Meat Image Classification Using Deep Learning With Resnet152v2 Architecture

Hidayat, Taopik; Saputri, Daniati Uki Eka; Aziz, Faruq

doi:10.33480/techno.v19i2.3932

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The image augmentation process is a technique for increasing the number of image data variations available in the training dataset so that the image becomes larger than the original number [14].…”

Section: Augmentationmentioning

confidence: 99%

Enhancing Skin Cancer Classification Using Optimized InceptionV3 Model

Daniati Uki Eka Saputri,

Nurul Khasanah,

Aziz

et al. 2023

MEDINFTech

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Skin cancer is a disease that starts in skin cells characterized by uncontrolled growth that can attack skin tissue. Although it has a high cure rate if treated in a timely manner, a delay in diagnosis can have serious consequences. The use of computer technology, especially Artificial Intelligence (AI), has played an important role in improving health services, including in the context of skin cancer. New innovations in the classification and detection of skin cancer using artificial neural networks have led to significant improvements in diagnosis and treatment. One promising approach is using the InceptionV3 algorithm, which has high accuracy and is capable of processing high-resolution images. This study aims to implement InceptionV3 to classify two types of skin cancer, namely malignant and benign, with an emphasis on improving accuracy performance. With the pre-processing process, namely augmentation and the addition of several features, this study aims to provide accurate and efficient results in skin cancer classification. The results of this study can have a positive impact in increasing the accuracy of early detection of skin cancer, especially by future researchers.

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Section: Augmentationmentioning

confidence: 99%

Enhancing Skin Cancer Classification Using Optimized InceptionV3 Model

Daniati Uki Eka Saputri,

Nurul Khasanah,

Aziz

et al. 2023

MEDINFTech

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“…The dataset's missing values were resolved by implementing an appropriate imputation method. Furthermore, the dataset was divided into a validation set, which consisted of 20% of the data, and a training set, which encompassed 80% of the data [15]. This division was implemented to aid in the process of model construction and evaluation.…”

Section: Preprocessing Datamentioning

confidence: 99%

Comparison Algorithm on Machine Learning for Student Mental Health Data

Nuarini,

Siti Fauziah,

Mayangky

et al. 2023

MEDINFTech

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The COVID-19 pandemic has posed unparalleled difficulties, encompassing substantial repercussions on the emotional well-being of students. This study utilises machine learning methodologies to forecast the mental health condition of students during and following the pandemic. The dataset consists of 11 distinct attributes and a total of 101 data points, which have been gathered from multiple sources. The preprocessing stage encompasses the removal of unnecessary characteristics, handling missing data, and partitioning the dataset into separate subsets for training and validation purposes. This study utilises three machine learning algorithms, namely RF, KNN, and NB, in order to make predictions regarding the potential need for psychiatric support among students. These algorithms are carefully optimised to enhance their predictive capabilities. Evaluation metrics commonly used in several fields of study. The findings suggest that the KNN and RF algorithms had outstanding performance, but the Naïve Bayes algorithm exhibited satisfactory accuracy and a balanced trade-off between precision and recall. The optimised models have practical consequences that may be applied at educational institutions and inform policymakers. These implications include the ability to provide tailored interventions and support services specifically designed for students who are facing mental health difficulties as a result of the epidemic. Future research endeavours encompass the need for additional improvement of existing models and the fostering of interdisciplinary collaboration. This study provides significant contributions to the field by examining the utilisation of machine learning techniques in addressing the mental health needs of students both during and after the epidemic.

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