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All Journal JOIN (Jurnal Online Informatika) JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Fitri, Maulatus Shaffira
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Computer Science & IT

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Evaluation of the Accuracy and Efficiency of Deep CNN Architecture in Feature Extraction for Guava Disease Classification Wicaqsana, Shiva Augusta; Luthfiarta, Ardytha; Dwi Mareta, Amalia Putri; Fitri, Maulatus Shaffira
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11655

Abstract

This study analyzes and compares several Deep Convolutional Neural Network (DCNN) architectures to evaluate the balance between classification accuracy and computational efficiency in guava fruit disease detection. A hybrid DCNN–Machine Learning (ML) approach was applied to 3,784 images from the Guava Fruit Disease Dataset using a 10-fold cross-validation scheme and undersampling techniques to address data imbalance. Six DCNN architectures were systematically tested, and the combination of ResNet50 with Artificial Neural Network (ANN) showed the best performance with an accuracy of 0.9979 and an F1-score of 0.9975, surpassing the InceptionV3 baseline (0.9974). In addition to being the most accurate, ResNet50 was also 2.5 times faster in feature extraction than DenseNet201, demonstrating an optimal balance between accuracy and time efficiency. These findings emphasize the importance of analyzing the accuracy-efficiency trade-off in selecting a DCNN architecture and open up opportunities for developing more efficient models for future agricultural image classification applications.
Comparative Performance of Fine-Tuned IndoBERT BASE and LARGE Variants for Emotion Detection in Indonesian Tweets Winarno, Sri; Novita Dewi, Ika; Nugraha, Adhitya; Firdausillah, Fahri; Fitri, Maulatus Shaffira; Ramadhani, Talitha Olga; Widhiyanti, Erna Amalia; Rizqi, Ainur Rahma Miftakhul
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1704

Abstract

In the digital era, where emotions play a crucial role in shaping human behavior, communication, and decision-making, their expressions are often conveyed through short and informal texts on platforms such as Twitter. This research aims to improve the accuracy of emotion detection in Indonesian text using the IndoBERT-BASE-P2 and IndoBERT-LARGE-P2 transformer models. The dataset consists of 7,080 tweets annotated with six basic emotion categories (anger, fear, joy, love, neutral, and sad). The research methodology included text preprocessing, class balancing using SMOTE, and fine-tuning with optimized training parameters. Evaluation results show that IndoBERT-BASE-P2 achieved an accuracy of 84.43% and a macro F1-score of 84.33%, surpassing previous studies, while the larger IndoBERT-LARGE-P2 model tended to overfit and offered no meaningful improvement. Error analysis showed the neutral class was the most difficult to classify. These findings demonstrate that with effective preprocessing and parameter optimization, a smaller model can be a highly efficient solution for emotion classification in Indonesian text, especially in resource-constrained conditions.
Co-Authors Adhitya Nugraha Ardytha Luthfiarta Dwi Mareta, Amalia Putri Fahri Firdausillah Ika Novita Dewi Ramadhani, Talitha Olga Rizqi, Ainur Rahma Miftakhul Sri Winarno Wicaqsana, Shiva Augusta Widhiyanti, Erna Amalia