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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,046 Documents
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Enhanced RegNetY-400MF for Fruit Fly Species Classification: Fine-Tuning Strategies and Data Balancing for Improved Accuracy Rahman, Sayuti; Indrawati, Asmah; Zen, Muhammad; Zealtiel, Billiam; Tanjung, Shabila Shaharani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6973

Abstract

Fruit fly infestations pose a significant threat to agricultural productivity, especially in chili plantations, which can cause substantial yield losses. Accurate and rapid species classification is crucial for implementing targeted pest control strategies. This study developed a computationally efficient fruit fly species classification model using a deep learning approach that focused on improving accuracy with fine tuning and class balancing strategies. The dataset consists of 1049 images across 4 fruit fly species, captured in a natural plantation environment and available at www.inaturalist.org. The model evaluated several lightweight Convolutional Neural Network (CNN) architectures, including MobileNetV3-Small, RegNetY-400MF, and SqueezeNet among others, with RegNetY-400MF emerging as the best performing model, achieving a validation accuracy of 96.10% and a macro F1 score of 95.70%. The models tested in this study included several lightweight Convolutional Neural Network (CNN) architectures, including MobileNetV3-Small, RegNetY-400MF, and SqueezeNet, among others. RegNetY-400MF proved to be the best performing model, achieving a validation accuracy of 96.10% and a macro F1 score of 95.70%. Compared to other state-of-the-art models, RegNetY-400MF demonstrated higher accuracy while maintaining a lower number of parameters (8.3 million) and reduced computational complexity (0.41 GFLOPs). This makes the model highly suitable for real-time applications in resource-constrained agricultural environments. The model offers a practical solution for fruit fly species detection, enabling early and accurate identification of pest infestations in chili plantations, thereby reducing the risk of crop failure. By providing an efficient and scalable pest control tool, the model supports precision pest management, improves yield stability, and contributes to sustainable agriculture.
Optimizing Transformer Model FlanT5 for Multi-Question Answering with Context-Aware Learning Rate Suryanto, Tri Lathif Mardi; Wibawa, Aji Prasetya; Hariyono, Hariyono; Nafalski, Andrew
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6985

Abstract

This study investigates the performance of FlanT5-based transformer models in handling Multiple-Question Answering (M-QA) tasks, in which multiple semantically related questions must be addressed with a single cohesive answer. Unlike traditional QA systems that focus on one-to-one question-answer pairs, the M-QA approach challenges the model to understand contextual relationships across several questions tied to the same topic. A custom dataset was developed with shared context, grouped questions, and a unified answer to train and evaluate the model. The FlanT5 architecture was fine-tuned using different learning rates (0.0001, 0.0002, 0.0003) to explore the effect of training configurations on model performance. The evaluation was conducted using the ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum metrics. The results indicate that a learning rate of 0.0003 provides the optimal performance, achieving a ROUGE-Lsum score of 0.7390. This study confirms the capability of instruction-tuned transformers to manage complex summarization scenarios that require contextual coherence. The findings are relevant for real-world applications such as intelligent digital assistants, clinical decision support, and educational chatbots. Furthermore, this study emphasizes the importance of hyperparameter tuning in improving the effectiveness of question-driven summarization systems for scalable and efficient deployment.
Comparative Analysis of ResNet-Based Wagner-Scale Classification for Imbalanced DFU Data Ramadhan, Aditya Wahyu; Pulung Nurtantio Andono; M. Arief Soeleman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7016

Abstract

Diabetic Foot Ulcers (DFU) are a serious complication of diabetes mellitus and carry a high risk of lower extremity amputation if not treated in a timely manner. The conventional classification process, which relies on visual inspection by clinicians, tends to be subjective and inconsistent. Therefore, this study proposes a multiclass classification model for DFU based on the Wagner Scale (Grades 0–5) using the ResNet-50 architecture with a transfer learning approach as the core machine learning method. The dataset used in this study consists of 1,415 clinical wound images that were annotated and verified by medical professionals. The dataset is highly imbalanced, with 543 images in Grade 0, 110 in Grade 1, 252 in Grade 2, 145 in Grade 3, 293 in Grade 4, and only 72 images in Grade 5. To address this imbalance, random oversampling (ROS) was applied, in addition to standard preprocessing techniques such as normalization and data augmentation to increase training data diversity.Experimental results demonstrate that the proposed model achieves high classification performance based on accuracy, precision, recall, and F1-score. Specifically, the model obtained a precision of 0.96, recall of 0.95, and F1-score of 0.95, indicating consistent and robust classification performance across all Wagner grades. The best configuration (ResNet-50 + ROS) successfully improved the classification performance across minority grades (e.g., Grade 1 and Grade 5). Moreover, the model consistently identifies minority classes and does not exhibit signs of overfitting. Model optimization using the Adam optimizer and data balancing strategies significantly improves the generalization capability of the classifier. These findings indicate that the proposed model is not only effective for automatic DFU classification, but also has great potential to support objective clinical decision making and accelerate diagnosis, particularly in healthcare facilities with limited resources.
RadReader: An Enhanced AlexNet-Based GUI Application for Pneumonia Prediction in Thoracic X-Ray Images Wiriasto, Giri Wahyu; Hipzi, Ahdiat Aunul; Suksmadana, I Made Budi; Misbahuddin; Kinasih, Indira puteri; Wiguna, Putu Aditya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7023

