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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Rizalde, Alika Rahmarsyarah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data Rizalde, Alika Rahmarsyarah; Mubarak, Haykal Alya; Ramadhan, Gilang; Fatan, Mohd. Adzka
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.1106

Abstract

The hallmarks of Obsessive-Compulsive Disorder (OCD) are intrusive, anxiety-inducing thoughts (called obsessions) and associated repeated activities (called compulsions). To understand the patterns and relationships between OCD data that have been obtained, data will be grouped (clustering). In clustering using several clustering algorithms, namely K-Means, BIRCH, In this work, hierarchical clustering was used to identify the optimal cluster value comparison, and the Davies Bouldin Index (DBI) was used to confirm the results. Then the results of the best cluster value in processing OCD data are using the BIRCH algorithm in the K10 experiment which gets a value of 1.3. While the K-Means algorithm obtained the best cluster at K10 with a value obtained of 1.36 and the Hierarchical clustering algorithm also at the K10 value of 2.03. Thus in this study, the comparison results of the application of 3 clustering algorithms obtained results, namely the BIRCH algorithm shows the value of the resulting cluster is the best in clustering OCD data. This means that the BIRCH algorithm can be used to cluster OCD data more accurately and efficiently.
A Deep Learning Approach Bassed on Classification to Detect Facial Skin Defect Rizalde, Alika Rahmarsyarah; Mubarak, Haykal Alya; Khairunnisa, Batrisia; Fatan, Mohd. Adzka
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1558

Abstract

As people are more active, facial skin is often neglected, which can lead to acne, eye bags, and redness. In this study, deep learning models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Generative Adversarial Networks (GANs) are used to classify facial skin damage. DenseNet201 and MobileNetV2 architectures were also used to evaluate the models in this study. The dataset used consists of facial skin disease photos collected from the Kaggle database. The model was trained and tested to classify the types of skin damage after going through data collection and preprocessing stages. The results showed that the GANs model and the DenseNet201 and MobileNetV2 architectures were the best models, with test accuracy values of 89% for the GANs model, 88% for the DenseNet201 architecture, and 89% for the MobileNetV2 architecture. These results show that deep learning approach techniques can help classify and find facial skin problems well. and it is expected that it will be a great progress in the field of dermatology and skin health.
Co-Authors Fatan, Mohd. Adzka Gilang Ramadhan Khairunnisa, Batrisia Mubarak, Haykal Alya