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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Internet of Things and Artificial Intelligence Journal
Arindika, Alti
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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PCOS Disease Classification Using XGBoost Algorithm and Genetic Algorithm for Feature Selection Atika, Enda Putri; Nadzirullah, Muh. Ilham; Arindika, Alti
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 1 (2025): Volume 5 Issue 1, 2025 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i1.874

Abstract

Polycystic Ovary Syndrome (PCOS) is an endocrine disorder that often occurs in women of reproductive age, with a global prevalence of 10-16%. The diagnosis of PCOS is still a challenge due to the uncertainty of the cause, which can worsen the patient's condition due to delayed detection. This study aims to develop a classification model to detect PCOS using a combination of SMOTE algorithm, genetic algorithm, and XGBoost. The dataset used is a public dataset from Kaggle entitled "Diet, Exercise, and PCOS Insights". A genetic algorithm was used to select the best 15 features, while SMOTE was applied to handle data imbalances. XGBoost is used for classification with a model accuracy of 82.86% and an F1-score of 88% for the PCOS negative class and 70% for the PCOS positive class. The results show that combining these algorithms can improve the accuracy of predictions and offer more efficient diagnosis solutions. This research is expected to contribute to developing early diagnosis methods for PCOS.
Comparison of Naïve Bayes and Support Vector Machine for Sentiment Classification of Acne Skincare Reviews Arindika, Alti; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11869

Abstract

The increasing popularity of skincare products for acne-prone skin had led to a surge in online consumer reviews, which are characterized by informal language, domain-specific terminology, and imbalanced sentiment distribution, posing challenges for sentiment classification tasks. This study aims not only to compare the performance but also to analyze the generalization behavior of two popular machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM), for sentiment classification of skincare product reviews specifically targeting acne-prone skin. A comprehensive methodology was employed, including thorough text preprocessing, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) with n-gram representation, and data balancing through Synthetic Minority Over-sampling Technique (SMOTE). The study utilized a dataset of 4,004 labeled reviews categorized into positive and negative sentiments. The models were evaluated using stratified 5-Fold cross-validation to ensure robust and fair assessment. Results indicate that Naïve Bayes slightly outperforms SVM on the testing set, achieving the highest accuracy of 91.14% compared to 90.64% for SVM. While SVM demonstrated higher performance during training, its testing performance suggested a tendency toward overfitting, whereas Naïve Bayes exhibited more stable generalization on unseen data. Further qualitative insight analysis revealed that product effectiveness and user experience are the primary drivers of consumer sentiment, while competitive analysis highlighted distinct brand perception patterns across skincare categories. These findings indicate that simpler probabilistic models such as Naïve Bayes can provide robust and reliable performance for sentiment analysis in specialized and imbalanced skincare review datasets.
Co-Authors Atika, Enda Putri Majid Rahardi Nadzirullah, Muh. Ilham