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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Embedded Systems, Security and Intelligent Systems
Muliadi
Universitas Lambung Mangkurat
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification Shalehah; Muhammad Itqan Mazdadi; Andi Farmadi; Dwi Kartini; Muliadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.305

Abstract

Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.
Performance and Training-Time Comparison of Five Pretrained CNN Architectures for South Kalimantan Food Image Classification Ahmad Balya Al Erpat; Dwi Kartini; Fatma Indriani; Andi Farmadi; Muliadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13850

Abstract

Purpose - This study analyzes the classification performance and computational efficiency of five pretrained Convolutional Neural Network (CNN) architectures for identifying South Kalimantan traditional food images as an expanded benchmark. Design/methods/approach - The models were trained on a curated traditional food image dataset using a two-stage transfer learning strategy consisting of linear probing and full fine-tuning with frozen Batch Normalization, supported by a multi-technique data augmentation pipeline. Evaluation was conducted under a fixed stratified data-splitting scenario across repeated runs with different random seeds to assess model stability and reproducibility. Findings - EfficientNetV2B0 achieved the strongest overall performance among the evaluated architectures and provided the most favorable balance between classification accuracy and computational efficiency. Its performance was comparable to other high-performing CNN architectures while requiring substantially lower training time than deeper residual and hybrid networks. The results indicate that greater architectural complexity does not necessarily translate into better recognition performance for a relatively small traditional food image dataset. Research implications/limitations - The findings provide practical guidance for selecting efficient CNN architectures for traditional food recognition. However, the evaluation was conducted on a curated dataset under controlled conditions, and the absence of an ablation study prevents isolating the individual effects of data augmentation and two-stage fine-tuning. Originality/value - This expanded benchmark highlights the critical trade-off between reliable classification performance and computational cost, offering practical guidance for selecting efficient deep learning models to support the digital preservation of culinary heritage.
Co-Authors Ahmad Balya Al Erpat Andi Farmadi Dwi Kartini, Dwi Fatma Indriani Muhammad Itqan Mazdadi Shalehah