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All Journal Media Statistika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Berkala Epidemiologi JUITA : Jurnal Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Berdaya Mandiri Computer Science and Information Technologies International Journal of Engagement and Empowerment (IJE2) Bulletin of Applied Mathematics and Mathematics Education
Sugiyarto Surono, Sugiyarto
Department of Mathematics, Faculty of Mathematics and Science Ahmad Dahlan University, Yogyakarta
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Journal : JUITA : Jurnal Informatika

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Handling Noise Data with PCA Method and Optimization Using Hybrid Fuzzy C-Means and Genetic Algorithm Widianti, Risa; Surono, Sugiyarto; Ibraheem, Kais Ismail
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21765

Abstract

The significance of machine learning (ML) and data mining techniques particularly clustering is examined in this research, in managing large data sets for customer segmentation in the retail sector. The research emphasizes the challenges posed by data noise and proposes a solution using Principal Component Analysis (PCA) to improve accuracy. This study introduces a hybrid approach that combines Fuzzy C-Means (FCM) with genetic algorithms for optimization in customer segmentation, and suggests further research on the optimal number of clusters and data noise elimination. By addressing data noise, the proposed PCA-based method achieved a higher accuracy rate of 98% compared to 93% without PCA. This finding underscores the effectiveness of PCA in noise reduction, improving clustering accuracy. This research contributes to the advancement of customer-focused business strategies through better data analysis and interpretation. The proposed approach has potential applications in areas including data analysis, pattern recognition, and image processing, highlighting its relevance in the contemporary business environment.
Performance Analysis of Resampling Techniques for Overcoming Data Imbalance in Multiclass Classification Larasati, Anggit; Surono, Sugiyarto; Thobirin, Aris; Dewi, Deshinta Arrova
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.25270

Abstract

In the digital era, the development of modern technology has brought significant transformation to the medical world. The main objective of this research is to identify the performance of deep learning models in classifying kidney disease. By integrating the Convolutional Neural Network model, the performance of the classification process can be analyzed effectively and efficiently. However, data imbalance dramatically affects the performance evaluation of a model, requiring data resampling techniques. This research applies two resampling techniques, bootstrap-based random oversampling and random undersampling, to training data and adds data augmentation to increase image variations to prevent model overfitting. The architecture uses MobileNetV2, which compares hyperparameter fine-tuning in three optimizers. This research shows that the performance of MobileNetV2, which implements the bootstrap-based random oversampling technique, has the highest accuracy compared to random undersampling and no resampling methods. The oversampling technique with the RMSprop optimizer produced the highest accuracy, namely 95%. With precision, recall, and F-1 score, respectively, 0.93, 0.95, 0.94. The accuracy of oversampling with the Adam and Nadam optimizer is 94%. So, the contribution of this research is by applying bootstrap-based oversampling techniques and adding data augmentation to produce good model performance to be used to classify medical images.
Co-Authors Afriyani, Sintia Ahmad, Fandy Ambar Lukitaningsih Aris Thobirin, Aris Azizan, Ahamad Tarmizi Bilqisa, Nazla H. Dewi, Deshinta Arrova Dina Arumsari, Mela Eko Prasetyo Endang Darmawan Fahrian Hipmi, Ahmad Hariadi Wijaya, Made Haryati, Anisa Eka Haryati, Annisa Eka Ibad., Irsyadul Ibraheem, Kais Ismail Irsalinda, Nursyiva Khang Wen, Goh khusnul khotimah Kusnul Khotimah Laely, Dera Kurnia Larasati, Anggit Lianti, Desi Maulidya, Thasya Alwa Muhamad Safiih Lola Nur Rahmatiya Abas, Siti Pangestu, Raka Puguh Wahyu Prasetyo, Puguh Rismawati, Dewi Saidon, Hasnul Jamal Salsabila, Sona Regina Siti Mariah, Siti Solihin, Iwan Mahmud Subrata, Arsyad Cahya Susanto, Moh. Rusnoto Sya'idah, Irma Binti Umi Mahmudah Wardani, Rahmania Ayu Wen, Goh Kang Widianti, Risa Wijayanti, Dian Eka Wiwit, Ilma Woro Supadmi Yudi Ari Adi, Yudi Ari