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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSAI (Journal Scientific and Applied Informatics) JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan)
Vina Ayumi
Universitas Dian Nusantara
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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Penerapan Metode Gamma Correction dan MobileNet Untuk Klasifikasi Citra Daun Mariana Purba; Vina Ayumi; Sarwati Rahayu; Umniy Salamah; Inge Handriani; Nur Ani
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9459

Abstract

This study proposed an enhanced leaf image classification model by integrating gamma correction as a preprocessing technique with the MobileNet (MNET) architecture to improve visual feature extraction. The dataset consisted of 750 images representing five classes of medicinal plants, namely Psidium guajava, Syzygium polyanthum, Piper betle, Annona muricata, and Andrographis paniculata, obtained from personal documentation, online sources, and public datasets. Gamma correction was applied to adjust illumination and enhance leaf texture clarity, followed by resizing and normalization processes. Data augmentation was performed using rotation, contrast adjustment, horizontal and vertical flipping, brightness adjustment, and channel shifting to increase training data variation. The MobileNet architecture was expanded with additional layers, including global average pooling, flatten, Dense–ReLU, and Dense–softmax, enabling it to function as an efficient feature extractor and classifier. Experiments were conducted using a batch size of 32, 50 epochs, the Adam optimizer, and a learning rate of 0.0001. The combined MNET and gamma correction model achieved a training accuracy of 99.00%, a validation accuracy of 87.50%, and a testing accuracy of 84.16%.
Efek Hyperparameter Tuning pada VGG16 Untuk Klasifikasi Citra Batik Basurek Bengkulu Vina Ayumi
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9778

Abstract

This study aimed to analyze the effect of combining hyperparameters, namely optimizer and batch size, on the performance of the VGG16 model in classifying Batik Basurek images. The dataset consisted of 250 images divided into five motif classes, with 50 images in each class. The data were split into training, validation, and testing sets with proportions of 70%, 15%, and 15%, respectively. The study employed a transfer learning approach using the VGG16 model, with hyperparameter variations including the RMSProp, Adam, and SGD optimizers, as well as batch sizes of 16, 32, and 64. The results showed that the Adam optimizer consistently delivered the best accuracy performance across all testing scenarios. The optimal performance was achieved using the combination of Adam and a batch size of 32, yielding a training accuracy of 97.55%, validation accuracy of 93.25%, and testing accuracy of 92.80%. Meanwhile, RMSProp demonstrated reasonably good performance but remained below Adam, and SGD produced the lowest accuracy across all evaluation stages. In terms of batch size, a batch size of 32 provided the most stable and accurate performance, whereas a batch size of 64 tended to reduce the model’s generalization capability. Therefore, the combination of Adam and a batch size of 32 was identified as the most optimal hyperparameter configuration for Batik Basurek image classification using the VGG16 model.
Klasifikasi Citra Aksara Tradisional Kaganga Bengkulu Menggunakan Optimasi Arsitektur ResNet50 Vina Ayumi
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 3 (2025): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i3.9780

Abstract

This study aimed to analyze the performance of the ResNet50 model based on transfer learning in classifying 19 classes of Kaganga script, while also evaluating the effect of applying L1, L2, and dropout regularization techniques on the model’s generalization ability in minimizing overfitting. In addition, the study examined the impact of varying batch sizes (16, 32, and 64) on training stability and overall model performance. The experiments were conducted by freezing the initial layers of ResNet50 as a feature extractor and modifying the final layers for the classification task. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics on the test dataset. The results showed that all model configurations achieved high training and validation accuracy. However, the combination of L2 regularization with a batch size of 32 yielded the best performance with a testing accuracy of 86.10%, indicating the most optimal generalization capability compared to other configurations. Meanwhile, the use of batch size 64 resulted in a more noticeable decrease in accuracy, making it less effective for this dataset. These findings indicated that the appropriate selection of regularization techniques and batch size played an important role in improving training stability and classification accuracy for traditional script image recognition.
Klasifikasi Sampah Multi-Kelas Berbasis Deep Learning Menggunakan Model VGG16 Vina Ayumi
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9880

Abstract

The manual waste sorting process has faced various challenges, such as low efficiency and a high potential for classification errors. This study aimed to implement and analyze the performance of a deep learning–based VGG16 model for multi-class waste classification using digital images. The dataset used consisted of six waste classes, namely cardboard, glass, metal, paper, plastic, and residual waste, with an imbalanced initial number of images. To address this issue, data augmentation was performed so that each class contained 500 images. The dataset was then divided into 70% training data, 15% validation data, and 15% testing data. The experiments were conducted using a transfer learning approach by varying training parameters, including the RMSProp, Adam, and Stochastic Gradient Descent (SGD) optimizers, as well as batch sizes of 16, 32, and 64. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the selection of training parameters significantly affected model performance. The best configuration was achieved using the VGG16 model with the Adam optimizer and a batch size of 16, which produced the highest testing accuracy of 85.87%. This study was expected to serve as a foundation for the development of automated computer vision–based waste sorting systems
Penerapan Optimasi Convolutional Neural Network untuk Klasifikasi Multi-Kelas Tumor Otak pada Citra MRI Vina Ayumi
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9986

Abstract

Brain tumors are among the most critical neurological diseases and require early and accurate diagnosis to support appropriate medical treatment. Magnetic Resonance Imaging (MRI) is widely used for brain tumor detection due to its high-resolution imaging capability; however, manual analysis of MRI images is time-consuming and highly dependent on the expertise of radiologists. Therefore, this study aims to apply an optimized Convolutional Neural Network (CNN) for multi-class brain tumor classification using MRI images. The dataset used in this study consists of 7,023 MRI images, categorized into four classes: glioma, meningioma, pituitary, and healthy, and divided into training, validation, and testing subsets. The research stages include image preprocessing, CNN architecture design, hyperparameter optimization, model training for 50 epochs, and performance evaluation. The training process achieved an accuracy of 87.44%, while the validation accuracy reached 85%, indicating good model generalization. Model evaluation on the test dataset using a confusion matrix, precision, recall, F1-score, and accuracy resulted in an overall accuracy of 77.8%.
Co-Authors Abd Rahman Anita Ratnasari Ida Farida Inge Handriani Inge Handriani Mariana Purba Mariana Purba Mariana Purba Noprisson, Handrie Nur Ani Nur Ani Nur Ani Sarwati Rahayu Sarwati Rahayu Siska Mailana Siska Mailana Umniy Salamah Uswatun Hasanah Wachyu Hari Haji