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All Journal Internet of Things and Artificial Intelligence Journal
Atika, Enda Putri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Performance Analysis of Genetic Algorithms and KNN Using Several Different Datasets Riwanto, Yudha; Atika, Enda Putri
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.767

Abstract

This research aims to increase the accuracy of the classification of mango, corn, and potato leaf types using an approach involving feature selection with a genetic algorithm (Genetic Algorithm), classification with K-Nearest Neighbors (KNN), and image processing in the HSV color space (Hue, Saturation). , Value). The dataset used consists of more than 1500 image samples for each type of leaf, with a total of 10 tests carried out. The research process begins with processing leaf images in HSV color space to extract more representative color information. Next, a genetic algorithm is applied to select the most relevant features from the processed image. The selected features are then used as input for the KNN model in the classification process. The test results show that the proposed method can achieve a classification accuracy of 87,9%. This shows that the combination of image processing in the HSV color space, feature selection using a genetic algorithm, and classification with KNN can improve performance in recognizing leaf types. This research makes significant contributions to the field of image processing and classification and shows the potential of using genetic algorithms for feature selection in pattern recognition applications.
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.
Co-Authors Arindika, Alti Nadzirullah, Muh. Ilham Riwanto, Yudha