Article Per Year (5 Year)
p-Index From 2020 - 2025
1.497
P-Index
This Author published in this journals
All Journal Indonesian Journal of Data and Science NERO (Networking Engineering Research Operation) International Journal of Artificial Intelligence in Medical Issues
Rismayanti, Nurul
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Indonesian Journal of Data and Science

 1 
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

Abstract

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

Abstract

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.
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.
Co-Authors Anik Nur Handayani Azzahrah, Sitti Fatimah Hayatou Oumarou Huzain Azis Naswin, Ahmad Purnamasari, Dwi Amalia Syafie, Lukman Titaley, Gilberth Valentino Umar Zaky