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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Sugiyarto Surono
Universitas Ahmad Dahlan, Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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Distance Functions Study in Fuzzy C-Means Core and Reduct Clustering Joko Eliyanto; Sugiyarto Surono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 1 (2021): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i1.20516

Abstract

Fuzzy C-Means is a distance-based clustering process which applied by fuzzy logic concept. Clustering process worked in linear to the iteration process to minimizing the objective function. The objective function is an addition of the multiplication between the coordinates distance towards their closest cluster centroid and their membership degree. The more the iteration process, the objective function should get lower and lower. The objective of this research is to observe whether the distances which usually applied are able to fulfill the aforementioned hypothesis for determining the most suitable distance for Fuzzy C-Means clustering application. Few distance function was applied in the same dataset. 5 standard datasets and 2 random datasets were used to test the fuzzy c-means clustering performance with the 7 different distance function. Accuracy, purity, and Rand Index also applied to measure the quality of the resulted cluster. The observation result depicted that the distance function which resulted in the best quality of clusters are Euclidean, Average, Manhattan, Minkowski, Minkowski-Chebisev, and Canberra distance. These 6 distances were able to fulfill the basic hypothesis of the objective function behavior on Fuzzy C-Means Clustering method. The only distance who were not able to fulfill the basic hypothesis is Chebisev distance.
Comparative Evaluation of Feature Selection Methods for Heart Disease Classification with Support Vector Machine Winarsi J. Bidul; Sugiyarto Surono; Tri Basuki Kurniawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28647

Abstract

The purpose of this study is to compare the effectiveness of a variety of feature selection techniques to enhance the performance of Support Vector Machine (SVM) models for classifying heart disease data, particularly in the context of big data. The main challenge lies in managing large datasets, which necessitates the application of feature selection techniques to streamline the analysis process. Therefore, several feature selection methods, including Logistic Regression-Recursive Feature Elimination (LR-RFE), Logistic RegressionSequential Forward Selection (LR-SFS), Correlation-based Feature Selection (CFS), and Variance Threshold were explored to identify the most efficient approach. Based on existing research, these methods have shown a great impact in improving classification accuracy. In this study, it was found that combining the SVM model with LR-RFE, LR-SFS, and Variance Threshold resulted in superior evaluation, achieving the highest accuracy of 89%. Based on the comparison of other evaluation results, including precision, recall, and F1-score, the performance of these models varied depending on the feature selection method chosen and the distribution of data used for training and testing. But in general, LR-RFE-SVM and Variance Threshold-SVM tend to provide better evaluation values than LR-SFS-SVM and SVM-CFS. Based on the computation time, SVM classification with the Variance Threshold method as the feature selection method obtained the fastest time of 118.1540 seconds with the number and retention of 23 important features. Therefore, it is very important to choose a suitable feature selection technique, taking into account the number of retained features and the computation time. This research underscores the significance of feature selection in addressing big data challenges, particularly in heart disease classification. In addition, this study also highlights practical implications for healthcare practitioners and researchers by recommending methods that can be integrated into real-world healthcare settings or existing clinical decision support systems.
Machine Learning-Based Early Breast Cancer Detection Through Temperature and Color Skin with Non-Invasive Smart Device Sona Regina Salsabila; Sugiyarto Surono; Irsyadul Ibad; Eko Prasetyo; Arsyad Cahya Subrata; Aris Thobirin
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30340

Abstract

Breast cancer remains a significant global health issue, affecting millions of women and often leading to late-stage diagnoses. Traditional diagnostic methods, such as mammograms, ultrasounds, and biopsies, are effective but can be costly, invasive, and not widely accessible, causing delays in detection and treatment.  This research highlights the potential of using machine learning models with physiological data for early breast cancer detection. By capturing subtle physiological variations from a smart bra, the device allows real-time, non-invasive monitoring, offering a preventive solution that reduces the need for frequent clinical visits. The data were collected from a modified mannequin designed to simulate conditions related to breast cancer. To classify cancerous conditions based on temperature and color data, three machine learning models were evaluated.  The Random Forest (RF) model proved to be the most effective, achieving 89% accuracy, 86.11% precision, 88.57% recall, and an F1-score of 87.33%, demonstrating strong performance in identifying complex patterns. The Support Vector Machine (SVM) achieved an accuracy of 81.25%, precision of 85.7%, recall of 80%, and an F1-score of 82.64%. The Multilayer Perceptron (MLP) exhibited an accuracy of 72%, precision of 69.69%, recall of 65.71%, and an F1-score of 67.52%, suggesting potential but requiring further optimization.  These models serve as valuable tools to assist medical professionals in early screening efforts. Future research should aim to improve the models’ generalizability by expanding the dataset, utilizing data augmentation, applying transfer learning, and incorporating additional variables. Clinical validation and human trials are essential next steps to evaluate the system's effectiveness.
Co-Authors Aris Thobirin Arsyad Cahya Subrata Eko Prasetyo Irsyadul Ibad Joko Eliyanto Sona Regina Salsabila Tri Basuki Kurniawan Winarsi J. Bidul