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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Bulletin of Computer Science Research
Ri Sabti Septarini
Universitas Muhammadiyah Tangerang, Tangerang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Classification of Medicinal Wild Plant Leaf Types Using a Combination of ELM and PCA Algorithms Dedy Alamsyah; Farli Rossi; Ri Sabti Septarini; Mohammad Imam Shalahudin
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6817

Abstract

Despite their detrimental nature, it turns out that wild plants have many benefits for human health. Wild plants with a form of herbaceous vegetation contain ingredients that can be used as medicine, especially in their leaves. However, because the information is very similar and the form is similar, people don't know about it. For this reason, the aim of this research is to implement an artificial neural network algorithm using Extreme Learning Machine (ELM) and the Principal Component Analysis (PCA) algorithm to classify images of wild plant leaves with medicinal properties, especially in herbaceous vegetation. The feature extraction used in this research involves morphological features by considering the shape of the object. The PCA algorithm will reduce data complexity and identify hidden patterns in the data by changing the original feature space to a new and more concise feature space. Next, the ELM algorithm is used to recognize class grouping patterns when solving classification problems. Accuracy test results show a value of 90.667%.
Pendekatan Hybrid K-Means SMOTE dan Logistic Regression Untuk Deteksi Dini Diabetes Mellitus Pada Imbalanced Data Abdus Salam; Lukman Azhari; Ri Sabti Septarini; Nofitri Heriyani
Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i3.502

Abstract

The increasing global prevalence of Diabetes Mellitus necessitates more accurate early detection efforts, particularly through machine learning-based approaches. However, one of the main challenges in medical classification lies in data imbalance, where the number of diabetic cases is significantly lower than that of non-diabetic ones. This study aims to develop a hybrid model by integrating Logistic Regression and K-Means SMOTE to enhance the sensitivity of early detection for Diabetes Mellitus, especially toward the minority class. Logistic Regression is chosen for its computational efficiency and interpretability, while K-Means SMOTE plays a role in balancing class distribution by generating synthetic samples in a structured manner based on clusters of minority class data. The dataset used consists of 2,000 records with 9 health-related features, obtained from the Kaggle platform. Evaluation results indicate that the model utilizing K-Means SMOTE achieves the best performance, with an accuracy of 82.00%, an F1-score of 72.73% for the Diabetes class, and the highest ROC-AUC score of 87.48%. Compared to models without oversampling and with standard SMOTE, this approach improves model generalization and sensitivity to positive cases. These findings have practical implications for the development of fairer and more effective machine learning-based early detection systems, particularly for implementation in healthcare facilities with limited resources.
Co-Authors Abdus Salam Dedy Alamsyah Farli Rossi Lukman Azhari Mohammad Imam Shalahudin Nofitri Heriyani