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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika
Meindiawan, Eka Putra Agus
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Computer Science & IT Education

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Application of MobileNetV2 and SVM Combination for Enhanced Accuracy in Pneumonia Classification Meindiawan, Eka Putra Agus; Muljono, Muljono
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8426

Abstract

Pneumonia is a very common respiratory infection in low- and middle-income countries and is still a leading cause of death, especially among children under five years old. Modern technologies, such as machine learning, offer significant potential in improving the automatic detection of pneumonia through chest X-ray (CXR) image analysis. This study aims to develop a more accurate pneumonia diagnosis system by evaluating various feature extraction methods. CXR datasets of pneumonia patients were resized to 180x180 pixels and balanced using the SMOTE-Tomek technique. Three main approaches were investigated: direct classification using Support Vector Machine (SVM) on the SMOTE-Tomek balanced dataset, feature extraction using Sobel edge detection followed by SVM classification, and feature extraction using MobileNet-V2 followed by SVM classification. The results showed that Scheme 1 achieved 97% accuracy, Scheme 2 decreased to 95%, and Scheme 3 achieved the highest accuracy at 98%. The lower accuracy in Scheme 2 is due to the limitations of Sobel edge detection, which reduces the key features in the CXR image. On the other hand, the improvement in Scheme 3 is due to the effective feature extraction capability of MobileNet-V2. In conclusion, the choice of feature extraction method plays an important role in determining the accuracy of an automated diagnostic system. This study builds on existing research and is expected to make a significant contribution to the development of more accurate and efficient automated diagnostic systems, which can ultimately help reduce pneumonia-related mortality.
Comparative Analysis of Homogeneous and Heterogeneous Ensembles for Diabetes Classification Optimization Maulana, Muhammad Naufal; Muljono, Muljono; Meindiawan, Eka Putra Agus
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14439

Abstract

Diabetes mellitus is a chronic disease with an increasing prevalence worldwide, including in Indonesia, reaching 11.7% by 2023. Early prediction of this disease is essential for more effective management. This study aims to develop a diabetes mellitus prediction model using an ensemble learning approach, including homogeneous (boosting and bagging) and heterogeneous (stacking and blending) techniques. In this study, the boosting algorithm using AdaBoost with Random Forest as the base estimator showed the highest accuracy of 98%, with balanced precision and recall. The bagging technique, which also uses Random Forest as the base estimator, achieved 97% accuracy, although slightly lower than boosting. The stacking technique, which combines XGBoost, Gradient Boosting, and Random Forest as base learners, with Random Forest as the meta-model, yields similar accuracy of 98%, but with lower prediction error, demonstrating its ability to cope with more complex data. Blending, which uses a similar approach but with training on the entire dataset, gave 98% accuracy with shorter processing time and more efficient memory usage than stacking.
On the Effectiveness of Lightweight CNN Architectures for Fine-Grained Coffee Bean Classification Burhanudhin, Akbar Muhamad; Sudibyo, Usman; Meindiawan, Eka Putra Agus
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34044

Abstract

Distinguishing coffee bean varieties remains a significant challenge in the agricultural industry due to high inter-class similarity and the subtle morphological differences between species. This study aims to conduct a comparative evaluation of MobileNetV2 and EfficientNetB0 for fine-grained coffee bean classification, specifically investigating how efficiency-oriented architectural mechanisms such as depthwise separable convolution and compound scaling influence feature extraction. The research employed a quantitative experimental method using a private dataset of 2,400 images comprising Arabica, Robusta, and Liberica varieties. Data preprocessing included resizing to 224×224 pixels and augmentation, followed by training the two architectures using transfer learning under a controlled experimental framework. The results showed that EfficientNetB0 achieved superior performance with a testing accuracy of 99.17%, while MobileNetV2 attained a competitive accuracy of 98.33% with lower computational complexity. These results demonstrate that while EfficientNetB0 is optimal for high-precision industrial sorting, MobileNetV2 offers a highly efficient alternative for resource-constrained mobile applications. This study provides a scalable framework for automating quality control, effectively balancing architectural efficiency with the sensitivity required for accurate coffee variety identification.
Co-Authors Burhanudhin, Akbar Muhamad Maulana, Muhammad Naufal Muljono Muljono Sudibyo, Usman