Abstract

Recent advancements in radiology applications have led to user-friendly interfaces, improving pneumonia diagnosis by accurately differentiating between viral and bacterial pneumonia from thoracic X-rays. This approach enhances diagnostic precision and efficiency while offering intuitive real-time interaction for radiologists. This study aims to achieve two objectives: (i) developing a desktop-based radiology reader application, and (ii) modifying the alexNet architecture for classifying pneumonia based on thoracic X-ray datasets with the output encompassing pneumonia and normal cases. The desktop application assists radiologists in efficient image analysis and is developed using python–Tkinter. Integrate enhanced of AlexNet models which has been modified to better differentiate. The modified alexNet includes changes like adding max pooling in specific blocks and adjusting hidden layer neuron count. The dataset consists of 7442 images, with 4484 positive pneumonia and 2958 normal images obtained from the Mendeley websites. The enhanced alexNet (EAM) model achieves impressive results: 95.36% accuracy, 95.34% precision, 95.28% recall, and 95.31% F1-score for classifying bacterial pneumonia.
Hybrid Video Transcription Summarization with a BERT-Based Clustering and BART Darmawan, Fathul Agit; Mauludin, Muhammad Bima; Aditya, Christian Sri Kusuma
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7066

Abstract

The use of video as a medium for information and education is rapidly increasing across online platforms. However, long durations and unstructured delivery often hinder audiences from grasping the core message, presenting challenges for the development of automatic summarization methods for monologues, interviews, and podcasts. Extractive methods often yield less coherent summaries, while abstractive methods may overlook important details. To address this issue, this study proposes a hybrid approach combining extractive and abstractive techniques. In the extractive stage, sentences are represented using BERT embeddings and clustered using two methods, namely K-Means Clustering and Hierarchical Clustering (agglomerative). The abstractive stage then employs the BART model to generate summaries that are more coherent and informative. Experimental evaluations on 20 Human Metapneumovirus (HMPV) videos indicate the strongest performance on monologues, with ROUGE-1 of 57%, ROUGE-2 of 30%, and ROUGE-L of 32%. Although lower performance was observed for interviews and podcasts due to dynamic interactions and frequent speaker shifts, the hybrid approach consistently surpassed extractive-only and abstractive-only baselines. These results highlight the effectiveness of the hybrid approach and its potential for developing more adaptive video summarization in the future.
Enhancing Face Authentication for Online Examination Systems Using Median Filtering and MobileNetV2 Dadang Sudrajat; Dian Ade Kurnia; Kurniawan, Rudi; Othman bin Mohd; Maulana Sujarwadi; Salman Alfarizi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7185

Abstract

Digital transformation in higher education is driving the uptake of online tests, which require academic integrity, security, and robust user experience. In the context of authentication of users, deep learning based face recognition, in particular the Convolutional Neural Network (CNN) architectures, such as MobileNetV2, combined with intermediate filter, promises to deliver a consistent performance across a wide range of devices and imaging environments. However, there are limited comprehensive studies evaluating the final integration of the median filter and MobileNetV2 in high-value test scenarios. This study contributes by proposing an effective end-to-end Face Authentication Pipeline, assessing the median impact of filtering on MobileNetV2 performance, and validating it with a prototype application. The authentic face dataset was collected using the Teachable Machine, preprocessed with cropping, resizing, and median filtering, and then augmented through rotation, shift, shear, zoom, reversal, and brightness adjustment. The MobileNetV2 model was trained with Adam in a stepwise manner, starting with 0.001 and then 0.0001 for 20 epochs in a batch size of 32, and was evaluated for accuracy, precision, recall, and F1 score. Results show that the accuracy curve has remained stable at almost 95 percent during the 20th epoch; most grades achieved 1.00 in both precis, recall and F1, with some classings showing a limited decrease due to facial similarity or expression differences. These findings confirm that MobileNetV2 median filtering can be the basis for an effective, accurate and ready to integrate face recognition in online testing applications on a wide range of devices.

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