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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi JOIV : International Journal on Informatics Visualization
Darmawan, Hendri
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Computer Science & IT Dentistry Education Library & Information Science

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Deep Metric Learning with Different Distance Metrics for Enhanced Classification Model in Typing Style Darmawan, Hendri; Zulfa Muflihah; Tita Karlita
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1292

Abstract

Writing can be a powerful and unique medium of self-expression for every individual. Therefore, we propound a deep metric learning technique to acquire the vector representation of text, aiming to enhance the performance of deep learning classification models in typing style classification. The study also compared the effect of text pre-processing and distance metrics on model performance using tweet data from six different Twitter users. The outcomes of the study showed that the model without text pre-processing and with deep metric learning using the Cosine distance metric had the optimal result with an accuracy of 0.79, compared to the deep learning model with a categorical cross-entropy loss function which only had an accuracy of 0.76. Additionally, the model with text pre-processing also produced a good performance, with an accuracy of 0.63 using the deep metric learning approach and Cosine distance metric, and an accuracy of 0.64 using deep learning classification with a categorical cross-entropy loss function.
Deep Metric Learning with Augmented Latent Fusion and Response-Based Knowledge Distillation on Edge Device for Paddy Pests and Disease Identification Darmawan, Hendri; Yuliana, Mike; Hadi, Mochammad Zen Samsono; Sangaiah, Arun Kumar
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3104

Abstract

The health of paddy fields significantly impacts rice yields and the economic stability of farmers. Limited number of experts available to watch these issues poses a challenge. Consequently, a reliable diagnostic system is necessary to find pests and diseases in rice crops. In this study, we propose deep metric learning with augmented latent fusion (FADMAKA) combined with a response-based knowledge distillation (KD) approach. The student model, which processes single RGB input images, is trained using soft latent labels derived from four augmented input from the teacher model. Our method delivers a high validation accuracy of 0.973, keeps an accuracy of 0.782 on the unseen data, and with rapid inference time of 38.911 milliseconds. This approach’s accuracy outperforms SoftMax deep learning classification with fine-tuning, which only has a maximum accuracy of 0.739 on the unseen data with computation time of 36.224 ms, and the DML with augmented latent fusion with k-NN classifier on the same base model, which achieves an accuracy of 0.78 with computation time of 124.977 ms. Our proposed model has 0.12 giga floating point operations per second (GFLOPs) that is suitable for edge devices with low computational resources. Following the modeling phase, we deployed the highest-accuracy student model to a Raspberry Pi 4B device equipped with a camera. This system can provide biological agent-based recommendations for identified pest and disease threats in rice fields. Our approach not only improved accuracy but also proved efficiency, enabling farmers to identify pests and disease without relying on internet connectivity. 
Co-Authors Hadi, Mochammad Zen Samsono Mike Yuliana Sangaiah, Arun Kumar Tita Karlita Zulfa Muflihah