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Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm Hayatou Oumarou; Rismayanti, Nurul
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.115

The study "Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm" investigates the potential of image processing and machine learning techniques in the classification of empon plants, specifically ginger and turmeric. Utilizing a dataset of leaf images, the research employed the Canny method for image segmentation and Hu Moments for feature extraction, followed by classification using the K-Nearest Neighbors (K-NN) algorithm. The performance of the model was evaluated through a 5-fold cross-validation method, focusing on metrics such as accuracy, precision, recall, and F1-score. The results showcased the model's variable performance, with the highest accuracy reaching 65.33%. The study contributes to the field by demonstrating the application of Hu Moments in plant classification and by assessing the K-NN algorithm's effectiveness in this context. These findings offer insights into the potential of combining image processing techniques with machine learning for accurate plant classification, paving the way for further research in the area.

Predicting Online Gaming Behaviour Using Machine Learning Techniques Rismayanti, Nurul
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.166

Understanding player behaviour in online gaming is essential for enhancing user engagement and retention. This study utilizes a dataset from Kaggle, capturing a wide range of player demographics and in-game metrics to predict player engagement levels categorized as 'High,' 'Medium,' or 'Low.' The dataset includes features such as age, gender, location, game genre, playtime, in-game purchases, game difficulty, session frequency, session duration, player level, and achievements. The research employs a Gaussian Naive Bayes model, with data pre-processing steps including feature selection, categorical data encoding, and scaling of numerical features. The dataset is split into training (80%) and testing (20%) sets, and a 5-fold cross-validation is used to ensure model robustness. The model's performance is evaluated using accuracy, precision, recall, and F1-score. The results show consistent performance across different folds, with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. These findings indicate that the Gaussian Naive Bayes model can reliably predict player engagement levels, identifying significant predictors such as session frequency and in-game purchases. The study contributes to game analytics by providing a predictive model that can help game developers and marketers design more engaging gaming experiences. Future research should incorporate a broader range of features, including psychological and social factors, and explore other machine learning algorithms to enhance predictive accuracy. This study's insights are valuable for developing strategies to improve player retention and satisfaction in the gaming industry.

Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes Rismayanti, Nurul; Naswin, Ahmad; Zaky, Umar; Zakariyah, Muhammad; Purnamasari, Dwi Amalia
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (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.v1i2.99

This study, titled "Evaluating Thresholding-Based Segmentation and HuMoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes," investigates the application of image processing and machine learning techniques in the classification of Acute Lymphoblastic Leukemia (ALL). Utilizing a dataset of microscopic blood smear images, the research focuses on the efficacy of thresholding-based segmentation and Hu moment feature extraction in distinguishing between benign and malignant cases of ALL. Gaussian Naive Bayes, known for its simplicity and effectiveness, is employed as the classification algorithm. The study adopts a 5-fold cross-validation approach to evaluate the model's performance, with particular emphasis on metrics such as accuracy, precision, recall, and F1-score. Results indicate a high precision rate across all folds, averaging approximately 84.13%, while exhibiting variability in accuracy, recall, and F1-scores. These findings suggest that while the model is effective in identifying malignant cases, further refinements are necessary for improving overall accuracy and consistency. This research contributes to the field of medical image analysis by demonstrating the potential of combining simple yet efficient techniques for the automated diagnosis of hepatological diseases. It highlights the importance of integrating image processing with machine learning to enhance diagnostic accuracy in medical applications.

Segmentation and Feature Extraction for Malaria Detection in Blood Smears Rismayanti, Nurul
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 1 (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.v2i1.138

Malaria remains a critical global health challenge, particularly in tropical and subtropical regions. Early and accurate diagnosis is essential for effective treatment and control. Traditional methods of malaria diagnosis, such as microscopic examination of blood smears, are time-consuming and prone to human error. This study aims to develop an automated system for malaria detection using machine learning techniques, specifically a decision tree classifier. The dataset, sourced from the National Institutes of Health (NIH), comprises 27,558 blood smear images equally divided into Normal and Malaria classes. The preprocessing steps included segmentation using the Canny edge detector and feature extraction using Hu Moments, followed by data normalization to ensure a mean of 0 and variance of 1. The decision tree classifier was trained and evaluated using 5-fold cross-validation, yielding an average accuracy of 77.32%, precision of 77.31%, recall of 77.37%, and F1-Score of 77.48%. These results demonstrate the model's robustness and effectiveness in differentiating between malaria-infected and uninfected images. The study confirms the viability of using Hu Moments for feature extraction and highlights the decision tree classifier's suitability for this task. The proposed method has significant implications for automated malaria diagnosis, potentially improving diagnostic accuracy and efficiency in clinical settings. Future research should validate these findings on diverse datasets, explore advanced classification techniques, and integrate real-time image acquisition to enhance practical applicability. The integration of such automated systems in healthcare can revolutionize malaria diagnosis, especially in resource-limited settings.

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

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

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

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
Networking Engineering Research Operation Vol 9, No 1 (2024): Nero - April 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

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

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.

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