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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Electrical Engineering and Computer Science
Fatma Susilawati Mohamad
Universiti Sultan Zainal Abidin
Computer Science & IT Engineering

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Automated handwriting analysis based on pattern recognition: a survey Samsuryadi Samsuryadi; Rudi Kurniawan; Fatma Susilawati Mohamad
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp196-206

Abstract

Handwriting analysis has wide scopes include recruitment, medical diagnosis, forensic, psychology, and human-computer interaction. Computerized handwriting analysis makes it easy to recognize human personality and can help graphologists to understand and identify it. The features of handwriting use as input to classify a person’s personality traits. This paper discusses a pattern recognition point of view, in which different stages are described. The stages of study are data collection and pre-processing technique, feature extraction with associated personality characteristics, and the classification model. Therefore, the purpose of this paper is to present a review of the methods and their achievements used in various stages of a pattern recognition system. 
Two-stage Transfer Learning for Chilli Leaf Disease Classification using CNN and Vision Transformer Rudi Kurniawan; Samsuryadi Samsuryadi; Fatma Susilawati Mohamad; Bunga Intan; Nelly Khairani Daulay
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026 (In progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7674

Abstract

Chili cultivation is considerably affected by leaf diseases, making timely and reliable disease identification essential for maintaining agricultural productivity. While deep learning has demonstrated strong capabilities in plant disease recognition, many existing studies primarily focus on modifying network architectures rather than optimizing the training process. This study introduces a two-stage transfer learning framework that combines frozen-backbone adaptation with controlled fine-tuning to enhance classification performance. The experiments utilized a publicly available dataset comprising 12,000 augmented chili leaf images representing six disease categories. Several deep learning architectures were investigated, including DenseNet121, EfficientNetB0, MobileNetV2, ResNet152V2, and the Vision Transformer (ViT-B16). The experimental results indicate that the proposed two-stage training strategy improves classification performance consistently across the evaluated architectures. Among the models, ViT-B16 obtained the highest test accuracy of 99.61%, together with stable macro-averaged precision, recall, and F1-score values. Furthermore, the confusion matrix and ROC analysis revealed strong discrimination among the disease classes, with only limited errors occurring between visually similar categories. These findings highlight the importance of optimizing the training schedule when adapting pretrained models for plant disease recognition. Overall, the proposed framework provides an effective and scalable approach for chili leaf disease classification while offering an alternative to approaches that primarily rely on architectural modifications.
Co-Authors Bunga Intan Nelly Khairani Daulay Rudi Kurniawan Rudi Kurniawan Samsuryadi Samsuryadi