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All Journal JTT (Jurnal Teknologi Terpadu) ILKOM Jurnal Ilmiah Indonesian Journal of Data and Science Ilmu Komputer untuk Masyarakat NERO (Networking Engineering Research Operation) International Journal of Artificial Intelligence in Medical Issues
Nurul Rismayanti
Universitas Muslim Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Engineering Health Professions Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Other

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Machine Learning Approaches to Gastrointestinal Disease Diagnosis: An Experimental Study with Endoscopic Images Rismayanti, Nurul
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.147

Abstract

Automatic detection of gastrointestinal (GI) diseases using endoscopic images is a critical and emerging field of research with significant implications for healthcare. This study leverages the Kvasir dataset, available on Kaggle, to develop a machine learning model for disease detection and classification. The dataset, consisting of annotated images from the GI tract, was pre-processed using Canny edge detection for segmentation and Hu Moments for feature extraction. The images were divided into training (80%) and testing (20%) sets. A Random Forest Classifier was employed to classify three specific classes: dyed lifted polyps, dyed resection margins, and esophagitis. The performance of the classifier was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results showed moderate performance with accuracy ranging from 39.00% to 44.67%, precision from 40.68% to 47.34%, recall from 39.00% to 44.67%, and F1-scores from 38.09% to 45.07%. These findings indicate that while the Random Forest Classifier demonstrates potential, there is room for improvement in the model and pre-processing techniques. The study contributes to the field by providing a comprehensive evaluation of a machine learning approach for GI disease detection and highlights the need for further research using more advanced models and diverse datasets. Future research should focus on optimizing pre-processing methods, exploring convolutional neural networks, and expanding the dataset to improve classification performance and clinical applicability.
Classification of Skin Diseases using Decision Tree Algorithm on an Imbalanced Dataset Rismayanti, Nurul; Azzahrah, Sitti Fatimah
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v2i2.155

Abstract

Skin infections caused by pathogens such as bacteria and fungi are common and can lead to serious health complications if not properly managed. Accurate classification of these infections is crucial for effective treatment and management. This study focuses on classifying two skin diseases, Chickenpox and Shingles, using a Decision Tree algorithm applied to an imbalanced dataset sourced from Kaggle. The dataset, which is imbalanced by nature, was split into training (80%) and testing (20%) subsets. Pre-processing involved segmentation using Thresholding to isolate regions of interest and feature extraction using Hu Moments to capture shape characteristics of the lesions. The dataset was scaled to ensure that all features had a mean of 0 and variance of 1. The classifier's performance was evaluated using 5-fold cross-validation, yielding a mean accuracy of 66.06%, with precision, recall, and F1-scores indicating moderate performance. The study highlights the challenges posed by imbalanced datasets and the limitations of the Decision Tree algorithm in this context. The results underscore the importance of proper pre-processing and feature extraction but also suggest the need for more advanced classification techniques and data balancing methods. This research contributes to the field by providing a detailed methodology and comprehensive evaluation metrics, offering insights into the application of machine learning for medical image classification. Future work should focus on improving classifier performance through data augmentation, advanced feature extraction, and exploring other machine learning models better suited for imbalanced datasets.
Comparative Analysis of Fuzzy Logic Models for Depression Prediction: Python and LabVIEW Approaches Rismayanti, Nurul; Titaley, Gilberth Valentino; Handayani, Anik Nur
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.189

