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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal Medical Informatics Technology
Marasabessy, Fahmi B
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Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health

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Optimizing Lung Cancer Prediction Using Evaluating Classification Methods and Sampling Techniques Metalica, Dika Putri; Marasabessy, Fahmi B
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.4

Abstract

Lung cancer is an extremely aggressive type of cancer and one of the leading causes of death globally. The focus of this study is to improve the detection and prediction of lung cancer by evaluating different approaches for classification and sampling. The research utilizes a dataset comprising 1000 patients and 24 Attributes. The primary goal is to compare the effectiveness of classification methods like Logistic Regression, AdaBoost, and GradientBoosting, in conjunction with diverse sampling techniques such as Random Over-Sampling, RandomUnder-Sampling, and SMOTE by Level Considering, for predicting lung cancer. The assessment metrics includeaccuracy, precision, recall, and F1-score. The experimental findings demonstrate that Gradient Boosting (GBoost) attains flawless accuracy, precision, recall, and F1-score results of 100% when identifying lung cancer instances within the dataset. This highlights the effectiveness of GBoost in accurately predicting lung cancer occurrence. The findings of this research aim to contribute significantly to the development of more effective diagnostic and predictive methods for lung cancer. 
Classification of regional language dialects using convolutional neural network and multilayer perceptron Marasabessy, Fahmi B.; Riana, Dwiza; Ernawati, Muji
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5017-5026

Abstract

Regional languages are vital for communication and preserving cultural identity, safeguarding local heritage. However, globalization and modernization endanger their existence as they are increasingly replaced by national or global languages. Despite progress in dialect recognition research, particularly for certain languages, further studies are needed to improve model performance and address less-represented dialects, including those in Indonesia. This study enhances a custom-built dataset for dialect recognition through the application of data augmentation techniques, specifically adding noise, time stretching, and pitch shifting. Using Mel-frequency cepstral coefficients (MFCC) for feature extraction, it evaluates the performance of convolutional neural network (CNN) and multilayer perceptron (MLP) in classifying six Indonesian dialects. Results indicate that CNN outperformed, achieving 97.92% accuracy, 97.90% recall, 97.97% precision, 97.92% F1-score, and a kappa score of 97.49% with combined augmentation techniques, setting a foundation for further research.
Co-Authors Dwiza Riana Ernawati, Muji Metalica, Dika Putri