted in the same size, namely 14.8MB, and the compactness was 92.67%. The size of the N2 model was 29.7MB, and the compactness was 85.29% compactness. The proposed models provide more accuracy and efficiency in classifying plant leaves and can be used as a standalone mobile application that benefits farmers.
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 Adang Suhendra Aries Muslim Emy Haryatmi Murniyati Murniyati Riyadi, Tri Agus