Abstract

Depression is one of the mental disorders with a significant impact on individuals' quality of life and productivity. The diagnostic process for depression, which typically relies on subjective assessment, often encounters challenges of uncertainty and variability in symptoms. This study aims to develop a fuzzy model for predicting depression levels based on five primary symptom variables: worthlessness, concentration, suicidal ideation, sleep disturbance, and hopelessness. The model is implemented on two platforms, Python and LabVIEW, to evaluate the accuracy and consistency of prediction results between these platforms. The analysis process begins with data preprocessing, input variable fuzzification, inference using 243 fuzzy rules, and defuzzification to generate a crisp output value classified into four depression levels: No Depression, Mild, Moderate, and Severe. The study results indicate a very small error margin between the two platforms, with error values below 0.01 in each trial. These findings suggest that both Python and LabVIEW can produce nearly identical and consistent predictions. This conclusion supports the effectiveness of fuzzy logic in addressing uncertainty in clinical data, especially for cases of depression with varying symptoms. Nonetheless, there are limitations related to the subjectivity in selecting membership functions and rules, as well as limitations in the number of variables used. Therefore, this study recommends expanding the developed fuzzy model with additional variables or integrating it with machine learning approaches to improve prediction accuracy. These findings are expected to serve as a foundation for the development of fuzzy-based systems in future mental health diagnostics.
PREDIKSI ANEMIA DARI PIXEL GAMBAR DAN LEVEL HEMOGLOBIN MENGGUNAKAN RANDOM FOREST CLASSIFIER Azis, Huzain; Rismayanti, Nurul
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.27916

Abstract

Anaemia is a widespread blood disorder characterized by a deficiency of red blood cells or hemoglobin, which can lead to severe health complications if not diagnosed and treated promptly. This research aims to develop a machine learning model to predict anaemia based on hemoglobin levels and image pixel distributions, leveraging a dataset from Kaggle. The dataset includes features such as percentages of red, green, and blue pixels in images and hemoglobin levels. We applied a Random Forest Classifier, a robust machine learning algorithm, and evaluated its performance using 5-fold cross-validation. The data pre-processing involved removing irrelevant columns, encoding categorical variables, and scaling numerical features. The model achieved a mean accuracy of 97.05%, precision of 97.02%, recall of 97.05%, and F1-score of 96.88%, indicating its high reliability in predicting anaemia. Visualizations such as Correlation Heatmaps, 3D PCA, Parallel Coordinates Plots, 3D t-SNE, and Violin Plots were used to understand feature relationships and distributions. These results underscore the potential of machine learning in providing a non-invasive, cost-effective diagnostic tool for anaemia, especially in resource-limited settings. Future research should address dataset imbalance and potential biases, explore additional features, and test other machine learning models to further enhance the predictive accuracy. This study contributes to the field of medical diagnostics by demonstrating the efficacy of integrating hemoglobin levels and image data for anaemia prediction, paving the way for improved early detection and treatment strategies.Keywords: Anaemia, Hemoglobin, Machine Learning, Random Forest.
Automated Diagnosis of Benign Prostatic Hyperplasia Using Deep Learning on RGB Prostate Images Syafie, Lukman; Rismayanti, Nurul
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 1 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i1.251

Abstract

Benign Prostatic Hyperplasia (BPH) is a prevalent non-cancerous enlargement of the prostate gland in aging men, often requiring early diagnosis to prevent urinary complications and improve patient outcomes. Traditional diagnostic procedures are limited by subjectivity and accessibility, especially in under-resourced regions. This study proposes an automated diagnostic approach using a deep learning model based on DenseNet121 to classify RGB prostate images into BPH and normal categories. A region-specific dataset consisting of 176 labeled RGB images, collected from a clinical facility in Bangladesh, was used to train and evaluate the model. Pre-processing included image resizing, normalization, and data augmentation to enhance generalization. Transfer learning was employed to fine-tune the model, which was trained over 10 epochs using the Adam optimizer and cross-entropy loss. The model achieved a best validation accuracy of 94.12%, with a recall of 72.2% for BPH detection, demonstrating its ability to identify pathological patterns in simple imaging modalities. Despite challenges such as dataset size and imbalance, the findings indicate that RGB image-based deep learning models can support clinical diagnosis of BPH in low-resource settings. This work contributes a lightweight, accessible solution for prostate disease screening and provides a foundation for future research on scalable AI-assisted diagnostics.
Performance Comparison of Ensemble Learning Models for Brain Tumor Detection on Augmented MRI Datasets Titaley, Gilberth Valentino; Rismayanti, Nurul; Handayani, Anik Nur; Ardiansah, Jevri Tri
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2523.86-97

Abstract

Brain tumors are highly fatal diseases, making early detection a critical factor in improving patient survival rates. Magnetic Resonance Imaging (MRI) has become a primary tool in brain tumor diagnosis; however, manual analysis processes are often time-consuming and prone to subjective errors. This study employs a machine learning-based classification model to detect four categories of brain tumors—glioma, meningioma, pituitary, and healthy—with high accuracy. The methods include image segmentation using the U-Net model, which excels in medical image analysis due to its encoder-decoder architecture with skip connections, allowing efficient integration of spatial and contextual information. Features are extracted using HuMoments, known for their invariance to rotation, translation, and scale, ensuring robust spatial pattern representation. Data normalization is conducted using Robust Scaling and L2 Normalization to address outliers and harmonize feature scales, enhancing model performance. The MRI dataset, originally comprising 7,023 images, was augmented to 8,000 images using techniques such as rotation, flipping, and contrast adjustments to improve class balance and minimize overfitting. Three ensemble algorithms—Random Forest, XGBoost, and Stacking—were employed to train the models, with performance evaluation based on accuracy, ROC-AUC, F1-score, and confusion matrix. The results demonstrate that Random Forest achieved the best performance with an accuracy of 72% and an ROC-AUC of 0.91. This study illustrates the potential of machine learning approaches for automated brain tumor diagnosis, with further improvement possible through model optimization and the use of more diverse datasets.
Comparative Study of Random Forest and Ordinal Regression in Concept Map Quality Assessment: The Role of TF-IDF, BERT, and SMOTE-based Balancing Rismayanti, Nurul; Prasetya, Didik Dwi; Widiyaningtyas, Triyanna; Hirashima, Tsukasa
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2906.336-345

Abstract

Automatic assessment of concept map quality is an important challenge in the field of education, particularly in evaluating students' conceptual understanding objectively and efficiently. This study aims to compare the performance of two machine learning algorithms, namely Random Forest and Ordinal Regression, in classifying the quality of concept maps. The evaluation was conducted on three approaches to text feature representation: Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and a combination of both (TF-IDF + BERT). Additionally, this study compares the performance of the models under two dataset conditions: original data and data balanced using the Synthetic Minority Over-sampling Technique (SMOTE), to address the class imbalance that often occurs in educational data. The data used consists of a collection of propositions from students' concept maps that have been labeled with ordinal scores based on quality. Text representation is extracted using the TF-IDF and BERT approaches, and then used as input to build the classification model. Performance evaluation was conducted using the metrics of Accuracy, Precision, Recall, F1-score, Cohen’s Kappa, and MAE. The results show that the Ordinal Regression model with TF-IDF representation combined with SMOTE achieved the best performance, with an accuracy of 0.8777, an F1-score of 0.8773, and a Cohen’s Kappa of 0.7701. These results indicate that classical feature representations like TF-IDF remain effective in limited data scenarios, and that the SMOTE technique successfully improved the model's performance by reducing bias towards the majority class. This research contributes to the development of an automatic concept map assessment system and suggests optimal classification strategies for educational datasets with ordinal and imbalanced characteristics
Co-Authors Admojo, Fadhila Tangguh Anik Nur Handayani Aulia Putri Utami Azzahrah, Sitti Fatimah Didik Dwi Prasetya Dwi Nur Halizah Fery Setyo Aji Hayatou Oumarou Hirashima, Tsukasa Huzain Azis Irawati Irawati Jevri Tri Ardiansah lilis nurhayati Muhammad Alim Abdi Muhammad Arif Naswin, Ahmad Nurfadillah Said Nurlinda Nurlinda Purnamasari, Dwi Amalia Risti Amelia Syafie, Lukman Titaley, Gilberth Valentino Triyanna Widiyaningtyas Umar Zaky Umniah Umniah Wibowo, Nanang Roni